Cancer classification in the genomic era: five contemporary problems.

Science.gov (United States)

Song, Qingxuan; Merajver, Sofia D; Li, Jun Z

2015-10-19

Classification is an everyday instinct as well as a full-fledged scientific discipline. Throughout the history of medicine, disease classification is central to how we develop knowledge, make diagnosis, and assign treatment. Here, we discuss the classification of cancer and the process of categorizing cancer subtypes based on their observed clinical and biological features. Traditionally, cancer nomenclature is primarily based on organ location, e.g., "lung cancer" designates a tumor originating in lung structures. Within each organ-specific major type, finer subgroups can be defined based on patient age, cell type, histological grades, and sometimes molecular markers, e.g., hormonal receptor status in breast cancer or microsatellite instability in colorectal cancer. In the past 15+ years, high-throughput technologies have generated rich new data regarding somatic variations in DNA, RNA, protein, or epigenomic features for many cancers. These data, collected for increasingly large tumor cohorts, have provided not only new insights into the biological diversity of human cancers but also exciting opportunities to discover previously unrecognized cancer subtypes. Meanwhile, the unprecedented volume and complexity of these data pose significant challenges for biostatisticians, cancer biologists, and clinicians alike. Here, we review five related issues that represent contemporary problems in cancer taxonomy and interpretation. (1) How many cancer subtypes are there? (2) How can we evaluate the robustness of a new classification system? (3) How are classification systems affected by intratumor heterogeneity and tumor evolution? (4) How should we interpret cancer subtypes? (5) Can multiple classification systems co-exist? While related issues have existed for a long time, we will focus on those aspects that have been magnified by the recent influx of complex multi-omics data. Exploration of these problems is essential for data-driven refinement of cancer classification

Reliability of McConnell's classification of patellar orientation in symptomatic and asymptomatic subjects.

Science.gov (United States)

Watson, C J; Propps, M; Galt, W; Redding, A; Dobbs, D

1999-07-01

Test-retest reliability study with blinded testers. To determine the intratester reliability of the McConnell classification system and to determine whether the intertester reliability of this system would be improved by one-on-one training of the testers, increasing the variability and numbers of subjects, blinding the testers to the absence or presence of patellofemoral pain syndrome, and adhering to the McConnell classification system as it is taught in the "McConnell Patellofemoral Treatment Plan" continuing education course. The McConnell classification system is currently used by physical therapy clinicians to quantify static patellar orientation. The measurements generated from this system purportedly guide the therapist in the application of patellofemoral tape and in assessment of the efficacy of treatment interventions on changing patellar orientation. Fifty-six subjects (age range, 21-65 years) provided a total of 101 knees for assessment. Seventy-six knees did not produce symptoms. A researcher who did not participate in the measuring process determined that 17 subjects had patellofemoral pain syndrome in 25 knees. Two testers concurrently measured static patellar orientation (anterior/posterior and medial/lateral tilt, medial/lateral glide, and patellar rotation) on subjects, using the McConnell classification system. Repeat measures were performed 3-7 days later. A kappa (kappa) statistic was used to assess the degree of agreement within each tester and between testers. The kappa coefficients for intratester reliability varied from -0.06 to 0.35. Intertester reliability ranged from -0.03 to 0.19. The McConnell classification system, in its current form, does not appear to be very reliable. Intratester reliability ranged from poor to fair, and intertester reliability was poor to slight. This system should not be used as a measurement tool or as a basis for treatment decisions.

The reliability of AO classification for distal radius fracture, using CT findings

International Nuclear Information System (INIS)

Nakanishi, Yasuaki; Ono, Hiroshi; Furuta, Kazuhiko; Fujitani, Ryoutarou; Ota, Hiroyoshi

2006-01-01

The purpose of this study was to assess the reliability of the AO (Association for the Study of Internal Fixation) classification of distal radius fracture, using plain radiographs and 2 cross-sectional computed tomographic (CT) surface images. Five observers independently classified 32 distal radius fractures into 9 groups under AO classification. We established 4 methods for observation. First, using only two-directional radiographs; second, four-directional radiographs; third, CT (axial view) with four-directional radiographs; and fourth, CT (axial and sagittal views) with four-directional radiographs. Kappa statistics were used to establish the relative level of agreement between the observers. Interobserver reliability was poor in both first and second methods in which only plain radiographs were used (κ=0.30 and 0.23, respectively). Furthermore, reliability did not increase in the third method with the addition of 1 CT surface image (κ=0.29). In the fourth method, with the addition of 2 cross-sectional CT surface images, the reliability increased to a moderate level (κ=0.44). Interobserver reliability of the AO system of the classification of distal radius fractures was observed on using 2 cross-sectional CT surface images with four-directional radiographs. (author)

Intra- and interrater reliability of three different MRI grading and classification systems after acute hamstring injuries.

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Wangensteen, Arnlaug; Tol, Johannes L; Roemer, Frank W; Bahr, Roald; Dijkstra, H Paul; Crema, Michel D; Farooq, Abdulaziz; Guermazi, Ali

2017-04-01

To assess and compare the intra- and interrater reliability of three different MRI grading and classification systems after acute hamstring injury. Male athletes (n=40) with clinical diagnosis of acute hamstring injury and MRI ≤5days were selected from a prospective cohort. Two radiologists independently evaluated the MRIs using standardised scoring form including the modified Peetrons grading system, the Chan acute muscle strain injury classification and the British Athletics Muscle Injury Classification. Intra-and interrater reliability was assessed with linear weighted kappa (κ) or unweighted Cohen's κ and percentage agreement was calculated. We observed 'substantial' to 'almost perfect' intra- (κ range 0.65-1.00) and interrater reliability (κ range 0.77-1.00) with percentage agreement 83-100% and 88-100%, respectively, for severity gradings, overall anatomical sites and overall classifications for the three MRI systems. We observed substantial variability (κ range -0.05 to 1.00) for subcategories within the Chan classification and the British Athletics Muscle Injury Classification, however, the prevalence of positive scorings was low for some subcategories. The modified Peetrons grading system, overall Chan classification and overall British Athletics Muscle Injury Classification demonstrated 'substantial' to 'almost perfect' intra- and interrater reliability when scored by experienced radiologists. The intra- and interrater reliability for the anatomical subcategories within the classifications remains unclear. Copyright © 2017 Elsevier B.V. All rights reserved.

Reliability testing of two classification systems for osteoarthritis and post-traumatic arthritis of the elbow.

Science.gov (United States)

Amini, Michael H; Sykes, Joshua B; Olson, Stephen T; Smith, Richard A; Mauck, Benjamin M; Azar, Frederick M; Throckmorton, Thomas W

2015-03-01

The severity of elbow arthritis is one of many factors that surgeons must evaluate when considering treatment options for a given patient. Elbow surgeons have historically used the Broberg and Morrey (BM) and Hastings and Rettig (HR) classification systems to radiographically stage the severity of post-traumatic arthritis (PTA) and primary osteoarthritis (OA). We proposed to compare the intraobserver and interobserver reliability between systems for patients with either PTA or OA. The radiographs of 45 patients were evaluated at least 2 weeks apart by 6 evaluators of different levels of training. Intraobserver and interobserver reliability were calculated by Spearman correlation coefficients with 95% confidence intervals. Agreement was considered almost perfect for coefficients >0.80 and substantial for coefficients of 0.61 to 0.80. In patients with both PTA and OA, intraobserver reliability and interobserver reliability were substantial, with no difference between classification systems. There were no significant differences in intraobserver or interobserver reliability between attending physicians and trainees for either classification system (all P > .10). The presence of fracture implants did not affect reliability in the BM system but did substantially worsen reliability in the HR system (intraobserver P = .04 and interobserver P = .001). The BM and HR classifications both showed substantial intraobserver and interobserver reliability for PTA and OA. Training level differences did not affect reliability for either system. Both trainees and fellowship-trained surgeons may easily and reliably apply each classification system to the evaluation of primary elbow OA and PTA, although the HR system was less reliable in the presence of fracture implants. Copyright © 2015 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

Quantum Cascade Laser-Based Infrared Microscopy for Label-Free and Automated Cancer Classification in Tissue Sections.

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Kuepper, Claus; Kallenbach-Thieltges, Angela; Juette, Hendrik; Tannapfel, Andrea; Großerueschkamp, Frederik; Gerwert, Klaus

2018-05-16

A feasibility study using a quantum cascade laser-based infrared microscope for the rapid and label-free classification of colorectal cancer tissues is presented. Infrared imaging is a reliable, robust, automated, and operator-independent tissue classification method that has been used for differential classification of tissue thin sections identifying tumorous regions. However, long acquisition time by the so far used FT-IR-based microscopes hampered the clinical translation of this technique. Here, the used quantum cascade laser-based microscope provides now infrared images for precise tissue classification within few minutes. We analyzed 110 patients with UICC-Stage II and III colorectal cancer, showing 96% sensitivity and 100% specificity of this label-free method as compared to histopathology, the gold standard in routine clinical diagnostics. The main hurdle for the clinical translation of IR-Imaging is overcome now by the short acquisition time for high quality diagnostic images, which is in the same time range as frozen sections by pathologists.

Intra- and interobserver reliability of the Eaton classification for trapeziometacarpal arthritis: a systematic review.

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Berger, Aaron J; Momeni, Arash; Ladd, Amy L

2014-04-01

Trapeziometacarpal, or thumb carpometacarpal (CMC), arthritis is a common problem with a variety of treatment options. Although widely used, the Eaton radiographic staging system for CMC arthritis is of questionable clinical utility, as disease severity does not predictably correlate with symptoms or treatment recommendations. A possible reason for this is that the classification itself may not be reliable, but the literature on this has not, to our knowledge, been systematically reviewed. We therefore performed a systematic review to determine the intra- and interobserver reliability of the Eaton staging system. We systematically reviewed English-language studies published between 1973 and 2013 to assess the degree of intra- and interobserver reliability of the Eaton classification for determining the stage of trapeziometacarpal joint arthritis and pantrapezial arthritis based on plain radiographic imaging. Search engines included: PubMed, Scopus(®), and CINAHL. Four studies, which included a total of 163 patients, met our inclusion criteria and were evaluated. The level of evidence of the studies included in this analysis was determined using the Oxford Centre for Evidence Based Medicine Levels of Evidence Classification by two independent observers. A limited number of studies have been performed to assess intra- and interobserver reliability of the Eaton classification system. The four studies included were determined to be Level 3b. These studies collectively indicate that the Eaton classification demonstrates poor to fair interobserver reliability (kappa values: 0.11-0.56) and fair to moderate intraobserver reliability (kappa values: 0.54-0.657). Review of the literature demonstrates that radiographs assist in the assessment of CMC joint disease, but there is not a reliable system for classification of disease severity. Currently, diagnosis and treatment of thumb CMC arthritis are based on the surgeon's qualitative assessment combining history, physical

X-ray diagnosis of esophageal cancer and application of Borrmann's classification

International Nuclear Information System (INIS)

Chin, Soo Yil

1985-01-01

In 126 cases and who were diagnosed as esophageal cancer and treated by radiation at Cancer Research Hospital, K. A. E. R. I., from January 1974 to July 1979, a study on the x-ray diagnosis of esophageal cancer was carried out mainly as to the type classification. The ordinary classification od esophageal cancer by x-ray picture was reviewed and Borrmann's classification using gastric cancer was tried to apply to the macroscopic classification of esophageal cancer, and also the application of this classification to x-ray diagnosis was discussed. And the effect of radiotherapy as to each type of cancer according to the ordinary x-ray classification and Borrmann's classification was studied too. The results were as follows: 1. The ordinary x-ray classification was not simple, because the degree of progression of cancer, difference of mural invasion, and position and method of radiography could make misinterpretation of the type of cancer and the therapeutic effect by radiation as to each type according to this classification did not represent a significant characteristic too, although the radiation was most effective in the polypoidal type and least effective in funnel type. 2. The Borrmann's classification was relatively easy even on the radiogram because of little overlapping between each type and the type became more evident on the resected specimen after operation. And also some correlation was recognized between the type of Borrmann's classification and radiotherapeutic effect. The effect was best in type I and It was gradually decreased in type II, III, and IV in the other. The radiotherapy was ineffective in about three quarters of type IV. 3. The Borrmann's classification is now employed to the carcinoma of large bowel, as well as to the gastric cancer. If it is applied to the esophageal cancer, the macroscopic classification for the cancer of digestive tract can be systemized and it will be convenient in clinical study.

A Classification Framework Applied to Cancer Gene Expression Profiles

Directory of Open Access Journals (Sweden)

Hussein Hijazi

2013-01-01

Full Text Available Classification of cancer based on gene expression has provided insight into possible treatment strategies. Thus, developing machine learning methods that can successfully distinguish among cancer subtypes or normal versus cancer samples is important. This work discusses supervised learning techniques that have been employed to classify cancers. Furthermore, a two-step feature selection method based on an attribute estimation method (e.g., ReliefF and a genetic algorithm was employed to find a set of genes that can best differentiate between cancer subtypes or normal versus cancer samples. The application of different classification methods (e.g., decision tree, k-nearest neighbor, support vector machine (SVM, bagging, and random forest on 5 cancer datasets shows that no classification method universally outperforms all the others. However, k-nearest neighbor and linear SVM generally improve the classification performance over other classifiers. Finally, incorporating diverse types of genomic data (e.g., protein-protein interaction data and gene expression increase the prediction accuracy as compared to using gene expression alone.

The reliability and validity of the Saliba Postural Classification System.

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Collins, Cristiana Kahl; Johnson, Vicky Saliba; Godwin, Ellen M; Pappas, Evangelos

2016-07-01

To determine the reliability and validity of the Saliba Postural Classification System (SPCS). Two physical therapists classified pictures of 100 volunteer participants standing in their habitual posture for inter and intra-tester reliability. For validity, 54 participants stood on a force plate in a habitual and a corrected posture, while a vertical force was applied through the shoulders until the clinician felt a postural give. Data were extracted at the time the give was felt and at a time in the corrected posture that matched the peak vertical ground reaction force (VGRF) in the habitual posture. Inter-tester reliability demonstrated 75% agreement with a Kappa = 0.64 (95% CI = 0.524-0.756, SE = 0.059). Intra-tester reliability demonstrated 87% agreement with a Kappa = 0.8, (95% CI = 0.702-0.898, SE = 0.05) and 80% agreement with a Kappa = 0.706, (95% CI = 0.594-0818, SE = 0.057). The examiner applied a significantly higher (p < 0.001) peak vertical force in the corrected posture prior to a postural give when compared to the habitual posture. Within the corrected posture, the %VGRF was higher when the test was ongoing vs. when a postural give was felt (p < 0.001). The %VGRF was not different between the two postures when comparing the peaks (p = 0.214). The SPCS has substantial agreement for inter- and intra-tester reliability and is largely a valid postural classification system as determined by the larger vertical forces in the corrected postures. Further studies on the correlation between the SPCS and diagnostic classifications are indicated.

Interobserver and intraobserver reliability of two classification systems for intra-articular calcaneal fractures.

Science.gov (United States)

Lauder, Anthony J; Inda, David J; Bott, Aaron M; Clare, Michael P; Fitzgibbons, Timothy C; Mormino, Matthew A

2006-04-01

For a fracture classification to be useful it must provide prognostic significance, interobserver reliability, and intraobserver reproducibility. Most studies have found reliability and reproducibility to be poor for fracture classification schemes. The purpose of this study was to evaluate the interobserver and intraobserver reliability of the Sanders and Crosby-Fitzgibbons classification systems, two commonly used methods for classifying intra-articular calcaneal fractures. Twenty-five CT scans of intra-articular calcaneal fractures occurring at one trauma center were reviewed. The CT images were presented to eight observers (two orthopaedic surgery chief residents, two foot and ankle fellows, two fellowship-trained orthopaedic trauma surgeons, and two fellowship-trained foot and ankle surgeons) on two separate occasions 8 weeks apart. On each viewing, observers were asked to classify the fractures according to both the Sanders and Crosby-Fitzgibbons systems. Interobserver reliability and intraobserver reproducibility were assessed with computer-generated kappa statistics (SAS software; SAS Institute Inc., Cary, North Carolina). Total unanimity (eight of eight observers assigned the same fracture classification) was achieved only 24% (six of 25) of the time with the Sanders system and 36% (nine of 25) of the time with the Crosby-Fitzgibbons scheme. Interobserver reliability for the Sanders classification method reached a moderate (kappa = 0.48, 0.50) level of agreement, when the subclasses were included. The agreement level increased but remained in the moderate (kappa = 0.55, 0.55) range when the subclasses were excluded. Interobserver agreement reached a substantial (kappa = 0.63, 0.63) level with the Crosby-Fitzgibbons system. Intraobserver reproducibility was better for both schemes. The Sanders system with subclasses included reached moderate (kappa = 0.57) agreement, while ignoring the subclasses brought agreement into the substantial (kappa = 0.77) range

Inter- and intraobserver reliability of the MTM-classification for proximal humeral fractures

DEFF Research Database (Denmark)

Bahrs, Christian; Schmal, Hagen; Lingenfelter, Erich

2008-01-01

tool. METHODS: Three observers classified plain radiographs of 22 fractures using both a simple version (fracture displacement, number of parts) and an extensive version (individual topographic fracture type and morphology) of the MTM classification. Kappa-statistics were used to determine reliability....... RESULTS: An acceptable reliability was found for the simple version classifying fracture displacement and fractured main parts. Fair interobserver agreement was found for the extensive version with individual topographic fracture type and morphology. CONCLUSION: Although the MTM-classification covers...

Molecular Classification and Correlates in Colorectal Cancer

OpenAIRE

Ogino, Shuji; Goel, Ajay

2008-01-01

Molecular classification of colorectal cancer is evolving. As our understanding of colorectal carcinogenesis improves, we are incorporating new knowledge into the classification system. In particular, global genomic status [microsatellite instability (MSI) status and chromosomal instability (CIN) status] and epigenomic status [CpG island methylator phenotype (CIMP) status] play a significant role in determining clinical, pathological and biological characteristics of colorectal cancer. In thi...

Lauren classification and individualized chemotherapy in gastric cancer

OpenAIRE

MA, JUNLI; SHEN, HONG; KAPESA, LINDA; ZENG, SHAN

2016-01-01

Gastric cancer is one of the most common malignancies worldwide. During the last 50 years, the histological classification of gastric carcinoma has been largely based on Lauren's criteria, in which gastric cancer is classified into two major histological subtypes, namely intestinal type and diffuse type adenocarcinoma. This classification was introduced in 1965, and remains currently widely accepted and employed, since it constitutes a simple and robust classification approach. The two histol...

Reliability assessment of AOSpine thoracolumbar spine injury classification system and Thoracolumbar Injury Classification and Severity Score (TLICS) for thoracolumbar spine injuries: results of a multicentre study.

Science.gov (United States)

Kaul, Rahul; Chhabra, Harvinder Singh; Vaccaro, Alexander R; Abel, Rainer; Tuli, Sagun; Shetty, Ajoy Prasad; Das, Kali Dutta; Mohapatra, Bibhudendu; Nanda, Ankur; Sangondimath, Gururaj M; Bansal, Murari Lal; Patel, Nishit

2017-05-01

The aim of this multicentre study was to determine whether the recently introduced AOSpine Classification and Injury Severity System has better interrater and intrarater reliability than the already existing Thoracolumbar Injury Classification and Severity Score (TLICS) for thoracolumbar spine injuries. Clinical and radiological data of 50 consecutive patients admitted at a single centre with a diagnosis of an acute traumatic thoracolumbar spine injury were distributed to eleven attending spine surgeons from six different institutions in the form of PowerPoint presentation, who classified them according to both classifications. After time span of 6 weeks, cases were randomly rearranged and sent again to same surgeons for re-classification. Interobserver and intraobserver reliability for each component of TLICS and new AOSpine classification were evaluated using Fleiss Kappa coefficient (k value) and Spearman rank order correlation. Moderate interrater and intrarater reliability was seen for grading fracture type and integrity of posterior ligamentous complex (Fracture type: k = 0.43 ± 0.01 and 0.59 ± 0.16, respectively, PLC: k = 0.47 ± 0.01 and 0.55 ± 0.15, respectively), and fair to moderate reliability (k = 0.29 ± 0.01 interobserver and 0.44+/0.10 intraobserver, respectively) for total score according to TLICS. Moderate interrater (k = 0.59 ± 0.01) and substantial intrarater reliability (k = 0.68 ± 0.13) was seen for grading fracture type regardless of subtype according to AOSpine classification. Near perfect interrater and intrarater agreement was seen concerning neurological status for both the classification systems. Recently proposed AOSpine classification has better reliability for identifying fracture morphology than the existing TLICS. Additional studies are clearly necessary concerning the application of these classification systems across multiple physicians at different level of training and trauma centers to evaluate not

Perceptual and Acoustic Reliability Estimates for the Speech Disorders Classification System (SDCS)

Science.gov (United States)

Shriberg, Lawrence D.; Fourakis, Marios; Hall, Sheryl D.; Karlsson, Heather B.; Lohmeier, Heather L.; McSweeny, Jane L.; Potter, Nancy L.; Scheer-Cohen, Alison R.; Strand, Edythe A.; Tilkens, Christie M.; Wilson, David L.

2010-01-01

A companion paper describes three extensions to a classification system for paediatric speech sound disorders termed the Speech Disorders Classification System (SDCS). The SDCS uses perceptual and acoustic data reduction methods to obtain information on a speaker's speech, prosody, and voice. The present paper provides reliability estimates for…

NIM: A Node Influence Based Method for Cancer Classification

Directory of Open Access Journals (Sweden)

Yiwen Wang

2014-01-01

Full Text Available The classification of different cancer types owns great significance in the medical field. However, the great majority of existing cancer classification methods are clinical-based and have relatively weak diagnostic ability. With the rapid development of gene expression technology, it is able to classify different kinds of cancers using DNA microarray. Our main idea is to confront the problem of cancer classification using gene expression data from a graph-based view. Based on a new node influence model we proposed, this paper presents a novel high accuracy method for cancer classification, which is composed of four parts: the first is to calculate the similarity matrix of all samples, the second is to compute the node influence of training samples, the third is to obtain the similarity between every test sample and each class using weighted sum of node influence and similarity matrix, and the last is to classify each test sample based on its similarity between every class. The data sets used in our experiments are breast cancer, central nervous system, colon tumor, prostate cancer, acute lymphoblastic leukemia, and lung cancer. experimental results showed that our node influence based method (NIM is more efficient and robust than the support vector machine, K-nearest neighbor, C4.5, naive Bayes, and CART.

Reliability of a treatment-based classification system for subgrouping people with low back pain.

Science.gov (United States)

Henry, Sharon M; Fritz, Julie M; Trombley, Andrea R; Bunn, Janice Y

2012-09-01

Observational, cross-sectional reliability study. To examine the interrater reliability of novice raters in their use of the treatment-based classification (TBC) system for low back pain and to explore the patterns of disagreement in classification errors. Although the interrater reliability of individual test items in the TBC system is moderate to good, some error persists in classification decision making. Understanding which classification errors are common could direct further refinement of the TBC system. Using previously recorded patient data (n = 24), 12 novice raters classified patients according to the TBC schema. These classification results were combined with those of 7 other raters, allowing examination of the overall agreement using the kappa statistic, as well as agreement/disagreement among pairwise comparisons in classification assignments. A chi-square test examined differences in percent agreement between the novice and more experienced raters and differences in classification distributions between these 2 groups of raters. Among 12 novice raters, there was 80.9% agreement in the pairs of classification (κ = 0.62; 95% confidence interval: 0.59, 0.65) and an overall 75.5% agreement (κ = 0.57; 95% confidence interval: 0.55, 0.69) for the combined data set. Raters were least likely to agree on a classification of stabilization (77.5% agreement). The overall percentage of pairwise classification judgments that disagreed was 24.5%, with the most common disagreement being between manipulation and stabilization (11.0%), followed by a mismatch between stabilization and specific exercise (8.2%). Additional refinement is needed to reduce rater disagreement that persists in the TBC decision-making algorithm, particularly in the stabilization category. J Orthop Sports Phys Ther 2012;42(9):797-805, Epub 7 June 2012. doi:10.2519/jospt.2012.4078.

An interobserver reliability comparison between the Orthopaedic Trauma Association's open fracture classification and the Gustilo and Anderson classification.

Science.gov (United States)

Ghoshal, A; Enninghorst, N; Sisak, K; Balogh, Z J

2018-02-01

To evaluate interobserver reliability of the Orthopaedic Trauma Association's open fracture classification system (OTA-OFC). Patients of any age with a first presentation of an open long bone fracture were included. Standard radiographs, wound photographs, and a short clinical description were given to eight orthopaedic surgeons, who independently evaluated the injury using both the Gustilo and Anderson (GA) and OTA-OFC classifications. The responses were compared for variability using Cohen's kappa. The overall interobserver agreement was ĸ = 0.44 for the GA classification and ĸ = 0.49 for OTA-OFC, which reflects moderate agreement (0.41 to 0.60) for both classifications. The agreement in the five categories of OTA-OFC was: for skin, ĸ = 0.55 (moderate); for muscle, ĸ = 0.44 (moderate); for arterial injury, ĸ = 0.74 (substantial); for contamination, ĸ = 0.35 (fair); and for bone loss, ĸ = 0.41 (moderate). Although the OTA-OFC, with similar interobserver agreement to GA, offers a more detailed description of open fractures, further development may be needed to make it a reliable and robust tool. Cite this article: Bone Joint J 2018;100-B:242-6. ©2018 The British Editorial Society of Bone & Joint Surgery.

Influence of nuclei segmentation on breast cancer malignancy classification

Science.gov (United States)

Jelen, Lukasz; Fevens, Thomas; Krzyzak, Adam

2009-02-01

Breast Cancer is one of the most deadly cancers affecting middle-aged women. Accurate diagnosis and prognosis are crucial to reduce the high death rate. Nowadays there are numerous diagnostic tools for breast cancer diagnosis. In this paper we discuss a role of nuclear segmentation from fine needle aspiration biopsy (FNA) slides and its influence on malignancy classification. Classification of malignancy plays a very important role during the diagnosis process of breast cancer. Out of all cancer diagnostic tools, FNA slides provide the most valuable information about the cancer malignancy grade which helps to choose an appropriate treatment. This process involves assessing numerous nuclear features and therefore precise segmentation of nuclei is very important. In this work we compare three powerful segmentation approaches and test their impact on the classification of breast cancer malignancy. The studied approaches involve level set segmentation, fuzzy c-means segmentation and textural segmentation based on co-occurrence matrix. Segmented nuclei were used to extract nuclear features for malignancy classification. For classification purposes four different classifiers were trained and tested with previously extracted features. The compared classifiers are Multilayer Perceptron (MLP), Self-Organizing Maps (SOM), Principal Component-based Neural Network (PCA) and Support Vector Machines (SVM). The presented results show that level set segmentation yields the best results over the three compared approaches and leads to a good feature extraction with a lowest average error rate of 6.51% over four different classifiers. The best performance was recorded for multilayer perceptron with an error rate of 3.07% using fuzzy c-means segmentation.

Quality of nursing intensity data: inter-rater reliability of the patient classification after two decades in clinical use.

Science.gov (United States)

Liljamo, Pia; Kinnunen, Ulla-Mari; Ohtonen, Pasi; Saranto, Kaija

2017-09-01

The aim of this study was to measure the inter-rater reliability of the Oulu Patient Classification and to discuss existing methods of reliability testing. The Oulu Patient Classification, part of the RAFAELA ® System, has been developed to assist nursing managers with the proper allocation of nursing resources. Due to the increased intensity of inpatient care during recent years, there is a need for the reliability testing of the classification, which has been in clinical use for 20 years. Retrospective statistical study. To test inter-rater reliability, a pair of nurses classified the same patients, without knowledge of each other's ratings, as a part of annually conducted standardization. Data on the parallel classifications (n = 19,997) was obtained from inpatient units (n = 32) with different specialties at a university hospital in Finland during 2010-2015. Parallel classification practices were also analysed. The reliability of the overall classification and its subareas were calculated using suitable statistical coefficients. Inter-rater reliability coefficients were a reliable or almost perfect means of considering the nursing intensity category and various practices, but there were detectable differences between subareas. The lowest agreement levels occurred in the subareas 'Planning and Coordination of Nursing Care' and 'Guiding of Care/Continued Care and Emotional Support'. There is a need to develop the descriptions of subareas and to clarify the related concepts. Precise nursing documentation can promote a high level of agreement and reliable results. The traditional overall proportion of agreement does not provide an adequate picture of reliability - weighted kappa coefficients should be used instead. © 2017 John Wiley & Sons Ltd.

Reliability and reproducibility analysis of the AOSpine thoracolumbar spine injury classification system by Chinese spinal surgeons.

Science.gov (United States)

Cheng, Jie; Liu, Peng; Sun, Dong; Qin, Tingzheng; Ma, Zikun; Liu, Jingpei

2017-05-01

The objective of this study was to analyze the interobserver reliability and intraobserver reproducibility of the new AOSpine thoracolumbar spine injury classification system in young Chinese orthopedic surgeons with different levels of experience in spinal trauma. Previous reports suggest that the new AOSpine thoracolumbar spine injury classification system demonstrates acceptable interobserver reliability and intraobserver reproducibility. However, there are few studies in Asia, especially in China. The AOSpine thoracolumbar spine injury classification system was applied to 109 patients with acute, traumatic thoracolumbar spinal injuries by two groups of spinal surgeons with different levels of clinical experience. The Kappa coefficient was used to determine interobserver reliability and intraobserver reproducibility. The overall Kappa coefficient for all cases was 0.362, which represents fair reliability. The Kappa statistic was 0.385 for A-type injuries and 0.292 for B-type injuries, which represents fair reliability, and 0.552 for C-type injuries, which represents moderate reliability. The Kappa coefficient for intraobserver reproducibility was 0.442 for A-type injuries, 0.485 for B-type injuries, and 0.412 for C-type injuries. These values represent moderate reproducibility for all injury types. The raters in Group A provided significantly better interobserver reliability than Group B (P < 0.05). There were no between-group differences in intraobserver reproducibility. This study suggests that the new AO spine injury classification system may be applied in day-to-day clinical practice in China following extensive training of healthcare providers. Further prospective studies in different healthcare providers and clinical settings are essential for validation of this classification system and to assess its utility.

Osteochondritis dissecans of the humeral capitellum: reliability of four classification systems using radiographs and computed tomography.

Science.gov (United States)

Claessen, Femke M A P; van den Ende, Kimberly I M; Doornberg, Job N; Guitton, Thierry G; Eygendaal, Denise; van den Bekerom, Michel P J

2015-10-01

The radiographic appearance of osteochondritis dissecans (OCD) of the humeral capitellum varies according to the stage of the lesion. It is important to evaluate the stage of OCD lesion carefully to guide treatment. We compared the interobserver reliability of currently used classification systems for OCD of the humeral capitellum to identify the most reliable classification system. Thirty-two musculoskeletal radiologists and orthopaedic surgeons specialized in elbow surgery from several countries evaluated anteroposterior and lateral radiographs and corresponding computed tomography (CT) scans of 22 patients to classify the stage of OCD of the humeral capitellum according to the classification systems developed by (1) Minami, (2) Berndt and Harty, (3) Ferkel and Sgaglione, and (4) Anderson on a Web-based study platform including a Digital Imaging and Communications in Medicine viewer. Magnetic resonance imaging was not evaluated as part of this study. We measured agreement among observers using the Siegel and Castellan multirater κ. All OCD classification systems, except for Berndt and Harty, which had poor agreement among observers (κ = 0.20), had fair interobserver agreement: κ was 0.27 for the Minami, 0.23 for Anderson, and 0.22 for Ferkel and Sgaglione classifications. The Minami Classification was significantly more reliable than the other classifications (P reliable for classifying different stages of OCD of the humeral capitellum. However, it is unclear whether radiographic evidence of OCD of the humeral capitellum, as categorized by the Minami Classification, guides treatment in clinical practice as a result of this fair agreement. Copyright © 2015 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

The Reliability of Classifications of Proximal Femoral Fractures with 3-Dimensional Computed Tomography: The New Concept of Comprehensive Classification

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Hiroaki Kijima

2014-01-01

Full Text Available The reliability of proximal femoral fracture classifications using 3DCT was evaluated, and a comprehensive “area classification” was developed. Eleven orthopedists (5–26 years from graduation classified 27 proximal femoral fractures at one hospital from June 2013 to July 2014 based on preoperative images. Various classifications were compared to “area classification.” In “area classification,” the proximal femur is divided into 4 areas with 3 boundary lines: Line-1 is the center of the neck, Line-2 is the border between the neck and the trochanteric zone, and Line-3 links the inferior borders of the greater and lesser trochanters. A fracture only in the first area was classified as a pure first area fracture; one in the first and second area was classified as a 1-2 type fracture. In the same way, fractures were classified as pure 2, 3-4, 1-2-3, and so on. “Area classification” reliability was highest when orthopedists with varying experience classified proximal femoral fractures using 3DCT. Other classifications cannot classify proximal femoral fractures if they exceed each classification’s particular zones. However, fractures that exceed the target zones are “dangerous” fractures. “Area classification” can classify such fractures, and it is therefore useful for selecting osteosynthesis methods.

Validity and reliability of the Greek version of the xerostomia questionnaire in head and neck cancer patients.

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Memtsa, Pinelopi Theopisti; Tolia, Maria; Tzitzikas, Ioannis; Bizakis, Ioannis; Pistevou-Gombaki, Kyriaki; Charalambidou, Martha; Iliopoulou, Chrysoula; Kyrgias, George

2017-03-01

Xerostomia after radiation therapy for head and neck (H&N) cancer has serious effects on patients' quality of life. The purpose of this study was to validate the Greek version of the self-reported eight-item xerostomia questionnaire (XQ) in patients treated with radiotherapy for H&N cancer. The XQ was translated into Greek and administered to 100 XQ patients. An exploratory factor analysis was performed. Reliability measures were calculated. Several types of validity were evaluated. The observer-rated scoring system was also used. The mean XQ value was 41.92 (SD 22.71). Factor analysis revealed the unidimensional nature of the questionnaire. High reliability measures (ICC, Cronbach's α, Pearson coefficients) were obtained. Patients differed statistically significantly in terms of XQ score, depending on the RTOG/EORTC classification. The Greek version of XQ is valid and reliable. Its score is well related to observer's findings and it can be used to evaluate the impact of radiation therapy on the subjective feeling of xerostomia.

Developing a contributing factor classification scheme for Rasmussen's AcciMap: Reliability and validity evaluation.

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Goode, N; Salmon, P M; Taylor, N Z; Lenné, M G; Finch, C F

2017-10-01

One factor potentially limiting the uptake of Rasmussen's (1997) Accimap method by practitioners is the lack of a contributing factor classification scheme to guide accident analyses. This article evaluates the intra- and inter-rater reliability and criterion-referenced validity of a classification scheme developed to support the use of Accimap by led outdoor activity (LOA) practitioners. The classification scheme has two levels: the system level describes the actors, artefacts and activity context in terms of 14 codes; the descriptor level breaks the system level codes down into 107 specific contributing factors. The study involved 11 LOA practitioners using the scheme on two separate occasions to code a pre-determined list of contributing factors identified from four incident reports. Criterion-referenced validity was assessed by comparing the codes selected by LOA practitioners to those selected by the method creators. Mean intra-rater reliability scores at the system (M = 83.6%) and descriptor (M = 74%) levels were acceptable. Mean inter-rater reliability scores were not consistently acceptable for both coding attempts at the system level (M T1  = 68.8%; M T2  = 73.9%), and were poor at the descriptor level (M T1  = 58.5%; M T2  = 64.1%). Mean criterion referenced validity scores at the system level were acceptable (M T1  = 73.9%; M T2  = 75.3%). However, they were not consistently acceptable at the descriptor level (M T1  = 67.6%; M T2  = 70.8%). Overall, the results indicate that the classification scheme does not currently satisfy reliability and validity requirements, and that further work is required. The implications for the design and development of contributing factors classification schemes are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Magnetic resonance imaging texture analysis classification of primary breast cancer

International Nuclear Information System (INIS)

Waugh, S.A.; Lerski, R.A.; Purdie, C.A.; Jordan, L.B.; Vinnicombe, S.; Martin, P.; Thompson, A.M.

2016-01-01

Patient-tailored treatments for breast cancer are based on histological and immunohistochemical (IHC) subtypes. Magnetic Resonance Imaging (MRI) texture analysis (TA) may be useful in non-invasive lesion subtype classification. Women with newly diagnosed primary breast cancer underwent pre-treatment dynamic contrast-enhanced breast MRI. TA was performed using co-occurrence matrix (COM) features, by creating a model on retrospective training data, then prospectively applying to a test set. Analyses were blinded to breast pathology. Subtype classifications were performed using a cross-validated k-nearest-neighbour (k = 3) technique, with accuracy relative to pathology assessed and receiver operator curve (AUROC) calculated. Mann-Whitney U and Kruskal-Wallis tests were used to assess raw entropy feature values. Histological subtype classifications were similar across training (n = 148 cancers) and test sets (n = 73 lesions) using all COM features (training: 75 %, AUROC = 0.816; test: 72.5 %, AUROC = 0.823). Entropy features were significantly different between lobular and ductal cancers (p < 0.001; Mann-Whitney U). IHC classifications using COM features were also similar for training and test data (training: 57.2 %, AUROC = 0.754; test: 57.0 %, AUROC = 0.750). Hormone receptor positive and negative cancers demonstrated significantly different entropy features. Entropy features alone were unable to create a robust classification model. Textural differences on contrast-enhanced MR images may reflect underlying lesion subtypes, which merits testing against treatment response. (orig.)

Magnetic resonance imaging texture analysis classification of primary breast cancer

Energy Technology Data Exchange (ETDEWEB)

Waugh, S.A.; Lerski, R.A. [Ninewells Hospital and Medical School, Department of Medical Physics, Dundee (United Kingdom); Purdie, C.A.; Jordan, L.B. [Ninewells Hospital and Medical School, Department of Pathology, Dundee (United Kingdom); Vinnicombe, S. [University of Dundee, Division of Imaging and Technology, Ninewells Hospital and Medical School, Dundee (United Kingdom); Martin, P. [Ninewells Hospital and Medical School, Department of Clinical Radiology, Dundee (United Kingdom); Thompson, A.M. [University of Texas MD Anderson Cancer Center, Department of Surgical Oncology, Houston, TX (United States)

2016-02-15

Patient-tailored treatments for breast cancer are based on histological and immunohistochemical (IHC) subtypes. Magnetic Resonance Imaging (MRI) texture analysis (TA) may be useful in non-invasive lesion subtype classification. Women with newly diagnosed primary breast cancer underwent pre-treatment dynamic contrast-enhanced breast MRI. TA was performed using co-occurrence matrix (COM) features, by creating a model on retrospective training data, then prospectively applying to a test set. Analyses were blinded to breast pathology. Subtype classifications were performed using a cross-validated k-nearest-neighbour (k = 3) technique, with accuracy relative to pathology assessed and receiver operator curve (AUROC) calculated. Mann-Whitney U and Kruskal-Wallis tests were used to assess raw entropy feature values. Histological subtype classifications were similar across training (n = 148 cancers) and test sets (n = 73 lesions) using all COM features (training: 75 %, AUROC = 0.816; test: 72.5 %, AUROC = 0.823). Entropy features were significantly different between lobular and ductal cancers (p < 0.001; Mann-Whitney U). IHC classifications using COM features were also similar for training and test data (training: 57.2 %, AUROC = 0.754; test: 57.0 %, AUROC = 0.750). Hormone receptor positive and negative cancers demonstrated significantly different entropy features. Entropy features alone were unable to create a robust classification model. Textural differences on contrast-enhanced MR images may reflect underlying lesion subtypes, which merits testing against treatment response. (orig.)

Gastric cancer: epidemiology, prevention, classification, and treatment

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Sitarz R

2018-02-01

Full Text Available Robert Sitarz,1–3 Małgorzata Skierucha,1,2 Jerzy Mielko,1 G Johan A Offerhaus,3 Ryszard Maciejewski,2 Wojciech P Polkowski1 1Department of Surgical Oncology, Medical University of Lublin, Lublin, Poland; 2Department of Human Anatomy, Medical University of Lublin, Lublin, Poland; 3Department of Pathology, University Medical Centre, Utrecht, The Netherlands Abstract: Gastric cancer is the second most common cause of cancer-related deaths in the world, the epidemiology of which has changed within last decades. A trend of steady decline in gastric cancer incidence rates is the effect of the increased standards of hygiene, conscious nutrition, and Helicobacter pylori eradication, which together constitute primary prevention. Avoidance of gastric cancer remains a priority. However, patients with higher risk should be screened for early detection and chemoprevention. Surgical resection enhanced by standardized lymphadenectomy remains the gold standard in gastric cancer therapy. This review briefly summarizes the most important aspects of gastric cancers, which include epidemiology, risk factors, classification, diagnosis, prevention, and treatment. The paper is mostly addressed to physicians who are interested in updating the state of art concerning gastric carcinoma from easily accessible and credible source. Keywords: gastric cancer, epidemiology, classification, risk factors, treatment

IDM-PhyChm-Ens: intelligent decision-making ensemble methodology for classification of human breast cancer using physicochemical properties of amino acids.

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Ali, Safdar; Majid, Abdul; Khan, Asifullah

2014-04-01

Development of an accurate and reliable intelligent decision-making method for the construction of cancer diagnosis system is one of the fast growing research areas of health sciences. Such decision-making system can provide adequate information for cancer diagnosis and drug discovery. Descriptors derived from physicochemical properties of protein sequences are very useful for classifying cancerous proteins. Recently, several interesting research studies have been reported on breast cancer classification. To this end, we propose the exploitation of the physicochemical properties of amino acids in protein primary sequences such as hydrophobicity (Hd) and hydrophilicity (Hb) for breast cancer classification. Hd and Hb properties of amino acids, in recent literature, are reported to be quite effective in characterizing the constituent amino acids and are used to study protein foldings, interactions, structures, and sequence-order effects. Especially, using these physicochemical properties, we observed that proline, serine, tyrosine, cysteine, arginine, and asparagine amino acids offer high discrimination between cancerous and healthy proteins. In addition, unlike traditional ensemble classification approaches, the proposed 'IDM-PhyChm-Ens' method was developed by combining the decision spaces of a specific classifier trained on different feature spaces. The different feature spaces used were amino acid composition, split amino acid composition, and pseudo amino acid composition. Consequently, we have exploited different feature spaces using Hd and Hb properties of amino acids to develop an accurate method for classification of cancerous protein sequences. We developed ensemble classifiers using diverse learning algorithms such as random forest (RF), support vector machines (SVM), and K-nearest neighbor (KNN) trained on different feature spaces. We observed that ensemble-RF, in case of cancer classification, performed better than ensemble-SVM and ensemble-KNN. Our

Call for a Computer-Aided Cancer Detection and Classification Research Initiative in Oman.

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Mirzal, Andri; Chaudhry, Shafique Ahmad

2016-01-01

Cancer is a major health problem in Oman. It is reported that cancer incidence in Oman is the second highest after Saudi Arabia among Gulf Cooperation Council countries. Based on GLOBOCAN estimates, Oman is predicted to face an almost two-fold increase in cancer incidence in the period 2008-2020. However, cancer research in Oman is still in its infancy. This is due to the fact that medical institutions and infrastructure that play central roles in data collection and analysis are relatively new developments in Oman. We believe the country requires an organized plan and efforts to promote local cancer research. In this paper, we discuss current research progress in cancer diagnosis using machine learning techniques to optimize computer aided cancer detection and classification (CAD). We specifically discuss CAD using two major medical data, i.e., medical imaging and microarray gene expression profiling, because medical imaging like mammography, MRI, and PET have been widely used in Oman for assisting radiologists in early cancer diagnosis and microarray data have been proven to be a reliable source for differential diagnosis. We also discuss future cancer research directions and benefits to Oman economy for entering the cancer research and treatment business as it is a multi-billion dollar industry worldwide.

Manual Ability Classification System (MACS: reliability between therapists and parents in Brazil

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Daniela B. R. Silva

2015-02-01

Full Text Available BACKGROUND: The Manual Ability Classification System (MACS has been widely used to describe the manual ability of children with cerebral palsy (CP; however its reliability has not been verified in Brazil. OBJECTIVE: To establish the inter- and intra-rater reliability of the Portuguese-Brazil version of the MACS by comparing the classifications given by therapists and parents of children with CP. METHOD: Data were obtained from 90 children with CP between the ages of 4 and 18 years, who were treated at the neurology and rehabilitation clinics of a Brazilian hospital. Therapists (an occupational therapist and a student classified manual ability (MACS through direct observation and information provided by parents. Therapists and parents used the Portuguese-Brazil version of the MACS. Intra- and inter-rater reliability was obtained using unweighted Kappa coefficient (k and intra-class correlation coefficient (ICC. The Chi-square test was used to identify the predominance of disagreements in the classification of parents and therapists. RESULTS: An almost perfect agreement resulted among therapists [K=0.90 (95% CI 0.83-0.97; ICC=0.97 (95%CI 0.96-0.98], as well as with intra-rater (therapists, with Kappa ranging between 0.83 and 0.95 and ICC between 0.96 and 0.99 for the evaluator with more and less experience in rehabilitation, respectively. The agreement between therapists and parents was fair [K=0.36 (95% CI 0.22-0.50; ICC=0.79 (95% CI 0.70-0.86]. CONCLUSIONS: The Portuguese version of the MACS is a reliable instrument to be used jointly by parents and therapists.

Reliability of the Crowe und Hartofilakidis classifications used in the assessment of the adult dysplastic hip

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Decking, Ralf; Brunner, Alexander; Puhl, Wolfhart [University of Ulm, Orthopaedic Department, RKU, Ulm (Germany); Decking, Jens [Johannes Gutenberg University School of Medicine, Department of Orthopaedic Surgery, Mainz (Germany); Guenther, Klaus-Peter [University of Ulm, Orthopaedic Department, RKU, Ulm (Germany); University-Hospital Carl Gustav Carus, Department of Orthopaedics, Dresden (Germany)

2006-05-15

To assess the inter-observer and intra-observer reliability of two commonly used radiographic classification systems in the evaluation of hip dysplasia in skeletally mature adults. Three observers with different levels of training independently classified 62 dysplastic hips on 51 standard anteriorposterior pelvis radiographs according to the criteria defined by Crowe and by Hartofilakidis. To assess intra-observer reliability, the same radiographs were reviewed 3 months later by the same observers. At the time of the radiographic examination, the mean age of the 51 patients had been 54 years (range 18-82 years). A high correlation concerning the inter- and intra-observer reliability of both systems was demonstrated. Inter-observer reliability displayed a weighted kappa coefficient of 0.82 for the Crowe and 0.75 for the Hartofilakidis classification. Intra-observer reliability showed a kappa coefficient of 0.86 and 0.79, respectively. Both classification systems can be recommended to compare collectives of adult patients with congenital dysplasia of the hip. However, for future clinical practice, it would be advisable to agree on one universally accepted system as a standard in the literature. (orig.)

Reliability of the Crowe und Hartofilakidis classifications used in the assessment of the adult dysplastic hip

International Nuclear Information System (INIS)

Decking, Ralf; Brunner, Alexander; Puhl, Wolfhart; Decking, Jens; Guenther, Klaus-Peter

2006-01-01

To assess the inter-observer and intra-observer reliability of two commonly used radiographic classification systems in the evaluation of hip dysplasia in skeletally mature adults. Three observers with different levels of training independently classified 62 dysplastic hips on 51 standard anteriorposterior pelvis radiographs according to the criteria defined by Crowe and by Hartofilakidis. To assess intra-observer reliability, the same radiographs were reviewed 3 months later by the same observers. At the time of the radiographic examination, the mean age of the 51 patients had been 54 years (range 18-82 years). A high correlation concerning the inter- and intra-observer reliability of both systems was demonstrated. Inter-observer reliability displayed a weighted kappa coefficient of 0.82 for the Crowe and 0.75 for the Hartofilakidis classification. Intra-observer reliability showed a kappa coefficient of 0.86 and 0.79, respectively. Both classification systems can be recommended to compare collectives of adult patients with congenital dysplasia of the hip. However, for future clinical practice, it would be advisable to agree on one universally accepted system as a standard in the literature. (orig.)

The Communication Function Classification System: cultural adaptation, validity, and reliability of the Farsi version for patients with cerebral palsy.

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Soleymani, Zahra; Joveini, Ghodsiye; Baghestani, Ahmad Reza

2015-03-01

This study developed a Farsi language Communication Function Classification System and then tested its reliability and validity. Communication Function Classification System is designed to classify the communication functions of individuals with cerebral palsy. Up until now, there has been no instrument for assessment of this communication function in Iran. The English Communication Function Classification System was translated into Farsi and cross-culturally modified by a panel of experts. Professionals and parents then assessed the content validity of the modified version. A backtranslation of the Farsi version was confirmed by the developer of the English Communication Function Classification System. Face validity was assessed by therapists and parents of 10 patients. The Farsi Communication Function Classification System was administered to 152 individuals with cerebral palsy (age, 2 to 18 years; median age, 10 years; mean age, 9.9 years; standard deviation, 4.3 years). Inter-rater reliability was analyzed between parents, occupational therapists, and speech and language pathologists. The test-retest reliability was assessed for 75 patients with a 14 day interval between tests. The inter-rater reliability of the Communication Function Classification System was 0.81 between speech and language pathologists and occupational therapists, 0.74 between parents and occupational therapists, and 0.88 between parents and speech and language pathologists. The test-retest reliability was 0.96 for occupational therapists, 0.98 for speech and language pathologists, and 0.94 for parents. The findings suggest that the Farsi version of Communication Function Classification System is a reliable and valid measure that can be used in clinical settings to assess communication function in patients with cerebral palsy. Copyright © 2015 Elsevier Inc. All rights reserved.

The influence of spine surgeons' experience on the classification and intraobserver reliability of the novel AOSpine thoracolumbar spine injury classification system : an international study

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Sadiqi, Said; Oner, F. Cumhur; Dvorak, Marcel F.; Aarabi, Bizhan; Schroeder, Gregory D.; Vaccaro, Alexander R.

2015-01-01

Study Design. International validation study. Objective. To investigate the influence of the spine surgeons' level of experience on the intraobserver reliability of the novel AOSpine Thoracolumbar Spine Injury Classification system, and the appropriate classification according to this system.

Cancer classification using the Immunoscore: a worldwide task force.

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Galon, Jérôme; Pagès, Franck; Marincola, Francesco M; Angell, Helen K; Thurin, Magdalena; Lugli, Alessandro; Zlobec, Inti; Berger, Anne; Bifulco, Carlo; Botti, Gerardo; Tatangelo, Fabiana; Britten, Cedrik M; Kreiter, Sebastian; Chouchane, Lotfi; Delrio, Paolo; Arndt, Hartmann; Asslaber, Martin; Maio, Michele; Masucci, Giuseppe V; Mihm, Martin; Vidal-Vanaclocha, Fernando; Allison, James P; Gnjatic, Sacha; Hakansson, Leif; Huber, Christoph; Singh-Jasuja, Harpreet; Ottensmeier, Christian; Zwierzina, Heinz; Laghi, Luigi; Grizzi, Fabio; Ohashi, Pamela S; Shaw, Patricia A; Clarke, Blaise A; Wouters, Bradly G; Kawakami, Yutaka; Hazama, Shoichi; Okuno, Kiyotaka; Wang, Ena; O'Donnell-Tormey, Jill; Lagorce, Christine; Pawelec, Graham; Nishimura, Michael I; Hawkins, Robert; Lapointe, Réjean; Lundqvist, Andreas; Khleif, Samir N; Ogino, Shuji; Gibbs, Peter; Waring, Paul; Sato, Noriyuki; Torigoe, Toshihiko; Itoh, Kyogo; Patel, Prabhu S; Shukla, Shilin N; Palmqvist, Richard; Nagtegaal, Iris D; Wang, Yili; D'Arrigo, Corrado; Kopetz, Scott; Sinicrope, Frank A; Trinchieri, Giorgio; Gajewski, Thomas F; Ascierto, Paolo A; Fox, Bernard A

2012-10-03

Prediction of clinical outcome in cancer is usually achieved by histopathological evaluation of tissue samples obtained during surgical resection of the primary tumor. Traditional tumor staging (AJCC/UICC-TNM classification) summarizes data on tumor burden (T), presence of cancer cells in draining and regional lymph nodes (N) and evidence for metastases (M). However, it is now recognized that clinical outcome can significantly vary among patients within the same stage. The current classification provides limited prognostic information, and does not predict response to therapy. Recent literature has alluded to the importance of the host immune system in controlling tumor progression. Thus, evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy. Accumulating data, collected from large cohorts of human cancers, has demonstrated the impact of immune-classification, which has a prognostic value that may add to the significance of the AJCC/UICC TNM-classification. It is therefore imperative to begin to incorporate the 'Immunoscore' into traditional classification, thus providing an essential prognostic and potentially predictive tool. Introduction of this parameter as a biomarker to classify cancers, as part of routine diagnostic and prognostic assessment of tumors, will facilitate clinical decision-making including rational stratification of patient treatment. Equally, the inherent complexity of quantitative immunohistochemistry, in conjunction with protocol variation across laboratories, analysis of different immune cell types, inconsistent region selection criteria, and variable ways to quantify immune infiltration, all underline the urgent requirement to reach assay harmonization. In an effort to promote the Immunoscore in routine clinical settings, an international task force was initiated. This review represents a follow-up of the announcement of this initiative, and of the J

Cancer classification using the Immunoscore: a worldwide task force

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Galon Jérôme

2012-10-01

Full Text Available Abstract Prediction of clinical outcome in cancer is usually achieved by histopathological evaluation of tissue samples obtained during surgical resection of the primary tumor. Traditional tumor staging (AJCC/UICC-TNM classification summarizes data on tumor burden (T, presence of cancer cells in draining and regional lymph nodes (N and evidence for metastases (M. However, it is now recognized that clinical outcome can significantly vary among patients within the same stage. The current classification provides limited prognostic information, and does not predict response to therapy. Recent literature has alluded to the importance of the host immune system in controlling tumor progression. Thus, evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy. Accumulating data, collected from large cohorts of human cancers, has demonstrated the impact of immune-classification, which has a prognostic value that may add to the significance of the AJCC/UICC TNM-classification. It is therefore imperative to begin to incorporate the ‘Immunoscore’ into traditional classification, thus providing an essential prognostic and potentially predictive tool. Introduction of this parameter as a biomarker to classify cancers, as part of routine diagnostic and prognostic assessment of tumors, will facilitate clinical decision-making including rational stratification of patient treatment. Equally, the inherent complexity of quantitative immunohistochemistry, in conjunction with protocol variation across laboratories, analysis of different immune cell types, inconsistent region selection criteria, and variable ways to quantify immune infiltration, all underline the urgent requirement to reach assay harmonization. In an effort to promote the Immunoscore in routine clinical settings, an international task force was initiated. This review represents a follow-up of the announcement of

Geometric classification of scalp hair for valid drug testing, 6 more reliable than 8 hair curl groups.

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K Mkentane

Full Text Available Curly hair is reported to contain higher lipid content than straight hair, which may influence incorporation of lipid soluble drugs. The use of race to describe hair curl variation (Asian, Caucasian and African is unscientific yet common in medical literature (including reports of drug levels in hair. This study investigated the reliability of a geometric classification of hair (based on 3 measurements: the curve diameter, curl index and number of waves.After ethical approval and informed consent, proximal virgin (6cm hair sampled from the vertex of scalp in 48 healthy volunteers were evaluated. Three raters each scored hairs from 48 volunteers at two occasions each for the 8 and 6-group classifications. One rater applied the 6-group classification to 80 additional volunteers in order to further confirm the reliability of this system. The Kappa statistic was used to assess intra and inter rater agreement.Each rater classified 480 hairs on each occasion. No rater classified any volunteer's 10 hairs into the same group; the most frequently occurring group was used for analysis. The inter-rater agreement was poor for the 8-groups (k = 0.418 but improved for the 6-groups (k = 0.671. The intra-rater agreement also improved (k = 0.444 to 0.648 versus 0.599 to 0.836 for 6-groups; that for the one evaluator for all volunteers was good (k = 0.754.Although small, this is the first study to test the reliability of a geometric classification. The 6-group method is more reliable. However, a digital classification system is likely to reduce operator error. A reliable objective classification of human hair curl is long overdue, particularly with the increasing use of hair as a testing substrate for treatment compliance in Medicine.

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey.

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Nahid, Abdullah-Al; Kong, Yinan

2017-01-01

Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors' and physicians' time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN), Logic Based classifiers such as the Random Forest (RF) algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey

Directory of Open Access Journals (Sweden)

Abdullah-Al Nahid

2017-01-01

Full Text Available Breast cancer is one of the largest causes of women’s death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors’ and physicians’ time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN, Logic Based classifiers such as the Random Forest (RF algorithm, Support Vector Machines (SVM, Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.

Reliability analysis of the AOSpine thoracolumbar spine injury classification system by a worldwide group of naïve spinal surgeons.

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Kepler, Christopher K; Vaccaro, Alexander R; Koerner, John D; Dvorak, Marcel F; Kandziora, Frank; Rajasekaran, Shanmuganathan; Aarabi, Bizhan; Vialle, Luiz R; Fehlings, Michael G; Schroeder, Gregory D; Reinhold, Maximilian; Schnake, Klaus John; Bellabarba, Carlo; Cumhur Öner, F

2016-04-01

The aims of this study were (1) to demonstrate the AOSpine thoracolumbar spine injury classification system can be reliably applied by an international group of surgeons and (2) to delineate those injury types which are difficult for spine surgeons to classify reliably. A previously described classification system of thoracolumbar injuries which consists of a morphologic classification of the fracture, a grading system for the neurologic status and relevant patient-specific modifiers was applied to 25 cases by 100 spinal surgeons from across the world twice independently, in grading sessions 1 month apart. The results were analyzed for classification reliability using the Kappa coefficient (κ). The overall Kappa coefficient for all cases was 0.56, which represents moderate reliability. Kappa values describing interobserver agreement were 0.80 for type A injuries, 0.68 for type B injuries and 0.72 for type C injuries, all representing substantial reliability. The lowest level of agreement for specific subtypes was for fracture subtype A4 (Kappa = 0.19). Intraobserver analysis demonstrated overall average Kappa statistic for subtype grading of 0.68 also representing substantial reproducibility. In a worldwide sample of spinal surgeons without previous exposure to the recently described AOSpine Thoracolumbar Spine Injury Classification System, we demonstrated moderate interobserver and substantial intraobserver reliability. These results suggest that most spine surgeons can reliably apply this system to spine trauma patients as or more reliably than previously described systems.

Lung cancer gene expression database analysis incorporating prior knowledge with support vector machine-based classification method

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Huang Desheng

2009-07-01

Full Text Available Abstract Background A reliable and precise classification is essential for successful diagnosis and treatment of cancer. Gene expression microarrays have provided the high-throughput platform to discover genomic biomarkers for cancer diagnosis and prognosis. Rational use of the available bioinformation can not only effectively remove or suppress noise in gene chips, but also avoid one-sided results of separate experiment. However, only some studies have been aware of the importance of prior information in cancer classification. Methods Together with the application of support vector machine as the discriminant approach, we proposed one modified method that incorporated prior knowledge into cancer classification based on gene expression data to improve accuracy. A public well-known dataset, Malignant pleural mesothelioma and lung adenocarcinoma gene expression database, was used in this study. Prior knowledge is viewed here as a means of directing the classifier using known lung adenocarcinoma related genes. The procedures were performed by software R 2.80. Results The modified method performed better after incorporating prior knowledge. Accuracy of the modified method improved from 98.86% to 100% in training set and from 98.51% to 99.06% in test set. The standard deviations of the modified method decreased from 0.26% to 0 in training set and from 3.04% to 2.10% in test set. Conclusion The method that incorporates prior knowledge into discriminant analysis could effectively improve the capacity and reduce the impact of noise. This idea may have good future not only in practice but also in methodology.

Association between gastric cancer and the Kyoto classification of gastritis.

Science.gov (United States)

Shichijo, Satoki; Hirata, Yoshihiro; Niikura, Ryota; Hayakawa, Yoku; Yamada, Atsuo; Koike, Kazuhiko

2017-09-01

Histological gastritis is associated with gastric cancer, but its diagnosis requires biopsy. Many classifications of endoscopic gastritis are available, but not all are useful for risk stratification of gastric cancer. The Kyoto Classification of Gastritis was proposed at the 85th Congress of the Japan Gastroenterological Endoscopy Society. This cross-sectional study evaluated the usefulness of the Kyoto Classification of Gastritis for risk stratification of gastric cancer. From August 2013 to September 2014, esophagogastroduodenoscopy was performed and the gastric findings evaluated according to the Kyoto Classification of Gastritis in a total of 4062 patients. The following five endoscopic findings were selected based on previous reports: atrophy, intestinal metaplasia, enlarged folds, nodularity, and diffuse redness. A total of 3392 patients (1746 [51%] men and 1646 [49%] women) were analyzed. Among them, 107 gastric cancers were diagnosed. Atrophy was found in 2585 (78%) and intestinal metaplasia in 924 (27%). Enlarged folds, nodularity, and diffuse redness were found in 197 (5.8%), 22 (0.6%), and 573 (17%), respectively. In univariate analyses, the severity of atrophy, intestinal metaplasia, diffuse redness, age, and male sex were associated with gastric cancer. In a multivariate analysis, atrophy and male sex were found to be independent risk factors. Younger age and severe atrophy were determined to be associated with diffuse-type gastric cancer. Endoscopic detection of atrophy was associated with the risk of gastric cancer. Thus, patients with severe atrophy should be examined carefully and may require intensive follow-up. © 2017 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

A New Direction of Cancer Classification: Positive Effect of Low-Ranking MicroRNAs.

Science.gov (United States)

Li, Feifei; Piao, Minghao; Piao, Yongjun; Li, Meijing; Ryu, Keun Ho

2014-10-01

Many studies based on microRNA (miRNA) expression profiles showed a new aspect of cancer classification. Because one characteristic of miRNA expression data is the high dimensionality, feature selection methods have been used to facilitate dimensionality reduction. The feature selection methods have one shortcoming thus far: they just consider the problem of where feature to class is 1:1 or n:1. However, because one miRNA may influence more than one type of cancer, human miRNA is considered to be ranked low in traditional feature selection methods and are removed most of the time. In view of the limitation of the miRNA number, low-ranking miRNAs are also important to cancer classification. We considered both high- and low-ranking features to cover all problems (1:1, n:1, 1:n, and m:n) in cancer classification. First, we used the correlation-based feature selection method to select the high-ranking miRNAs, and chose the support vector machine, Bayes network, decision tree, k-nearest-neighbor, and logistic classifier to construct cancer classification. Then, we chose Chi-square test, information gain, gain ratio, and Pearson's correlation feature selection methods to build the m:n feature subset, and used the selected miRNAs to determine cancer classification. The low-ranking miRNA expression profiles achieved higher classification accuracy compared with just using high-ranking miRNAs in traditional feature selection methods. Our results demonstrate that the m:n feature subset made a positive impression of low-ranking miRNAs in cancer classification.

Rater Reliability of the Hardy Classification for Pituitary Adenomas in the Magnetic Resonance Imaging Era.

Science.gov (United States)

Mooney, Michael A; Hardesty, Douglas A; Sheehy, John P; Bird, C Roger; Chapple, Kristina; White, William L; Little, Andrew S

2017-10-01

Objectives  The Hardy classification is used to classify pituitary tumors for clinical and research purposes. The scale was developed using lateral skull radiographs and encephalograms, and its reliability has not been evaluated in the magnetic resonance imaging (MRI) era. Design  Fifty preoperative MRI scans of biopsy-proven pituitary adenomas using the sellar invasion and suprasellar extension components of the Hardy scale were reviewed. Setting  This study was a cohort study set at a single institution. Participants  There were six independent raters. Main Outcome Measures  The main outcome measures of this study were interrater reliability, intrarater reliability, and percent agreement. Results  Overall interrater reliability of both Hardy subscales on MRI was strong. However, reliability of the intermediate scores was weak, and percent agreement among raters was poor (12-16%) using the full scales. Dichotomizing the scale into clinically useful groups maintained strong interrater reliability for the sellar invasion scale and increased the percent agreement for both scales. Conclusion  This study raises important questions about the reliability of the original Hardy classification. Editing the measure to a clinically relevant dichotomous scale simplifies the rating process and may be useful for preoperative tumor characterization in the MRI era. Future research studies should use the dichotomized Hardy scale (sellar invasion Grades 0-III versus Grade IV, suprasellar extension Types 0-C versus Type D).

Reliability of classification of cerebral palsy in low-birthweight children in four countries

NARCIS (Netherlands)

Paneth, N.; Qiu, H.; Rosenbaum, P.; Saigal, S.; Bishai, S.; Jetton, J.; Ouden, L. den; Broyles, S.; Tyson, J.; Kugler, K.

2003-01-01

The reliability of classification of cerebral palsy (CP) in low-birthweight children was assessed by using clinical and research study records sampled from population-based cohort studies in the USA, the Netherlands, Canada, and Germany. Records of neurological examination findings and functional

An enhanced topologically significant directed random walk in cancer classification using gene expression datasets

Directory of Open Access Journals (Sweden)

Choon Sen Seah

2017-12-01

Full Text Available Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed random walk is proposed as one of the cancerous classification approach which have higher sensitivity of risk gene prediction and higher accuracy of cancer classification. In this paper, the methodology and material used for the experiment are presented. Tuning parameter selection method and weight as parameter are applied in proposed approach. Gene expression dataset is used as the input datasets while pathway dataset is used to build a directed graph, as reference datasets, to complete the bias process in random walk approach. In addition, we demonstrate that our approach can improve sensitive predictions with higher accuracy and biological meaningful classification result. Comparison result takes place between significant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification.

Three-class classification in computer-aided diagnosis of breast cancer by support vector machine

Science.gov (United States)

Sun, Xuejun; Qian, Wei; Song, Dansheng

2004-05-01

Design of classifier in computer-aided diagnosis (CAD) scheme of breast cancer plays important role to its overall performance in sensitivity and specificity. Classification of a detected object as malignant lesion, benign lesion, or normal tissue on mammogram is a typical three-class pattern recognition problem. This paper presents a three-class classification approach by using two-stage classifier combined with support vector machine (SVM) learning algorithm for classification of breast cancer on mammograms. The first classification stage is used to detect abnormal areas and normal breast tissues, and the second stage is for classification of malignant or benign in detected abnormal objects. A series of spatial, morphology and texture features have been extracted on detected objects areas. By using genetic algorithm (GA), different feature groups for different stage classification have been investigated. Computerized free-response receiver operating characteristic (FROC) and receiver operating characteristic (ROC) analyses have been employed in different classification stages. Results have shown that obvious performance improvement in both sensitivity and specificity was observed through proposed classification approach compared with conventional two-class classification approaches, indicating its effectiveness in classification of breast cancer on mammograms.

Evolving cancer classification in the era of personalized medicine: A primer for radiologists

Energy Technology Data Exchange (ETDEWEB)

O' Neill, Alibhe C.; Jagannathan, Jyothi P.; Ramaiya, Nikhil H. [Dept. of of Imaging, Dana Farber Cancer Institute, Boston (United States)

2017-01-15

Traditionally tumors were classified based on anatomic location but now specific genetic mutations in cancers are leading to treatment of tumors with molecular targeted therapies. This has led to a paradigm shift in the classification and treatment of cancer. Tumors treated with molecular targeted therapies often show morphological changes rather than change in size and are associated with class specific and drug specific toxicities, different from those encountered with conventional chemotherapeutic agents. It is important for the radiologists to be familiar with the new cancer classification and the various treatment strategies employed, in order to effectively communicate and participate in the multi-disciplinary care. In this paper we will focus on lung cancer as a prototype of the new molecular classification.

Classification of cancerous cells based on the one-class problem approach

Science.gov (United States)

Murshed, Nabeel A.; Bortolozzi, Flavio; Sabourin, Robert

1996-03-01

One of the most important factors in reducing the effect of cancerous diseases is the early diagnosis, which requires a good and a robust method. With the advancement of computer technologies and digital image processing, the development of a computer-based system has become feasible. In this paper, we introduce a new approach for the detection of cancerous cells. This approach is based on the one-class problem approach, through which the classification system need only be trained with patterns of cancerous cells. This reduces the burden of the training task by about 50%. Based on this approach, a computer-based classification system is developed, based on the Fuzzy ARTMAP neural networks. Experimental results were performed using a set of 542 patterns taken from a sample of breast cancer. Results of the experiment show 98% correct identification of cancerous cells and 95% correct identification of non-cancerous cells.

Classification between normal and tumor tissues based on the pair-wise gene expression ratio

International Nuclear Information System (INIS)

Yap, YeeLeng; Zhang, XueWu; Ling, MT; Wang, XiangHong; Wong, YC; Danchin, Antoine

2004-01-01

Precise classification of cancer types is critically important for early cancer diagnosis and treatment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. However, reliable cancer-related signals are generally lacking. Using recent datasets on colon and prostate cancer, a data transformation procedure from single gene expression to pair-wise gene expression ratio is proposed. Making use of the internal consistency of each expression profiling dataset this transformation improves the signal to noise ratio of the dataset and uncovers new relevant cancer-related signals (features). The efficiency in using the transformed dataset to perform normal/tumor classification was investigated using feature partitioning with informative features (gene annotation) as discriminating axes (single gene expression or pair-wise gene expression ratio). Classification results were compared to the original datasets for up to 10-feature model classifiers. 82 and 262 genes that have high correlation to tissue phenotype were selected from the colon and prostate datasets respectively. Remarkably, data transformation of the highly noisy expression data successfully led to lower the coefficient of variation (CV) for the within-class samples as well as improved the correlation with tissue phenotypes. The transformed dataset exhibited lower CV when compared to that of single gene expression. In the colon cancer set, the minimum CV decreased from 45.3% to 16.5%. In prostate cancer, comparable CV was achieved with and without transformation. This improvement in CV, coupled with the improved correlation between the pair-wise gene expression ratio and tissue phenotypes, yielded higher classification efficiency, especially with the colon dataset – from 87.1% to 93.5%. Over 90% of the top ten discriminating axes in both datasets showed significant improvement after data transformation. The high classification efficiency achieved suggested

Prognostic classifications of lymph node involvement in lung cancer and current International Association for the Study of Lung Cancer descriptive classification in zones.

Science.gov (United States)

Riquet, Marc; Arame, Alex; Foucault, Christophe; Le Pimpec Barthes, Françoise

2010-09-01

The lymphatic drainage of solid organ tumors crosses through the lymph nodes (LNs) whose tumoral involvement may still be considered as local disease. Concerning lung cancer, LN involvement may be intrapulmonary (N1), and mediastinal and/or extra-thoracic. More than 30 years ago, mediastinal involved LNs were all considered as N2, and outside the scope of surgery. In 1978, Naruke presented an original article entitled 'Lymph node mapping and curability at various levels of metastasis in resected lung cancer', demonstrating that N2 was not a contraindication to surgery in all patients. The map permitted to localize the favorable N2 on the lung cancer ipsilateral side of the mediastinum. Several maps ensued aiming to discriminate between right and left involvement (1983), and to distinguish N2 (ipsilateral) and N3 (contralateral) mediastinal LN involvement (1983, 1986). The last map (1997 regional LN classification) was recently replaced by a descriptive classification in anatomical zones. This new LN map of the TNM classification for lung cancer is a step toward using anatomical view points which might be the best way to better understand lung cancer lymphatic spread. Nowadays, the LNs are easily identified by current radiological imaging, and their resectability may be anticipated. Each LN chain may be removed by en-bloc lymphadenectomy performed during radical lung resection, a safe procedure which seems to be more oncological based than sampling, and which avoids the source of discrepancies pointed out during the labeling of LN stations by surgeons.

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey

OpenAIRE

Nahid, Abdullah-Al; Kong, Yinan

2017-01-01

Breast cancer is one of the largest causes of women’s death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors’ and physicians’ time. Despite the various publications on breast image classification, very few review papers are available w...

EPA`s program for risk assessment guidelines: Cancer classification issues

Energy Technology Data Exchange (ETDEWEB)

Wiltse, J. [Environmental Protection Agency, Washington, DC (United States)

1990-12-31

Issues presented are related to classification of weight of evidence in cancer risk assessments. The focus in this paper is on lines of evidence used in constructing a conclusion about potential human carcinogenicity. The paper also discusses issues that are mistakenly addressed as classification issues but are really part of the risk assessment process. 2 figs.

Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony.

Science.gov (United States)

Gao, Lingyun; Ye, Mingquan; Wu, Changrong

2017-11-29

Intelligent optimization algorithms have advantages in dealing with complex nonlinear problems accompanied by good flexibility and adaptability. In this paper, the FCBF (Fast Correlation-Based Feature selection) method is used to filter irrelevant and redundant features in order to improve the quality of cancer classification. Then, we perform classification based on SVM (Support Vector Machine) optimized by PSO (Particle Swarm Optimization) combined with ABC (Artificial Bee Colony) approaches, which is represented as PA-SVM. The proposed PA-SVM method is applied to nine cancer datasets, including five datasets of outcome prediction and a protein dataset of ovarian cancer. By comparison with other classification methods, the results demonstrate the effectiveness and the robustness of the proposed PA-SVM method in handling various types of data for cancer classification.

Efficacy of the Kyoto Classification of Gastritis in Identifying Patients at High Risk for Gastric Cancer.

Science.gov (United States)

Sugimoto, Mitsushige; Ban, Hiromitsu; Ichikawa, Hitomi; Sahara, Shu; Otsuka, Taketo; Inatomi, Osamu; Bamba, Shigeki; Furuta, Takahisa; Andoh, Akira

2017-01-01

Objective The Kyoto gastritis classification categorizes the endoscopic characteristics of Helicobacter pylori (H. pylori) infection-associated gastritis and identifies patterns associated with a high risk of gastric cancer. We investigated its efficacy, comparing scores in patients with H. pylori-associated gastritis and with gastric cancer. Methods A total of 1,200 patients with H. pylori-positive gastritis alone (n=932), early-stage H. pylori-positive gastric cancer (n=189), and successfully treated H. pylori-negative cancer (n=79) were endoscopically graded according to the Kyoto gastritis classification for atrophy, intestinal metaplasia, fold hypertrophy, nodularity, and diffuse redness. Results The prevalence of O-II/O-III-type atrophy according to the Kimura-Takemoto classification in early-stage H. pylori-positive gastric cancer and successfully treated H. pylori-negative cancer groups was 45.1%, which was significantly higher than in subjects with gastritis alone (12.7%, pgastritis scores of atrophy and intestinal metaplasia in the H. pylori-positive cancer group were significantly higher than in subjects with gastritis alone (all pgastritis classification may thus be useful for detecting these patients.

Inter- and intrarater reliability of the Chicago Classification in pediatric high-resolution esophageal manometry recordings

NARCIS (Netherlands)

Singendonk, M. M. J.; Smits, M. J.; Heijting, I. E.; van Wijk, M. P.; Nurko, S.; Rosen, R.; Weijenborg, P. W.; Abu-Assi, R.; Hoekman, D. R.; Kuizenga-Wessel, S.; Seiboth, G.; Benninga, M. A.; Omari, T. I.; Kritas, S.

2015-01-01

The Chicago Classification (CC) facilitates interpretation of high-resolution manometry (HRM) recordings. Application of this adult based algorithm to the pediatric population is unknown. We therefore assessed intra and interrater reliability of software-based CC diagnosis in a pediatric cohort.

Using fuzzy association rule mining in cancer classification

International Nuclear Information System (INIS)

Mahmoodian, Hamid; Marhaban, M.H.; Abdulrahim, Raha; Rosli, Rozita; Saripan, Iqbal

2011-01-01

Full text: The classification of the cancer tumors based on gene expression profiles has been extensively studied in numbers of studies. A wide variety of cancer datasets have been implemented by the various methods of gene selec tion and classification to identify the behavior of the genes in tumors and find the relationships between them and outcome of diseases. Interpretability of the model, which is developed by fuzzy rules and linguistic variables in this study, has been rarely considered. In addition, creating a fuzzy classifier with high performance in classification that uses a subset of significant genes which have been selected by different types of gene selection methods is another goal of this study. A new algorithm has been developed to identify the fuzzy rules and significant genes based on fuzzy association rule mining. At first, different subset of genes which have been selected by different methods, were used to generate primary fuzzy classifiers separately and then proposed algorithm was implemented to mix the genes which have been associated in the primary classifiers and generate a new classifier. The results show that fuzzy classifier can classify the tumors with high performance while presenting the relationships between the genes by linguistic variables

Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRI

Energy Technology Data Exchange (ETDEWEB)

Dikaios, Nikolaos; Halligan, Steve; Taylor, Stuart; Atkinson, David; Punwani, Shonit [University College London, Centre for Medical Imaging, London (United Kingdom); University College London Hospital, Departments of Radiology, London (United Kingdom); Alkalbani, Jokha; Sidhu, Harbir Singh [University College London, Centre for Medical Imaging, London (United Kingdom); Abd-Alazeez, Mohamed; Ahmed, Hashim U.; Emberton, Mark [University College London, Research Department of Urology, Division of Surgery and Interventional Science, London (United Kingdom); Kirkham, Alex [University College London Hospital, Departments of Radiology, London (United Kingdom); Freeman, Alex [University College London Hospital, Department of Histopathology, London (United Kingdom)

2015-09-15

To assess the interchangeability of zone-specific (peripheral-zone (PZ) and transition-zone (TZ)) multiparametric-MRI (mp-MRI) logistic-regression (LR) models for classification of prostate cancer. Two hundred and thirty-one patients (70 TZ training-cohort; 76 PZ training-cohort; 85 TZ temporal validation-cohort) underwent mp-MRI and transperineal-template-prostate-mapping biopsy. PZ and TZ uni/multi-variate mp-MRI LR-models for classification of significant cancer (any cancer-core-length (CCL) with Gleason > 3 + 3 or any grade with CCL ≥ 4 mm) were derived from the respective cohorts and validated within the same zone by leave-one-out analysis. Inter-zonal performance was tested by applying TZ models to the PZ training-cohort and vice-versa. Classification performance of TZ models for TZ cancer was further assessed in the TZ validation-cohort. ROC area-under-curve (ROC-AUC) analysis was used to compare models. The univariate parameters with the best classification performance were the normalised T2 signal (T2nSI) within the TZ (ROC-AUC = 0.77) and normalized early contrast-enhanced T1 signal (DCE-nSI) within the PZ (ROC-AUC = 0.79). Performance was not significantly improved by bi-variate/tri-variate modelling. PZ models that contained DCE-nSI performed poorly in classification of TZ cancer. The TZ model based solely on maximum-enhancement poorly classified PZ cancer. LR-models dependent on DCE-MRI parameters alone are not interchangeable between prostatic zones; however, models based exclusively on T2 and/or ADC are more robust for inter-zonal application. (orig.)

A protein and mRNA expression-based classification of gastric cancer.

Science.gov (United States)

Setia, Namrata; Agoston, Agoston T; Han, Hye S; Mullen, John T; Duda, Dan G; Clark, Jeffrey W; Deshpande, Vikram; Mino-Kenudson, Mari; Srivastava, Amitabh; Lennerz, Jochen K; Hong, Theodore S; Kwak, Eunice L; Lauwers, Gregory Y

2016-07-01

The overall survival of gastric carcinoma patients remains poor despite improved control over known risk factors and surveillance. This highlights the need for new classifications, driven towards identification of potential therapeutic targets. Using sophisticated molecular technologies and analysis, three groups recently provided genetic and epigenetic molecular classifications of gastric cancer (The Cancer Genome Atlas, 'Singapore-Duke' study, and Asian Cancer Research Group). Suggested by these classifications, here, we examined the expression of 14 biomarkers in a cohort of 146 gastric adenocarcinomas and performed unsupervised hierarchical clustering analysis using less expensive and widely available immunohistochemistry and in situ hybridization. Ultimately, we identified five groups of gastric cancers based on Epstein-Barr virus (EBV) positivity, microsatellite instability, aberrant E-cadherin, and p53 expression; the remaining cases constituted a group characterized by normal p53 expression. In addition, the five categories correspond to the reported molecular subgroups by virtue of clinicopathologic features. Furthermore, evaluation between these clusters and survival using the Cox proportional hazards model showed a trend for superior survival in the EBV and microsatellite-instable related adenocarcinomas. In conclusion, we offer as a proposal a simplified algorithm that is able to reproduce the recently proposed molecular subgroups of gastric adenocarcinoma, using immunohistochemical and in situ hybridization techniques.

Identifying colon cancer risk modules with better classification performance based on human signaling network.

Science.gov (United States)

Qu, Xiaoli; Xie, Ruiqiang; Chen, Lina; Feng, Chenchen; Zhou, Yanyan; Li, Wan; Huang, Hao; Jia, Xu; Lv, Junjie; He, Yuehan; Du, Youwen; Li, Weiguo; Shi, Yuchen; He, Weiming

2014-10-01

Identifying differences between normal and tumor samples from a modular perspective may help to improve our understanding of the mechanisms responsible for colon cancer. Many cancer studies have shown that signaling transduction and biological pathways are disturbed in disease states, and expression profiles can distinguish variations in diseases. In this study, we integrated a weighted human signaling network and gene expression profiles to select risk modules associated with tumor conditions. Risk modules as classification features by our method had a better classification performance than other methods, and one risk module for colon cancer had a good classification performance for distinguishing between normal/tumor samples and between tumor stages. All genes in the module were annotated to the biological process of positive regulation of cell proliferation, and were highly associated with colon cancer. These results suggested that these genes might be the potential risk genes for colon cancer. Copyright © 2013. Published by Elsevier Inc.

Reliability of a New Radiographic Classification for Developmental Dysplasia of the Hip.

Science.gov (United States)

Narayanan, Unni; Mulpuri, Kishore; Sankar, Wudbhav N; Clarke, Nicholas M P; Hosalkar, Harish; Price, Charles T

2015-01-01

Existing radiographic classification schemes (eg, Tönnis criteria) for DDH quantify the severity of disease based on the position of the ossific nucleus relative to Hilgenreiner's and Perkin's lines. By definition, this method requires the presence of an ossification centre, which can be delayed in appearance and eccentric in location within the femoral head. A new radiographic classification system has been developed by the International Hip Dysplasia Institute (IHDI), which uses the mid-point of the proximal femoral metaphysis as a reference landmark, and can therefore be applied to children of all ages. The purpose of this study was to compare the reliability of this new method with that of Tönnis, as the first step in establishing its validity and clinical utility. Twenty standardized anteroposterior pelvic radiographs of children with untreated DDH were selected purposefully to capture the spectrum of age (range, 3 to 32 mo) at presentation and disease severity. Each of the hips was classified separately by the IHDI and Tönnis methods by 6 experienced pediatric orthopaedists from the United States, Canada, Mexico, United Kingdom, and by 2 orthopaedic senior residents. The inter-rater reliability was tested using the Intra Class Correlation coefficient (ICC) to measure concordance between raters. All 40 hips were classifiable by the IHDI method by all raters. Ten of the 40 hips could not be classified by the Tönnis method because of the absence of the ossific nucleus on one or both sides. The ICC (95% confidence interval) for the IHDI method for all raters was 0.90 (0.83-0.95) and 0.95 (0.91-0.98) for the right and left hips, respectively. The corresponding ICCs for the Tönnis method were 0.63 (0.46-0.80) and 0.60 (0.43-0.78), respectively. There was no significant difference between the ICCs of the 6 experts and 2 trainees. The IHDI method of classification has excellent inter-rater reliability, both among experts and novices, and is more widely

Classification of mitocans, anti-cancer drugs acting on mitochondria

Czech Academy of Sciences Publication Activity Database

Neužil, Jiří; Dong, L. F.; Rohlena, Jakub; Truksa, Jaroslav; Ralph, S. J.

2013-01-01

Roč. 13, č. 3 (2013), s. 199-208 ISSN 1567-7249 Institutional research plan: CEZ:AV0Z50520701 Keywords : Mitocans * Anti-cancer therapeutics * Classification Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 3.524, year: 2013

Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

Directory of Open Access Journals (Sweden)

Maolong Xi

2016-01-01

Full Text Available This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO for cancer feature gene selection, coupling support vector machine (SVM for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV. Finally, the BQPSO coupling SVM (BQPSO/SVM, binary PSO coupling SVM (BPSO/SVM, and genetic algorithm coupling SVM (GA/SVM are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.

Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

Science.gov (United States)

Sun, Jun; Liu, Li; Fan, Fangyun; Wu, Xiaojun

2016-01-01

This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms. PMID:27642363

Ultrasonographic characteristics and BI-RADS-US classification of BRCA1 mutation-associated breast cancer in Guangxi, China.

Science.gov (United States)

Li, Cheng; Liu, Junjie; Wang, Sida; Chen, Yuanyuan; Yuan, Zhigang; Zeng, Jian; Li, Zhixian

2015-01-01

To retrospectively analyze and compare the ultrasonographic characteristics and BI-RADS-US classification between patients with BRCA1 mutation-associated breast cancer and those without BRCA1 gene mutation in Guangxi, China. The study was performed in 36 lesions from 34 BRCA1 mutation-associated breast cancer patients. A total of 422 lesions from 422 breast cancer patients without BRCA1 mutations served as control group. The comparison of the ultrasonographic features and BI-RADS-US classification between two the groups were reviewed. More complex inner echo was disclosed in BRCA1 mutation-associated breast cancer patients (x(2) = 4.741, P = 0.029). The BI-RADS classification of BRCA1 mutation-associated breast cancer was lower (U = 6094.0, P = 0.022). BRCA1 mutation-associated breast cancer frequently displays as microlobulated margin and complex echo. It also shows more benign characteristics in morphology, and the BI-RADS classification is prone to be underestimated.

Classification of mitocans, anti-cancer drugs acting on mitochondria

Czech Academy of Sciences Publication Activity Database

Neužil, Jiří; Dong, L. F.; Rohlena, Jakub; Truksa, Jaroslav; Ralph, S. J.

2013-01-01

Roč. 13, č. 3 (2013), s. 199-208 ISSN 1567-7249 Institutional research plan: CEZ:AV0Z50520701 Keywords : Mitocans * Anti-cancer therapeutics * Classification Subject RIV: EB - Gene tics ; Molecular Biology Impact factor: 3.524, year: 2013

New Atrophic Acne Scar Classification: Reliability of Assessments Based on Size, Shape, and Number.

Science.gov (United States)

Kang, Sewon; Lozada, Vicente Torres; Bettoli, Vincenzo; Tan, Jerry; Rueda, Maria Jose; Layton, Alison; Petit, Lauren; Dréno, Brigitte

2016-06-01

Post-acne atrophic scarring is a major concern for which standardized outcome measures are needed. Traditionally, this type of scar has been classified based on shape; but survey of practicing dermatologists has shown that atrophic scar morphology has not been well enough defined to allow good agreement in clinical classification. Reliance on clinical assessment is still needed at the current time, since objective tools are not yet available in routine practice. Evaluate classification for atrophic acne scars by shape, size, and facial location and establish reliability in assessments. We conducted a non-interventional study with dermatologists performing live clinical assessments of atrophic acne scars. To objectively compare identification of lesions, individual lesions were marked on a high-resolution photo of the patient that was displayed on a computer during the clinical evaluation. The Jacob clinical classification system was used to define three primary shapes of scars 1) icepick, 2) boxcar, and 3) rolling. To determine agreement for classification by size, independent technicians assessed the investigators' markings on digital images. Identical localization of scars was denoted if the maximal distance between their centers was ≤ 60 pixels (approximately 3 mm). Raters assessed scars on the same patients twice (morning/afternoon). Aggregate models of rater assessments were created and analyzed for agreement. Raters counted a mean scar count per subject ranging from 15.75 to 40.25 scars. Approximately 50% of scars were identified by all raters and ~75% of scars were identified by at least 2 of 3 raters (weak agreement, Kappa pairwise agreement 0.30). Agreement between consecutive counts was moderate, with Kappa index ranging from 0.26 to 0.47 (after exclusion of one outlier investigator who had significantly higher counts than all others). Shape classifications of icepick, boxcar, and rolling differed significantly between raters and even for same raters at

British athletics muscle injury classification: a reliability study for a new grading system

International Nuclear Information System (INIS)

Patel, A.; Chakraverty, J.; Pollock, N.; Chakraverty, R.; Suokas, A.K.; James, S.L.

2015-01-01

Aim: To implement and validate the newly proposed British athletics muscle injury classification in the assessment of hamstring injuries in track and field athletes and to analyse the nature and frequency of the discrepancies. Materials and methods: This was a retrospective study analysing hamstring injuries in elite British athletes using the proposed classification system. Classification of 65 hamstring injuries in 45 high-level athletes by two radiologists at two time points 4 months apart to determine interrater variability, intrarater variability, and feasibility of the classification system was undertaken. Results: Interrater Kappa values of 0.80 (95% confidence interval [CI]: 0.67–0.92; p<0.0001) for Round 1 and 0.88 (95% CI: 0.76–1.00; p<0.0001) for Round 2 of the review were observed. Percentages of agreement were 85% for Round 1 and 91% for Round 2. The intrarater Kappa value for the two reviewers were 0.76 (95% CI: 0.63–0.88; p<0.0001) and 0.65 (95% CI: 0.53–0.76; p<0.0001) and the average was 0.71 suggesting substantial overall agreement. The percentages of agreement were 82% and 72%, respectively. Conclusions: This classification system is straightforward to use and produces both reproducible and consistent results based on interrater and intrarater Kappa values with at least substantial agreement in all groups. Further work is ongoing to investigate whether individual grades within this classification system provide prognostic information and could guide clinical management. - Highlights: • This classification system is based on MRI parameters shown to have prognostic relevance. • It is simple to use, reproducible and clinically relevant which will enhance clinical practice. • Once clinicians are familiar with the classification inter & intrarater reliability will improve.

Classification of treatment-related mortality in children with cancer

DEFF Research Database (Denmark)

Alexander, Sarah; Pole, Jason D; Gibson, Paul

2015-01-01

Treatment-related mortality is an important outcome in paediatric cancer clinical trials. An international group of experts in supportive care in paediatric cancer developed a consensus-based definition of treatment-related mortality and a cause-of-death attribution system. The reliability and va...

Classification of human cancers based on DNA copy number amplification modeling

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Knuutila Sakari

2008-05-01

Full Text Available Abstract Background DNA amplifications alter gene dosage in cancer genomes by multiplying the gene copy number. Amplifications are quintessential in a considerable number of advanced cancers of various anatomical locations. The aims of this study were to classify human cancers based on their amplification patterns, explore the biological and clinical fundamentals behind their amplification-pattern based classification, and understand the characteristics in human genomic architecture that associate with amplification mechanisms. Methods We applied a machine learning approach to model DNA copy number amplifications using a data set of binary amplification records at chromosome sub-band resolution from 4400 cases that represent 82 cancer types. Amplification data was fused with background data: clinical, histological and biological classifications, and cytogenetic annotations. Statistical hypothesis testing was used to mine associations between the data sets. Results Probabilistic clustering of each chromosome identified 111 amplification models and divided the cancer cases into clusters. The distribution of classification terms in the amplification-model based clustering of cancer cases revealed cancer classes that were associated with specific DNA copy number amplification models. Amplification patterns – finite or bounded descriptions of the ranges of the amplifications in the chromosome – were extracted from the clustered data and expressed according to the original cytogenetic nomenclature. This was achieved by maximal frequent itemset mining using the cluster-specific data sets. The boundaries of amplification patterns were shown to be enriched with fragile sites, telomeres, centromeres, and light chromosome bands. Conclusions Our results demonstrate that amplifications are non-random chromosomal changes and specifically selected in tumor tissue microenvironment. Furthermore, statistical evidence showed that specific chromosomal features

Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features

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Eman Magdy

2015-01-01

Full Text Available Computer-aided diagnostic (CAD systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. Using 70 different patients’ lung CT dataset, Wiener filtering on the original CT images is applied firstly as a preprocessing step. Secondly, we combine histogram analysis with thresholding and morphological operations to segment the lung regions and extract each lung separately. Amplitude-Modulation Frequency-Modulation (AM-FM method thirdly, has been used to extract features for ROIs. Then, the significant AM-FM features have been selected using Partial Least Squares Regression (PLSR for classification step. Finally, K-nearest neighbour (KNN, support vector machine (SVM, naïve Bayes, and linear classifiers have been used with the selected AM-FM features. The performance of each classifier in terms of accuracy, sensitivity, and specificity is evaluated. The results indicate that our proposed CAD system succeeded to differentiate between normal and cancer lungs and achieved 95% accuracy in case of the linear classifier.

Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering.

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Nahid, Abdullah-Al; Mehrabi, Mohamad Ali; Kong, Yinan

2018-01-01

Breast Cancer is a serious threat and one of the largest causes of death of women throughout the world. The identification of cancer largely depends on digital biomedical photography analysis such as histopathological images by doctors and physicians. Analyzing histopathological images is a nontrivial task, and decisions from investigation of these kinds of images always require specialised knowledge. However, Computer Aided Diagnosis (CAD) techniques can help the doctor make more reliable decisions. The state-of-the-art Deep Neural Network (DNN) has been recently introduced for biomedical image analysis. Normally each image contains structural and statistical information. This paper classifies a set of biomedical breast cancer images (BreakHis dataset) using novel DNN techniques guided by structural and statistical information derived from the images. Specifically a Convolutional Neural Network (CNN), a Long-Short-Term-Memory (LSTM), and a combination of CNN and LSTM are proposed for breast cancer image classification. Softmax and Support Vector Machine (SVM) layers have been used for the decision-making stage after extracting features utilising the proposed novel DNN models. In this experiment the best Accuracy value of 91.00% is achieved on the 200x dataset, the best Precision value 96.00% is achieved on the 40x dataset, and the best F -Measure value is achieved on both the 40x and 100x datasets.

Intraobserver and interobserver reliability of recategorized Neer classification in differentiating 2-part surgical neck fractures from multi-fragmented proximal humeral fractures in 116 patients

DEFF Research Database (Denmark)

Sumrein, Bakir O; Mattila, Ville M; Lepola, Vesa

2018-01-01

BACKGROUND: Optimal fracture classification should be simple and reproducible and should guide treatment. For proximal humeral fractures, the Neer classification is commonly used. However, intraobserver and interobserver reliability of the Neer classification has been shown to be poor. In clinical...... for interobserver reliability showed substantial correlation (0.61-0.73) and was as follows: 0.73 for radiographs alone, 0.61 for CT scans alone, and 0.72 for radiographs and CT scans viewed together. After 24 weeks, the process was repeated and intraobserver reliability was calculated.The κ coefficient...... for intraobserver reliability showed substantial correlation (0.62-0.75) and was as follows: 0.62 for radiographs alone, 0.64 for CT scans alone, and 0.75 for radiographs and CT scans viewed together. CONCLUSION: Clinicians were able to differentiate 2-part surgical neck fractures from multi-fragmented fractures...

[New molecular classification of colorectal cancer, pancreatic cancer and stomach cancer: Towards "à la carte" treatment?].

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Dreyer, Chantal; Afchain, Pauline; Trouilloud, Isabelle; André, Thierry

2016-01-01

This review reports 3 of recently published molecular classifications of the 3 main gastro-intestinal cancers: gastric, pancreatic and colorectal adenocarcinoma. In colorectal adenocarcinoma, 6 independent classifications were combined to finally hold 4 molecular sub-groups, Consensus Molecular Subtypes (CMS 1-4), linked to various clinical, molecular and survival data. CMS1 (14% MSI with immune activation); CMS2 (37%: canonical with epithelial differentiation and activation of the WNT/MYC pathway); CMS3 (13% metabolic with epithelial differentiation and RAS mutation); CMS4 (23%: mesenchymal with activation of TGFβ pathway and angiogenesis with stromal invasion). In gastric adenocarcinoma, 4 groups were established: subtype "EBV" (9%, high frequency of PIK3CA mutations, hypermetylation and amplification of JAK2, PD-L1 and PD-L2), subtype "MSI" (22%, high rate of mutation), subtype "genomically stable tumor" (20%, diffuse histology type and mutations of RAS and genes encoding integrins and adhesion proteins including CDH1) and subtype "tumors with chromosomal instability" (50%, intestinal type, aneuploidy and receptor tyrosine kinase amplification). In pancreatic adenocarcinomas, a classification in four sub-groups has been proposed, stable subtype (20%, aneuploidy), locally rearranged subtype (30%, focal event on one or two chromosoms), scattered subtype (36%,200 structural variation events, defects in DNA maintenance). Although currently away from the care of patients, these classifications open the way to "à la carte" treatment depending on molecular biology. Copyright © 2016 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

RRHGE: A Novel Approach to Classify the Estrogen Receptor Based Breast Cancer Subtypes

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Ashish Saini

2014-01-01

Full Text Available Background. Breast cancer is the most common type of cancer among females with a high mortality rate. It is essential to classify the estrogen receptor based breast cancer subtypes into correct subclasses, so that the right treatments can be applied to lower the mortality rate. Using gene signatures derived from gene interaction networks to classify breast cancers has proven to be more reproducible and can achieve higher classification performance. However, the interactions in the gene interaction network usually contain many false-positive interactions that do not have any biological meanings. Therefore, it is a challenge to incorporate the reliability assessment of interactions when deriving gene signatures from gene interaction networks. How to effectively extract gene signatures from available resources is critical to the success of cancer classification. Methods. We propose a novel method to measure and extract the reliable (biologically true or valid interactions from gene interaction networks and incorporate the extracted reliable gene interactions into our proposed RRHGE algorithm to identify significant gene signatures from microarray gene expression data for classifying ER+ and ER− breast cancer samples. Results. The evaluation on real breast cancer samples showed that our RRHGE algorithm achieved higher classification accuracy than the existing approaches.

Gross Motor Function Classification System Expanded & Revised (GMFCS E & R: reliability between therapists and parents in Brazil

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Daniela B. R. Silva

2013-10-01

Full Text Available BACKGROUND: Several studies have demonstrated the importance of using the Gross Motor Function Classification System (GMFCS to classify gross motor function in children with cerebral palsy, but the reliability of the expanded and revised version has not been examined in Brazil (GMFCS E & R. OBJECTIVE:: To determine the intra- and inter-rater reliability of the Portuguese-Brazil version of the GMFCS E & R applied by therapists and compare to classification provided by parents of children with cerebral palsy. METHOD: Data were obtained from 90 children with cerebral palsy, aged 4 to 18 years old, attending the neurology or rehabilitation service of a Brazilian hospital. Therapists classified the children's motor function using the GMFCS E & R and parents used the Brazilian Portuguese version of the GMFCS Family Report Questionnaire. Intra- and inter-rater reliability was obtained through percentage agreement and Cohen's unweighted Kappa statistics (k. The Chi-square test was used to identify significant differences in the classification of parents and therapists. RESULTS: Almost perfect agreement was reached between the therapists [K=0.90 (95% confidence interval 0.83-0.97] and intra-raters (therapists with K=1.00 [95% confidence interval (1.00-1.00], p<0.001. Agreement between therapists and parents was substantial (k=0.716, confidence interval 0.596-0.836, though parents classify gross motor impairment more severely than therapists (p=0.04. CONCLUSIONS: The Portuguese version of the GMFCS E & R is reliable for use by parents and therapists. Parents tend to classify their children's limitations more severely, because they know their performance in different environments.

A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

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Idil Isikli Esener

2017-01-01

Full Text Available A new and effective feature ensemble with a multistage classification is proposed to be implemented in a computer-aided diagnosis (CAD system for breast cancer diagnosis. A publicly available mammogram image dataset collected during the Image Retrieval in Medical Applications (IRMA project is utilized to verify the suggested feature ensemble and multistage classification. In achieving the CAD system, feature extraction is performed on the mammogram region of interest (ROI images which are preprocessed by applying a histogram equalization followed by a nonlocal means filtering. The proposed feature ensemble is formed by concatenating the local configuration pattern-based, statistical, and frequency domain features. The classification process of these features is implemented in three cases: a one-stage study, a two-stage study, and a three-stage study. Eight well-known classifiers are used in all cases of this multistage classification scheme. Additionally, the results of the classifiers that provide the top three performances are combined via a majority voting technique to improve the recognition accuracy on both two- and three-stage studies. A maximum of 85.47%, 88.79%, and 93.52% classification accuracies are attained by the one-, two-, and three-stage studies, respectively. The proposed multistage classification scheme is more effective than the single-stage classification for breast cancer diagnosis.

Reliability of a novel, semi-quantitative scale for classification of structural brain magnetic resonance imaging in children with cerebral palsy.

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Fiori, Simona; Cioni, Giovanni; Klingels, Katrjin; Ortibus, Els; Van Gestel, Leen; Rose, Stephen; Boyd, Roslyn N; Feys, Hilde; Guzzetta, Andrea

2014-09-01

To describe the development of a novel rating scale for classification of brain structural magnetic resonance imaging (MRI) in children with cerebral palsy (CP) and to assess its interrater and intrarater reliability. The scale consists of three sections. Section 1 contains descriptive information about the patient and MRI. Section 2 contains the graphical template of brain hemispheres onto which the lesion is transposed. Section 3 contains the scoring system for the quantitative analysis of the lesion characteristics, grouped into different global scores and subscores that assess separately side, regions, and depth. A larger interrater and intrarater reliability study was performed in 34 children with CP (22 males, 12 females; mean age at scan of 9 y 5 mo [SD 3 y 3 mo], range 4 y-16 y 11 mo; Gross Motor Function Classification System level I, [n=22], II [n=10], and level III [n=2]). Very high interrater and intrarater reliability of the total score was found with indices above 0.87. Reliability coefficients of the lobar and hemispheric subscores ranged between 0.53 and 0.95. Global scores for hemispheres, basal ganglia, brain stem, and corpus callosum showed reliability coefficients above 0.65. This study presents the first visual, semi-quantitative scale for classification of brain structural MRI in children with CP. The high degree of reliability of the scale supports its potential application for investigating the relationship between brain structure and function and examining treatment response according to brain lesion severity in children with CP. © 2014 Mac Keith Press.

Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data

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George Rumbe

2010-12-01

Full Text Available Accurate diagnostic detection of the cancerous cells in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Bayesian classifier and other Artificial neural network classifiers (Backpropagation, linear programming, Learning vector quantization, and K nearest neighborhood on the Wisconsin breast cancer classification problem.

hemaClass.org: Online One-By-One Microarray Normalization and Classification of Hematological Cancers for Precision Medicine.

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Falgreen, Steffen; Ellern Bilgrau, Anders; Brøndum, Rasmus Froberg; Hjort Jakobsen, Lasse; Have, Jonas; Lindblad Nielsen, Kasper; El-Galaly, Tarec Christoffer; Bødker, Julie Støve; Schmitz, Alexander; H Young, Ken; Johnsen, Hans Erik; Dybkær, Karen; Bøgsted, Martin

2016-01-01

Dozens of omics based cancer classification systems have been introduced with prognostic, diagnostic, and predictive capabilities. However, they often employ complex algorithms and are only applicable on whole cohorts of patients, making them difficult to apply in a personalized clinical setting. This prompted us to create hemaClass.org, an online web application providing an easy interface to one-by-one RMA normalization of microarrays and subsequent risk classifications of diffuse large B-cell lymphoma (DLBCL) into cell-of-origin and chemotherapeutic sensitivity classes. Classification results for one-by-one array pre-processing with and without a laboratory specific RMA reference dataset were compared to cohort based classifiers in 4 publicly available datasets. Classifications showed high agreement between one-by-one and whole cohort pre-processsed data when a laboratory specific reference set was supplied. The website is essentially the R-package hemaClass accompanied by a Shiny web application. The well-documented package can be used to run the website locally or to use the developed methods programmatically. The website and R-package is relevant for biological and clinical lymphoma researchers using affymetrix U-133 Plus 2 arrays, as it provides reliable and swift methods for calculation of disease subclasses. The proposed one-by-one pre-processing method is relevant for all researchers using microarrays.

Application of machine learning on brain cancer multiclass classification

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Panca, V.; Rustam, Z.

2017-07-01

Classification of brain cancer is a problem of multiclass classification. One approach to solve this problem is by first transforming it into several binary problems. The microarray gene expression dataset has the two main characteristics of medical data: extremely many features (genes) and only a few number of samples. The application of machine learning on microarray gene expression dataset mainly consists of two steps: feature selection and classification. In this paper, the features are selected using a method based on support vector machine recursive feature elimination (SVM-RFE) principle which is improved to solve multiclass classification, called multiple multiclass SVM-RFE. Instead of using only the selected features on a single classifier, this method combines the result of multiple classifiers. The features are divided into subsets and SVM-RFE is used on each subset. Then, the selected features on each subset are put on separate classifiers. This method enhances the feature selection ability of each single SVM-RFE. Twin support vector machine (TWSVM) is used as the method of the classifier to reduce computational complexity. While ordinary SVM finds single optimum hyperplane, the main objective Twin SVM is to find two non-parallel optimum hyperplanes. The experiment on the brain cancer microarray gene expression dataset shows this method could classify 71,4% of the overall test data correctly, using 100 and 1000 genes selected from multiple multiclass SVM-RFE feature selection method. Furthermore, the per class results show that this method could classify data of normal and MD class with 100% accuracy.

Bladder cancer: Analysis of the 2004 WHO classification in ...

African Journals Online (AJOL)

Objectives: Bladder cancer (BCA) is aworldwide disease and shows a wide range of geographical variation. The aim of this study is to analyze the prevalence of schistosomal and non-schistosomal associated BCA as well as compare our findings with the 2004 WHO consensus classification of urothelial neoplasms and ...

Fluorescently labeled bevacizumab in human breast cancer: defining the classification threshold

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Koch, Maximilian; de Jong, Johannes S.; Glatz, Jürgen; Symvoulidis, Panagiotis; Lamberts, Laetitia E.; Adams, Arthur L. L.; Kranendonk, Mariëtte E. G.; Terwisscha van Scheltinga, Anton G. T.; Aichler, Michaela; Jansen, Liesbeth; de Vries, Jakob; Lub-de Hooge, Marjolijn N.; Schröder, Carolien P.; Jorritsma-Smit, Annelies; Linssen, Matthijs D.; de Boer, Esther; van der Vegt, Bert; Nagengast, Wouter B.; Elias, Sjoerd G.; Oliveira, Sabrina; Witkamp, Arjen J.; Mali, Willem P. Th. M.; Van der Wall, Elsken; Garcia-Allende, P. Beatriz; van Diest, Paul J.; de Vries, Elisabeth G. E.; Walch, Axel; van Dam, Gooitzen M.; Ntziachristos, Vasilis

2017-07-01

In-vivo fluorescently labelled drug (bevacizumab) breast cancer specimen where obtained from patients. We propose a new structured method to determine the optimal classification threshold in targeted fluorescence intra-operative imaging.

Gene expression classification of colon cancer into molecular subtypes: characterization, validation, and prognostic value.

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Laetitia Marisa

Full Text Available Colon cancer (CC pathological staging fails to accurately predict recurrence, and to date, no gene expression signature has proven reliable for prognosis stratification in clinical practice, perhaps because CC is a heterogeneous disease. The aim of this study was to establish a comprehensive molecular classification of CC based on mRNA expression profile analyses.Fresh-frozen primary tumor samples from a large multicenter cohort of 750 patients with stage I to IV CC who underwent surgery between 1987 and 2007 in seven centers were characterized for common DNA alterations, including BRAF, KRAS, and TP53 mutations, CpG island methylator phenotype, mismatch repair status, and chromosomal instability status, and were screened with whole genome and transcriptome arrays. 566 samples fulfilled RNA quality requirements. Unsupervised consensus hierarchical clustering applied to gene expression data from a discovery subset of 443 CC samples identified six molecular subtypes. These subtypes were associated with distinct clinicopathological characteristics, molecular alterations, specific enrichments of supervised gene expression signatures (stem cell phenotype-like, normal-like, serrated CC phenotype-like, and deregulated signaling pathways. Based on their main biological characteristics, we distinguished a deficient mismatch repair subtype, a KRAS mutant subtype, a cancer stem cell subtype, and three chromosomal instability subtypes, including one associated with down-regulated immune pathways, one with up-regulation of the Wnt pathway, and one displaying a normal-like gene expression profile. The classification was validated in the remaining 123 samples plus an independent set of 1,058 CC samples, including eight public datasets. Furthermore, prognosis was analyzed in the subset of stage II-III CC samples. The subtypes C4 and C6, but not the subtypes C1, C2, C3, and C5, were independently associated with shorter relapse-free survival, even after

MO-DE-207B-03: Improved Cancer Classification Using Patient-Specific Biological Pathway Information Via Gene Expression Data

Energy Technology Data Exchange (ETDEWEB)

Young, M; Craft, D [Massachusetts General Hospital and Harvard Medical School, Boston, MA (United States)

2016-06-15

Purpose: To develop an efficient, pathway-based classification system using network biology statistics to assist in patient-specific response predictions to radiation and drug therapies across multiple cancer types. Methods: We developed PICS (Pathway Informed Classification System), a novel two-step cancer classification algorithm. In PICS, a matrix m of mRNA expression values for a patient cohort is collapsed into a matrix p of biological pathways. The entries of p, which we term pathway scores, are obtained from either principal component analysis (PCA), normal tissue centroid (NTC), or gene expression deviation (GED). The pathway score matrix is clustered using both k-means and hierarchical clustering, and a clustering is judged by how well it groups patients into distinct survival classes. The most effective pathway scoring/clustering combination, per clustering p-value, thus generates various ‘signatures’ for conventional and functional cancer classification. Results: PICS successfully regularized large dimension gene data, separated normal and cancerous tissues, and clustered a large patient cohort spanning six cancer types. Furthermore, PICS clustered patient cohorts into distinct, statistically-significant survival groups. For a suboptimally-debulked ovarian cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00127) showed significant improvement over that of a prior gene expression-classified study (p = .0179). For a pancreatic cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00141) showed significant improvement over that of a prior gene expression-classified study (p = .04). Pathway-based classification confirmed biomarkers for the pyrimidine, WNT-signaling, glycerophosphoglycerol, beta-alanine, and panthothenic acid pathways for ovarian cancer. Despite its robust nature, PICS requires significantly less run time than current pathway scoring methods. Conclusion: This work validates the PICS method to improve

Apparent diffusion coefficient value of gastric cancer by diffusion-weighted imaging: Correlations with the histological differentiation and Lauren classification

International Nuclear Information System (INIS)

Liu, Song; Guan, Wenxian; Wang, Hao; Pan, Liang; Zhou, Zhuping; Yu, Haiping; Liu, Tian; Yang, Xiaofeng; He, Jian; Zhou, Zhengyang

2014-01-01

Highlights: • Gastric cancers’ ADC values were significantly lower than normal gastric wall. • Gastric adenocarcinomas with different differentiation had different ADC values. • Gastric adenocarcinomas’ ADC values correlated with histologic differentiations. • Gastric cancers’ ADC values correlated with Lauren classifications. • Mean ADC value was better than min ADC value in characterizing gastric cancers. - Abstract: Objective: The purpose of this study was to evaluate the correlations between histological differentiation and Lauren classification of gastric cancer and the apparent diffusion coefficient (ADC) value of diffusion weighted imaging (DWI). Materials and methods: Sixty-nine patients with gastric cancer lesions underwent preoperative magnetic resonance imaging (MRI) (3.0T) and surgical resection. DWI was obtained with a single-shot, echo-planar imaging sequence in the axial plane (b values: 0 and 1000 s/mm 2 ). Mean and minimum ADC values were obtained for each gastric cancer and normal gastric walls by two radiologists, who were blinded to the histological findings. Histological type, degree of differentiation and Lauren classification of each resected specimen were determined by one pathologist. Mean and minimum ADC values of gastric cancers with different histological types, degrees of differentiation and Lauren classifications were compared. Correlations between ADC values and histological differentiation and Lauren classification were analyzed. Results: The mean and minimum ADC values of gastric cancers, as a whole and separately, were significantly lower than those of normal gastric walls (all p values <0.001). There were significant differences in the mean and minimum ADC values among gastric cancers with different histological types, degrees of differentiation and Lauren classifications (p < 0.05). Mean and minimum ADC values correlated significantly (all p < 0.001) with histological differentiation (r = 0.564, 0.578) and Lauren

Classification of breast cancer patients using somatic mutation profiles and machine learning approaches.

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Vural, Suleyman; Wang, Xiaosheng; Guda, Chittibabu

2016-08-26

The high degree of heterogeneity observed in breast cancers makes it very difficult to classify the cancer patients into distinct clinical subgroups and consequently limits the ability to devise effective therapeutic strategies. Several classification strategies based on ER/PR/HER2 expression or the expression profiles of a panel of genes have helped, but such methods often produce misleading results due to their dynamic nature. In contrast, somatic DNA mutations are relatively stable and lead to initiation and progression of many sporadic cancers. Hence in this study, we explore the use of gene mutation profiles to classify, characterize and predict the subgroups of breast cancers. We analyzed the whole exome sequencing data from 358 ethnically similar breast cancer patients in The Cancer Genome Atlas (TCGA) project. Somatic and non-synonymous single nucleotide variants identified from each patient were assigned a quantitative score (C-score) that represents the extent of negative impact on the gene function. Using these scores with non-negative matrix factorization method, we clustered the patients into three subgroups. By comparing the clinical stage of patients, we identified an early-stage-enriched and a late-stage-enriched subgroup. Comparison of the mutation scores of early and late-stage-enriched subgroups identified 358 genes that carry significantly higher mutations rates in the late stage subgroup. Functional characterization of these genes revealed important functional gene families that carry a heavy mutational load in the late state rich subgroup of patients. Finally, using the identified subgroups, we also developed a supervised classification model to predict the stage of the patients. This study demonstrates that gene mutation profiles can be effectively used with unsupervised machine-learning methods to identify clinically distinguishable breast cancer subgroups. The classification model developed in this method could provide a reasonable

Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes

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Eils Roland

2005-11-01

Full Text Available Abstract Background The extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer studies. Although similar questions for the same type of cancer are addressed in these different studies, a comparative analysis of their results is hampered by the use of heterogeneous microarray platforms and analysis methods. Results In contrast to a meta-analysis approach where results of different studies are combined on an interpretative level, we investigate here how to directly integrate raw microarray data from different studies for the purpose of supervised classification analysis. We use median rank scores and quantile discretization to derive numerically comparable measures of gene expression from different platforms. These transformed data are then used for training of classifiers based on support vector machines. We apply this approach to six publicly available cancer microarray gene expression data sets, which consist of three pairs of studies, each examining the same type of cancer, i.e. breast cancer, prostate cancer or acute myeloid leukemia. For each pair, one study was performed by means of cDNA microarrays and the other by means of oligonucleotide microarrays. In each pair, high classification accuracies (> 85% were achieved with training and testing on data instances randomly chosen from both data sets in a cross-validation analysis. To exemplify the potential of this cross-platform classification analysis, we use two leukemia microarray data sets to show that important genes with regard to the biology of leukemia are selected in an integrated analysis, which are missed in either single-set analysis. Conclusion Cross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by different laboratories and

Actionable gene-based classification toward precision medicine in gastric cancer

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Hiroshi Ichikawa

2017-10-01

Full Text Available Abstract Background Intertumoral heterogeneity represents a significant hurdle to identifying optimized targeted therapies in gastric cancer (GC. To realize precision medicine for GC patients, an actionable gene alteration-based molecular classification that directly associates GCs with targeted therapies is needed. Methods A total of 207 Japanese patients with GC were included in this study. Formalin-fixed, paraffin-embedded (FFPE tumor tissues were obtained from surgical or biopsy specimens and were subjected to DNA extraction. We generated comprehensive genomic profiling data using a 435-gene panel including 69 actionable genes paired with US Food and Drug Administration-approved targeted therapies, and the evaluation of Epstein-Barr virus (EBV infection and microsatellite instability (MSI status. Results Comprehensive genomic sequencing detected at least one alteration of 435 cancer-related genes in 194 GCs (93.7% and of 69 actionable genes in 141 GCs (68.1%. We classified the 207 GCs into four The Cancer Genome Atlas (TCGA subtypes using the genomic profiling data; EBV (N = 9, MSI (N = 17, chromosomal instability (N = 119, and genomically stable subtype (N = 62. Actionable gene alterations were not specific and were widely observed throughout all TCGA subtypes. To discover a novel classification which more precisely selects candidates for targeted therapies, 207 GCs were classified using hypermutated phenotype and the mutation profile of 69 actionable genes. We identified a hypermutated group (N = 32, while the others (N = 175 were sub-divided into six clusters including five with actionable gene alterations: ERBB2 (N = 25, CDKN2A, and CDKN2B (N = 10, KRAS (N = 10, BRCA2 (N = 9, and ATM cluster (N = 12. The clinical utility of this classification was demonstrated by a case of unresectable GC with a remarkable response to anti-HER2 therapy in the ERBB2 cluster. Conclusions This actionable gene

Reliability and Validity of the Korean Version of the Cancer Stigma Scale.

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So, Hyang Sook; Chae, Myeong Jeong; Kim, Hye Young

2017-02-01

In this study the reliability and validity of the Korean version of the Cancer Stigma Scale (KCSS) was evaluated. The KCSS was formed through translation and modification of Cataldo Lung Cancer Stigma Scale. The KCSS, Psychological Symptom Inventory (PSI), and European Organization for Research and Treatment of Cancer Quality of Life Questionnaire - Core 30 (EORTC QLQ-C30) were administered to 247 men and women diagnosed with one of the five major cancers. Construct validity, item convergent and discriminant validity, concurrent validity, known-group validity, and internal consistency reliability of the KCSS were evaluated. Exploratory factor analysis supported the construct validity with a six-factor solution; that explained 65.7% of the total variance. The six-factor model was validated by confirmatory factor analysis (Q (χ²/df)= 2.28, GFI=.84, AGFI=.81, NFI=.80, TLI=.86, RMR=.03, and RMSEA=.07). Concurrent validity was demonstrated with the QLQ-C30 (global: r=-.44; functional: r=-.19; symptom: r=.42). The KCSS had known-group validity. Cronbach's alpha coefficient for the 24 items was .89. The results of this study suggest that the 24-item KCSS has relatively acceptable reliability and validity and can be used in clinical research to assess cancer stigma and its impacts on health-related quality of life in Korean cancer patients. © 2017 Korean Society of Nursing Science

Recursive Partitioning Analysis for New Classification of Patients With Esophageal Cancer Treated by Chemoradiotherapy

International Nuclear Information System (INIS)

Nomura, Motoo; Shitara, Kohei; Kodaira, Takeshi; Kondoh, Chihiro; Takahari, Daisuke; Ura, Takashi; Kojima, Hiroyuki; Kamata, Minoru; Muro, Kei; Sawada, Satoshi

2012-01-01

Background: The 7th edition of the American Joint Committee on Cancer staging system does not include lymph node size in the guidelines for staging patients with esophageal cancer. The objectives of this study were to determine the prognostic impact of the maximum metastatic lymph node diameter (ND) on survival and to develop and validate a new staging system for patients with esophageal squamous cell cancer who were treated with definitive chemoradiotherapy (CRT). Methods: Information on 402 patients with esophageal cancer undergoing CRT at two institutions was reviewed. Univariate and multivariate analyses of data from one institution were used to assess the impact of clinical factors on survival, and recursive partitioning analysis was performed to develop the new staging classification. To assess its clinical utility, the new classification was validated using data from the second institution. Results: By multivariate analysis, gender, T, N, and ND stages were independently and significantly associated with survival (p < 0.05). The resulting new staging classification was based on the T and ND. The four new stages led to good separation of survival curves in both the developmental and validation datasets (p < 0.05). Conclusions: Our results showed that lymph node size is a strong independent prognostic factor and that the new staging system, which incorporated lymph node size, provided good prognostic power, and discriminated effectively for patients with esophageal cancer undergoing CRT.

Reliability and Validity of the Korean Cancer Pain Assessment Tool (KCPAT)

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Kim, Jeong A; Lee, Juneyoung; Park, Jeanno; Lee, Myung Ah; Yeom, Chang Hwan; Jang, Se Kwon; Yoon, Duck Mi; Kim, Jun Suk

2005-01-01

The Korean Cancer Pain Assessment Tool (KCPAT), which was developed in 2003, consists of questions concerning the location of pain, the nature of pain, the present pain intensity, the symptoms associated with the pain, and psychosocial/spiritual pain assessments. This study was carried out to evaluate the reliability and validity of the KCPAT. A stratified, proportional-quota, clustered, systematic sampling procedure was used. The study population (903 cancer patients) was 1% of the target population (90,252 cancer patients). A total of 314 (34.8%) questionnaires were collected. The results showed that the average pain score (5 point on Likert scale) according to the cancer type and the at-present average pain score (VAS, 0-10) were correlated (r=0.56, p<0.0001), and showed moderate agreement (kappa=0.364). The mean satisfaction score was 3.8 (1-5). The average time to complete the questionnaire was 8.9 min. In conclusion, the KCPAT is a reliable and valid instrument for assessing cancer pain in Koreans. PMID:16224166

CrossLink: a novel method for cross-condition classification of cancer subtypes.

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Ma, Chifeng; Sastry, Konduru S; Flore, Mario; Gehani, Salah; Al-Bozom, Issam; Feng, Yusheng; Serpedin, Erchin; Chouchane, Lotfi; Chen, Yidong; Huang, Yufei

2016-08-22

We considered the prediction of cancer classes (e.g. subtypes) using patient gene expression profiles that contain both systematic and condition-specific biases when compared with the training reference dataset. The conventional normalization-based approaches cannot guarantee that the gene signatures in the reference and prediction datasets always have the same distribution for all different conditions as the class-specific gene signatures change with the condition. Therefore, the trained classifier would work well under one condition but not under another. To address the problem of current normalization approaches, we propose a novel algorithm called CrossLink (CL). CL recognizes that there is no universal, condition-independent normalization mapping of signatures. In contrast, it exploits the fact that the signature is unique to its associated class under any condition and thus employs an unsupervised clustering algorithm to discover this unique signature. We assessed the performance of CL for cross-condition predictions of PAM50 subtypes of breast cancer by using a simulated dataset modeled after TCGA BRCA tumor samples with a cross-validation scheme, and datasets with known and unknown PAM50 classification. CL achieved prediction accuracy >73 %, highest among other methods we evaluated. We also applied the algorithm to a set of breast cancer tumors derived from Arabic population to assign a PAM50 classification to each tumor based on their gene expression profiles. A novel algorithm CrossLink for cross-condition prediction of cancer classes was proposed. In all test datasets, CL showed robust and consistent improvement in prediction performance over other state-of-the-art normalization and classification algorithms.

Classification of breast cancer histology images using Convolutional Neural Networks.

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Teresa Araújo

Full Text Available Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.

Classifier Fusion With Contextual Reliability Evaluation.

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Liu, Zhunga; Pan, Quan; Dezert, Jean; Han, Jun-Wei; He, You

2018-05-01

Classifier fusion is an efficient strategy to improve the classification performance for the complex pattern recognition problem. In practice, the multiple classifiers to combine can have different reliabilities and the proper reliability evaluation plays an important role in the fusion process for getting the best classification performance. We propose a new method for classifier fusion with contextual reliability evaluation (CF-CRE) based on inner reliability and relative reliability concepts. The inner reliability, represented by a matrix, characterizes the probability of the object belonging to one class when it is classified to another class. The elements of this matrix are estimated from the -nearest neighbors of the object. A cautious discounting rule is developed under belief functions framework to revise the classification result according to the inner reliability. The relative reliability is evaluated based on a new incompatibility measure which allows to reduce the level of conflict between the classifiers by applying the classical evidence discounting rule to each classifier before their combination. The inner reliability and relative reliability capture different aspects of the classification reliability. The discounted classification results are combined with Dempster-Shafer's rule for the final class decision making support. The performance of CF-CRE have been evaluated and compared with those of main classical fusion methods using real data sets. The experimental results show that CF-CRE can produce substantially higher accuracy than other fusion methods in general. Moreover, CF-CRE is robust to the changes of the number of nearest neighbors chosen for estimating the reliability matrix, which is appealing for the applications.

Long-term Prostate-specific Antigen Velocity in Improved Classification of Prostate Cancer Risk and Mortality

DEFF Research Database (Denmark)

Ørsted, David Dynnes; Bojesen, Stig E; Kamstrup, Pia R

2013-01-01

BACKGROUND: It remains unclear whether adding long-term prostate-specific antigen velocity (PSAV) to baseline PSA values improves classification of prostate cancer (PCa) risk and mortality in the general population. OBJECTIVE: To determine whether long-term PSAV improves classification of PCa risk...

BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data.

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Guo, Yang; Liu, Shuhui; Li, Zhanhuai; Shang, Xuequn

2018-04-11

The classification of cancer subtypes is of great importance to cancer disease diagnosis and therapy. Many supervised learning approaches have been applied to cancer subtype classification in the past few years, especially of deep learning based approaches. Recently, the deep forest model has been proposed as an alternative of deep neural networks to learn hyper-representations by using cascade ensemble decision trees. It has been proved that the deep forest model has competitive or even better performance than deep neural networks in some extent. However, the standard deep forest model may face overfitting and ensemble diversity challenges when dealing with small sample size and high-dimensional biology data. In this paper, we propose a deep learning model, so-called BCDForest, to address cancer subtype classification on small-scale biology datasets, which can be viewed as a modification of the standard deep forest model. The BCDForest distinguishes from the standard deep forest model with the following two main contributions: First, a named multi-class-grained scanning method is proposed to train multiple binary classifiers to encourage diversity of ensemble. Meanwhile, the fitting quality of each classifier is considered in representation learning. Second, we propose a boosting strategy to emphasize more important features in cascade forests, thus to propagate the benefits of discriminative features among cascade layers to improve the classification performance. Systematic comparison experiments on both microarray and RNA-Seq gene expression datasets demonstrate that our method consistently outperforms the state-of-the-art methods in application of cancer subtype classification. The multi-class-grained scanning and boosting strategy in our model provide an effective solution to ease the overfitting challenge and improve the robustness of deep forest model working on small-scale data. Our model provides a useful approach to the classification of cancer subtypes

Cross-Disciplinary Analysis of Lymph Node Classification in Lung Cancer on CT Scanning.

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El-Sherief, Ahmed H; Lau, Charles T; Obuchowski, Nancy A; Mehta, Atul C; Rice, Thomas W; Blackstone, Eugene H

2017-04-01

Accurate and consistent regional lymph node classification is an important element in the staging and multidisciplinary management of lung cancer. Regional lymph node definition sets-lymph node maps-have been created to standardize regional lymph node classification. In 2009, the International Association for the Study of Lung Cancer (IASLC) introduced a lymph node map to supersede all preexisting lymph node maps. Our aim was to study if and how lung cancer specialists apply the IASLC lymph node map when classifying thoracic lymph nodes encountered on CT scans during lung cancer staging. From April 2013 through July 2013, invitations were distributed to all members of the Fleischner Society, Society of Thoracic Radiology, General Thoracic Surgical Club, and the American Association of Bronchology and Interventional Pulmonology to participate in an anonymous online image-based and text-based 20-question survey regarding lymph node classification for lung cancer staging on CT imaging. Three hundred thirty-seven people responded (approximately 25% participation). Respondents consisted of self-reported thoracic radiologists (n = 158), thoracic surgeons (n = 102), and pulmonologists who perform endobronchial ultrasonography (n = 77). Half of the respondents (50%; 95% CI, 44%-55%) reported using the IASLC lymph node map in daily practice, with no significant differences between subspecialties. A disparity was observed between the IASLC definition sets and their interpretation and application on CT scans, in particular for lymph nodes near the thoracic inlet, anterior to the trachea, anterior to the tracheal bifurcation, near the ligamentum arteriosum, between the bronchus intermedius and esophagus, in the internal mammary space, and adjacent to the heart. Use of older lymph node maps and inconsistencies in interpretation and application of definitions in the IASLC lymph node map may potentially lead to misclassification of stage and suboptimal management of lung

On the International Agency for Research on Cancer classification of glyphosate as a probable human carcinogen.

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Tarone, Robert E

2018-01-01

The recent classification by International Agency for Research on Cancer (IARC) of the herbicide glyphosate as a probable human carcinogen has generated considerable discussion. The classification is at variance with evaluations of the carcinogenic potential of glyphosate by several national and international regulatory bodies. The basis for the IARC classification is examined under the assumptions that the IARC criteria are reasonable and that the body of scientific studies determined by IARC staff to be relevant to the evaluation of glyphosate by the Monograph Working Group is sufficiently complete. It is shown that the classification of glyphosate as a probable human carcinogen was the result of a flawed and incomplete summary of the experimental evidence evaluated by the Working Group. Rational and effective cancer prevention activities depend on scientifically sound and unbiased assessments of the carcinogenic potential of suspected agents. Implications of the erroneous classification of glyphosate with respect to the IARC Monograph Working Group deliberative process are discussed.

Molecular classification of gastric cancer: a new paradigm.

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Shah, Manish A; Khanin, Raya; Tang, Laura; Janjigian, Yelena Y; Klimstra, David S; Gerdes, Hans; Kelsen, David P

2011-05-01

Gastric cancer may be subdivided into 3 distinct subtypes--proximal, diffuse, and distal gastric cancer--based on histopathologic and anatomic criteria. Each subtype is associated with unique epidemiology. Our aim is to test the hypothesis that these distinct gastric cancer subtypes may also be distinguished by gene expression analysis. Patients with localized gastric adenocarcinoma being screened for a phase II preoperative clinical trial (National Cancer Institute, NCI #5917) underwent endoscopic biopsy for fresh tumor procurement. Four to 6 targeted biopsies of the primary tumor were obtained. Macrodissection was carried out to ensure more than 80% carcinoma in the sample. HG-U133A GeneChip (Affymetrix) was used for cDNA expression analysis, and all arrays were processed and analyzed using the Bioconductor R-package. Between November 2003 and January 2006, 57 patients were screened to identify 36 patients with localized gastric cancer who had adequate RNA for expression analysis. Using supervised analysis, we built a classifier to distinguish the 3 gastric cancer subtypes, successfully classifying each into tightly grouped clusters. Leave-one-out cross-validation error was 0.14, suggesting that more than 85% of samples were classified correctly. Gene set analysis with the false discovery rate set at 0.25 identified several pathways that were differentially regulated when comparing each gastric cancer subtype to adjacent normal stomach. Subtypes of gastric cancer that have epidemiologic and histologic distinctions are also distinguished by gene expression data. These preliminary data suggest a new classification of gastric cancer with implications for improving our understanding of disease biology and identification of unique molecular drivers for each gastric cancer subtype. ©2011 AACR.

Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

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Jia Uddin

2014-01-01

Full Text Available This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D texture features and a multiclass support vector machine (MCSVM. The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns, and extracts these texture features by generating the dominant neighborhood structure (DNS map. The principal component analysis (PCA is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA multiclass support vector machines (MCSVMs to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.

Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data

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List Markus

2014-06-01

Full Text Available Selecting the most promising treatment strategy for breast cancer crucially depends on determining the correct subtype. In recent years, gene expression profiling has been investigated as an alternative to histochemical methods. Since databases like TCGA provide easy and unrestricted access to gene expression data for hundreds of patients, the challenge is to extract a minimal optimal set of genes with good prognostic properties from a large bulk of genes making a moderate contribution to classification. Several studies have successfully applied machine learning algorithms to solve this so-called gene selection problem. However, more diverse data from other OMICS technologies are available, including methylation. We hypothesize that combining methylation and gene expression data could already lead to a largely improved classification model, since the resulting model will reflect differences not only on the transcriptomic, but also on an epigenetic level. We compared so-called random forest derived classification models based on gene expression and methylation data alone, to a model based on the combined features and to a model based on the gold standard PAM50. We obtained bootstrap errors of 10-20% and classification error of 1-50%, depending on breast cancer subtype and model. The gene expression model was clearly superior to the methylation model, which was also reflected in the combined model, which mainly selected features from gene expression data. However, the methylation model was able to identify unique features not considered as relevant by the gene expression model, which might provide deeper insights into breast cancer subtype differentiation on an epigenetic level.

Reliability and cross-cultural validation of the Turkish version of Manual Ability Classification System (MACS) for children with cerebral palsy.

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Akpinar, Pinar; Tezel, Canan G; Eliasson, Ann-Christin; Icagasioglu, Afitap

2010-01-01

To determine the reliability and cross-cultural validation of the Turkish translation of the Manual Ability Classification System (MACS) for children with cerebral palsy (CP) and to investigate the relation to gross motor function and other comorbidities. After the forward and backward translation procedures, inter-rater and test-retest reliability was assessed between parents, physiotherapists and physicians using the intra-class correlation coefficient (ICC). Children (N = 118, 4 to 18 years, mean age 9 years 4 months; 68 boys, 50 girls) with various types of CP were classified. Additional data on the Gross Motor Function Classification System (GMFCS), intellectual delay, visual acuity, and epilepsy were collected. The inter-rater reliability was high; the ICC ranged from 0.89 to 0.96 among different professionals and parents. Between two persons of the same profession it ranged from 0.97 to 0.98. For the test-retest reliability it ranged from 0.91 to 0.98. Total agreement between the GMFCS and the MACS occurred in only 45% of the children. The level of the MACS was found to correlate with the accompanying comorbidities, namely intellectual delay and epilepsy. The Turkish version of the MACS is found to be valid and reliable, and is suggested to be appropriate for the assessment of manual ability within the Turkish population.

Detection and classification of Breast Cancer in Wavelet Sub-bands of Fractal Segmented Cancerous Zones.

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Shirazinodeh, Alireza; Noubari, Hossein Ahmadi; Rabbani, Hossein; Dehnavi, Alireza Mehri

2015-01-01

Recent studies on wavelet transform and fractal modeling applied on mammograms for the detection of cancerous tissues indicate that microcalcifications and masses can be utilized for the study of the morphology and diagnosis of cancerous cases. It is shown that the use of fractal modeling, as applied to a given image, can clearly discern cancerous zones from noncancerous areas. In this paper, for fractal modeling, the original image is first segmented into appropriate fractal boxes followed by identifying the fractal dimension of each windowed section using a computationally efficient two-dimensional box-counting algorithm. Furthermore, using appropriate wavelet sub-bands and image Reconstruction based on modified wavelet coefficients, it is shown that it is possible to arrive at enhanced features for detection of cancerous zones. In this paper, we have attempted to benefit from the advantages of both fractals and wavelets by introducing a new algorithm. By using a new algorithm named F1W2, the original image is first segmented into appropriate fractal boxes, and the fractal dimension of each windowed section is extracted. Following from that, by applying a maximum level threshold on fractal dimensions matrix, the best-segmented boxes are selected. In the next step, the segmented Cancerous zones which are candidates are then decomposed by utilizing standard orthogonal wavelet transform and db2 wavelet in three different resolution levels, and after nullifying wavelet coefficients of the image at the first scale and low frequency band of the third scale, the modified reconstructed image is successfully utilized for detection of breast cancer regions by applying an appropriate threshold. For detection of cancerous zones, our simulations indicate the accuracy of 90.9% for masses and 88.99% for microcalcifications detection results using the F1W2 method. For classification of detected mictocalcification into benign and malignant cases, eight features are identified and

The rise of a novel classification system for endometrial carcinoma; integration of molecular subclasses.

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McAlpine, Jessica; Leon-Castillo, Alicia; Bosse, Tjalling

2018-04-01

Endometrial cancer is a clinically heterogeneous disease and it is becoming increasingly clear that this heterogeneity may be a function of the diversity of the underlying molecular alterations. Recent large-scale genomic studies have revealed that endometrial cancer can be divided into at least four distinct molecular subtypes, with well-described underlying genomic aberrations. These subtypes can be reliably delineated and carry significant prognostic as well as predictive information; embracing and incorporating them into clinical practice is thus attractive. The road towards the integration of molecular features into current classification systems is not without obstacles. Collaborative studies engaging research teams from across the world are working to define pragmatic assays, improve risk stratification systems by combining molecular features and traditional clinicopathological parameters, and determine how molecular classification can be optimally utilized to direct patient care. Pathologists and clinicians caring for women with endometrial cancer need to engage with and understand the possibilities and limitations of this new approach, because integration of molecular classification of endometrial cancers is anticipated to become an essential part of gynaecological pathology practice. This review will describe the challenges in current systems of endometrial carcinoma classification, the evolution of new molecular technologies that define prognostically distinct molecular subtypes, and potential applications of molecular classification as a step towards precision medicine and refining care for individuals with the most common gynaecological cancer in the developed world. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Cross-cultural adaptation, reliability, and validity of the Turkish version of the Cancer Fatigue Scale in patients with breast cancer

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Şahin, Sedef; Huri, Meral; Aran, Orkun Tahir; Uyanık, Mine

2018-02-23

Background/aim: The Cancer Fatigue Scale (CFS) was developed to evaluate the severity of fatigue in patients with breast cancer. The aim of this study is to translate and culturally adapt a Turkish version and investigate the validity and reliability of the CFS in Turkish patients with fatigue symptoms. Materials and methods: Eighty participants completed the Turkish version of the CFS for breast cancer and the European Organization for Research and Treatment of Cancer Quality of Life Core Questionnaire ″Core 30″ (EORTC QLQ-C30). Test-retest reliability was evaluated by repeating the CFS with a 7-day interval. Results: The CFS demonstrated high test-retest reliability (ICC = 0.95) and good internal consistency (Cronbach′s alpha = 0.74) for all domains. The Kaiser-Meyer-Olkin measure of sampling adequacy was found to be 0.819, which is considered to be satisfactory (>0.5). Correlations between domains of CFS physical and EORTC physical (r: 0.77), CFS cognitive and EORTC cognitive (r: 0.70), and CFS physical and EORTC fatigue (r: 0.80) were found to be significant. Conclusion: The Turkish version of the CFS is a reliable and valid instrument to assess physical, effective, and cognitive dimensions of fatigue. The CFS may be used to evaluate the severity of fatigue in Turkish-speaking breast cancer patients.

Inter-rater reliability of a modified version of Delitto et al.’s classification-based system for low back pain : a pilot study

NARCIS (Netherlands)

Apeldoorn, Adri T.; van Helvoirt, Hans; Ostelo, Raymond W.; Meihuizen, Hanneke; Kamper, Steven J.; van Tulder, Maurits W.; de Vet, Henrica C W

2016-01-01

Study design:: Observational inter-rater reliability study. Objectives: To examine: (1) the inter-rater reliability of a modified version of Delitto et al.’s classification-based algorithm for patients with low back pain; (2) the influence of different levels of familiarity with the system; and (3)

Evaluation Methodology between Globalization and Localization Features Approaches for Skin Cancer Lesions Classification

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Ahmed, H. M.; Al-azawi, R. J.; Abdulhameed, A. A.

2018-05-01

Huge efforts have been put in the developing of diagnostic methods to skin cancer disease. In this paper, two different approaches have been addressed for detection the skin cancer in dermoscopy images. The first approach uses a global method that uses global features for classifying skin lesions, whereas the second approach uses a local method that uses local features for classifying skin lesions. The aim of this paper is selecting the best approach for skin lesion classification. The dataset has been used in this paper consist of 200 dermoscopy images from Pedro Hispano Hospital (PH2). The achieved results are; sensitivity about 96%, specificity about 100%, precision about 100%, and accuracy about 97% for globalization approach while, sensitivity about 100%, specificity about 100%, precision about 100%, and accuracy about 100% for Localization Approach, these results showed that the localization approach achieved acceptable accuracy and better than globalization approach for skin cancer lesions classification.

Does the use of the 2009 FIGO classification of endometrial cancer impact on indications of the sentinel node biopsy?

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Ballester Marcos

2010-08-01

Full Text Available Abstract Background Lymphadenectomy is debated in early stages endometrial cancer. Moreover, a new FIGO classification of endometrial cancer, merging stages IA and IB has been recently published. Therefore, the aims of the present study was to evaluate the relevance of the sentinel node (SN procedure in women with endometrial cancer and to discuss whether the use of the 2009 FIGO classification could modify the indications for SN procedure. Methods Eighty-five patients with endometrial cancer underwent the SN procedure followed by pelvic lymphadenectomy. SNs were detected with a dual or single labelling method in 74 and 11 cases, respectively. All SNs were analysed by both H&E staining and immunohistochemistry. Presumed stage before surgery was assessed for all patients based on MR imaging features using the 1988 FIGO classification and the 2009 FIGO classification. Results An SN was detected in 88.2% of cases (75/85 women. Among the fourteen patients with lymph node metastases one-half were detected by serial sectioning and immunohistochemical analysis. There were no false negative case. Using the 1988 FIGO classification and the 2009 FIGO classification, the correlation between preoperative MRI staging and final histology was moderate with Kappa = 0.24 and Kappa = 0.45, respectively. None of the patients with grade 1 endometrioid carcinoma on biopsy and IA 2009 FIGO stage on MR imaging exhibited positive SN. In patients with grade 2-3 endometrioid carcinoma and stage IA on MR imaging, the rate of positive SN reached 16.6% with an incidence of micrometastases of 50%. Conclusions The present study suggests that sentinel node biopsy is an adequate technique to evaluate lymph node status. The use of the 2009 FIGO classification increases the accuracy of MR imaging to stage patients with early stages of endometrial cancer and contributes to clarify the indication of SN biopsy according to tumour grade and histological type.

Does the use of the 2009 FIGO classification of endometrial cancer impact on indications of the sentinel node biopsy?

International Nuclear Information System (INIS)

Ballester, Marcos; Koskas, Martin; Coutant, Charles; Chéreau, Elisabeth; Seror, Jeremy; Rouzier, Roman; Daraï, Emile

2010-01-01

Lymphadenectomy is debated in early stages endometrial cancer. Moreover, a new FIGO classification of endometrial cancer, merging stages IA and IB has been recently published. Therefore, the aims of the present study was to evaluate the relevance of the sentinel node (SN) procedure in women with endometrial cancer and to discuss whether the use of the 2009 FIGO classification could modify the indications for SN procedure. Eighty-five patients with endometrial cancer underwent the SN procedure followed by pelvic lymphadenectomy. SNs were detected with a dual or single labelling method in 74 and 11 cases, respectively. All SNs were analysed by both H&E staining and immunohistochemistry. Presumed stage before surgery was assessed for all patients based on MR imaging features using the 1988 FIGO classification and the 2009 FIGO classification. An SN was detected in 88.2% of cases (75/85 women). Among the fourteen patients with lymph node metastases one-half were detected by serial sectioning and immunohistochemical analysis. There were no false negative case. Using the 1988 FIGO classification and the 2009 FIGO classification, the correlation between preoperative MRI staging and final histology was moderate with Kappa = 0.24 and Kappa = 0.45, respectively. None of the patients with grade 1 endometrioid carcinoma on biopsy and IA 2009 FIGO stage on MR imaging exhibited positive SN. In patients with grade 2-3 endometrioid carcinoma and stage IA on MR imaging, the rate of positive SN reached 16.6% with an incidence of micrometastases of 50%. The present study suggests that sentinel node biopsy is an adequate technique to evaluate lymph node status. The use of the 2009 FIGO classification increases the accuracy of MR imaging to stage patients with early stages of endometrial cancer and contributes to clarify the indication of SN biopsy according to tumour grade and histological type

An NRG Oncology/GOG study of molecular classification for risk prediction in endometrioid endometrial cancer.

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Cosgrove, Casey M; Tritchler, David L; Cohn, David E; Mutch, David G; Rush, Craig M; Lankes, Heather A; Creasman, William T; Miller, David S; Ramirez, Nilsa C; Geller, Melissa A; Powell, Matthew A; Backes, Floor J; Landrum, Lisa M; Timmers, Cynthia; Suarez, Adrian A; Zaino, Richard J; Pearl, Michael L; DiSilvestro, Paul A; Lele, Shashikant B; Goodfellow, Paul J

2018-01-01

The purpose of this study was to assess the prognostic significance of a simplified, clinically accessible classification system for endometrioid endometrial cancers combining Lynch syndrome screening and molecular risk stratification. Tumors from NRG/GOG GOG210 were evaluated for mismatch repair defects (MSI, MMR IHC, and MLH1 methylation), POLE mutations, and loss of heterozygosity. TP53 was evaluated in a subset of cases. Tumors were assigned to four molecular classes. Relationships between molecular classes and clinicopathologic variables were assessed using contingency tests and Cox proportional methods. Molecular classification was successful for 982 tumors. Based on the NCI consensus MSI panel assessing MSI and loss of heterozygosity combined with POLE testing, 49% of tumors were classified copy number stable (CNS), 39% MMR deficient, 8% copy number altered (CNA) and 4% POLE mutant. Cancer-specific mortality occurred in 5% of patients with CNS tumors; 2.6% with POLE tumors; 7.6% with MMR deficient tumors and 19% with CNA tumors. The CNA group had worse progression-free (HR 2.31, 95%CI 1.53-3.49) and cancer-specific survival (HR 3.95; 95%CI 2.10-7.44). The POLE group had improved outcomes, but the differences were not statistically significant. CNA class remained significant for cancer-specific survival (HR 2.11; 95%CI 1.04-4.26) in multivariable analysis. The CNA molecular class was associated with TP53 mutation and expression status. A simple molecular classification for endometrioid endometrial cancers that can be easily combined with Lynch syndrome screening provides important prognostic information. These findings support prospective clinical validation and further studies on the predictive value of a simplified molecular classification system. Copyright © 2017 Elsevier Inc. All rights reserved.

Classification across gene expression microarray studies

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Kuner Ruprecht

2009-12-01

Full Text Available Abstract Background The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive and histological grade (low/high of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM, predictive analysis of microarrays (PAM, random forest (RF and k-top scoring pairs (kTSP. Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing. Results For each individual study the generalization error was benchmarked via complete cross-validation and was found to be similar for all classification methods. The misclassification rates were substantially higher in classification across studies, when each single study was used as an independent test set while all remaining studies were combined for the training of the classifier. However, with increasing number of independent microarray studies used in the training, the overall classification performance improved. DV performed better than the average and showed slightly less variance. In

Reliability and Validity Study of a Tool to Measure Cancer Stigma: Patient Version.

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Yılmaz, Medine; Dişsiz, Gülçin; Demir, Filiz; Irız, Sibel; Alacacioglu, Ahmet

2017-01-01

The aim of this methodological study is to establish the validity and reliability of the Turkish version of "A Questionnaire for Measuring Attitudes toward Cancer (Cancer Stigma) - Patient version." The sample comprised oncology patients who had active cancer treatment. The construct validity was assessed using the confirmatory and exploratory factor analysis. The mean age of the participants was 54.9±12.3 years. In the confirmatory factor analysis, fit values were determined as comparative fit index = 0.93, goodness of fit index = 0.91, normed-fit index=0.91, and root mean square error of approximation RMSEA = 0.09 ( P Kaiser-Meyer-Olkin = 0.88, χ 2 = 1084.41, Df = 66, and Barletta's test P <0.000). The first factor was "impossibility of recovery and experience of social discrimination" and the second factor was "stereotypes of cancer patients." The two-factor structure accounted for 56.74% of the variance. The Cronbach's alpha value was determined as 0.88 for the two-factor scale. "A questionnaire for measuring attitudes toward cancer (cancer stigma) - Patient version" is a reliable and valid questionnaire to assess stigmatization of cancer in cancer patients.

From Molecular Classification to Targeted Therapeutics: The Changing Face of Systemic Therapy in Metastatic Gastroesophageal Cancer

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Adrian Murphy

2015-01-01

Full Text Available Histological classification of adenocarcinoma or squamous cell carcinoma for esophageal cancer or using the Lauren classification for intestinal and diffuse type gastric cancer has limited clinical utility in the management of advanced disease. Germline mutations in E-cadherin (CDH1 or mismatch repair genes (Lynch syndrome were identified many years ago but given their rarity, the identification of these molecular alterations does not substantially impact treatment in the advanced setting. Recent molecular profiling studies of upper GI tumors have added to our knowledge of the underlying biology but have not led to an alternative classification system which can guide clinician’s therapeutic decisions. Recently the Cancer Genome Atlas Research Network has proposed four subtypes of gastric cancer dividing tumors into those positive for Epstein-Barr virus, microsatellite unstable tumors, genomically stable tumors, and tumors with chromosomal instability. Unfortunately to date, many phase III clinical trials involving molecularly targeted agents have failed to meet their survival endpoints due to their use in unselected populations. Future clinical trials should utilize molecular profiling of individual tumors in order to determine the optimal use of targeted therapies in preselected patients.

In vivo subsite classification and diagnosis of oral cancers using Raman spectroscopy

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Aditi Sahu

2016-09-01

Full Text Available Oral cancers suffer from poor disease-free survival rates due to delayed diagnosis. Noninvasive, rapid, objective approaches as adjuncts to visual inspection can help in better management of oral cancers. Raman spectroscopy (RS has shown potential in identification of oral premalignant and malignant conditions and also in the detection of early cancer changes like cancer-field-effects (CFE at buccal mucosa subsite. Anatomic differences between different oral subsites have also been reported using RS. In this study, anatomical differences between subsites and their possible influence on healthy vs pathological classification were evaluated on 85 oral cancer and 72 healthy subjects. Spectra were acquired from buccal mucosa, lip and tongue in healthy, contralateral (internal healthy control, premalignant and cancer conditions using fiber-optic Raman spectrometer. Mean spectra indicate predominance of lipids in healthy buccal mucosa, contribution of both lipids and proteins in lip while major dominance of protein in tongue spectra. From healthy to tumor, changes in protein secondary-structure, DNA and heme-related features were observed. Principal component linear discriminant analysis (PC-LDA followed by leave-one-out-cross-validation (LOOCV was used for data analysis. Findings indicate buccal mucosa and tongue are distinct entities, while lip misclassifies with both these subsites. Additionally, the diagnostic algorithm for individual subsites gave improved classification efficiencies with respect to the pooled subsites model. However, as the pooled subsites model yielded 98% specificity and 100% sensitivity, this model may be more useful for preliminary screening applications. Large-scale validation studies are a pre-requisite before envisaging future clinical applications.

A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability

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Reinders Marcel JT

2009-11-01

Full Text Available Abstract Background Large discrepancies in signature composition and outcome concordance have been observed between different microarray breast cancer expression profiling studies. This is often ascribed to differences in array platform as well as biological variability. We conjecture that other reasons for the observed discrepancies are the measurement error associated with each feature and the choice of preprocessing method. Microarray data are known to be subject to technical variation and the confidence intervals around individual point estimates of expression levels can be wide. Furthermore, the estimated expression values also vary depending on the selected preprocessing scheme. In microarray breast cancer classification studies, however, these two forms of feature variability are almost always ignored and hence their exact role is unclear. Results We have performed a comprehensive sensitivity analysis of microarray breast cancer classification under the two types of feature variability mentioned above. We used data from six state of the art preprocessing methods, using a compendium consisting of eight diferent datasets, involving 1131 hybridizations, containing data from both one and two-color array technology. For a wide range of classifiers, we performed a joint study on performance, concordance and stability. In the stability analysis we explicitly tested classifiers for their noise tolerance by using perturbed expression profiles that are based on uncertainty information directly related to the preprocessing methods. Our results indicate that signature composition is strongly influenced by feature variability, even if the array platform and the stratification of patient samples are identical. In addition, we show that there is often a high level of discordance between individual class assignments for signatures constructed on data coming from different preprocessing schemes, even if the actual signature composition is identical

Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks.

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Wu, Miao; Yan, Chuanbo; Liu, Huiqiang; Liu, Qian

2018-06-29

Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images. © 2018 The Author(s).

Vabrasio is a reliable test to rule out endometrial cancer

DEFF Research Database (Denmark)

Andersen, Anita; Lauszus, Finn Friis

2015-01-01

Introduction: Endometrial cancer is the most common gynaecological cancer in Denmark, and its incidence peaks in the postmenopausal years. The aim of the present study was to evaluate the effectiveness of vacuum aspirator (vabrasio) for the detection of endometrial cancer in terms of sensitivity......, specificity and predictive value. Methods: A cohort counting 503 women who had vabrasio was evaluated retrospectively. The women included were consecutive patients who had received vabrasio at the 
Department of Gynaecology and Obstetrics at Herning Hos­pital, Denmark, during a two-year period. They were iden......­tified by searching the hospital database for the Inter­na­tional Classification of Diseases, tenth version (ICD-10) code for vabrasio.
 Results: The indications for vabrasio were postmeno­pausal bleeding (45%), meno/metrorrhagia (43%) and thickened endometrium/polyp (6%). The first evaluation by vabrasio was normal...

DJ-1 is a reliable serum biomarker for discriminating high-risk endometrial cancer.

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Di Cello, Annalisa; Di Sanzo, Maddalena; Perrone, Francesca Marta; Santamaria, Gianluca; Rania, Erika; Angotti, Elvira; Venturella, Roberta; Mancuso, Serafina; Zullo, Fulvio; Cuda, Giovanni; Costanzo, Francesco

2017-06-01

New reliable approaches to stratify patients with endometrial cancer into risk categories are highly needed. We have recently demonstrated that DJ-1 is overexpressed in endometrial cancer, showing significantly higher levels both in serum and tissue of patients with high-risk endometrial cancer compared with low-risk endometrial cancer. In this experimental study, we further extended our observation, evaluating the role of DJ-1 as an accurate serum biomarker for high-risk endometrial cancer. A total of 101 endometrial cancer patients and 44 healthy subjects were prospectively recruited. DJ-1 serum levels were evaluated comparing cases and controls and, among endometrial cancer patients, between high- and low-risk patients. The results demonstrate that DJ-1 levels are significantly higher in cases versus controls and in high- versus low-risk patients. The receiver operating characteristic curve analysis shows that DJ-1 has a very good diagnostic accuracy in discriminating endometrial cancer patients versus controls and an excellent accuracy in distinguishing, among endometrial cancer patients, low- from high-risk cases. DJ-1 sensitivity and specificity are the highest when high- and low-risk patients are compared, reaching the value of 95% and 99%, respectively. Moreover, DJ-1 serum levels seem to be correlated with worsening of the endometrial cancer grade and histotype, making it a reliable tool in the preoperative decision-making process.

Side effects of cancer therapies. International classification and documentation systems

International Nuclear Information System (INIS)

Seegenschmiedt, M.H.

1998-01-01

The publication presents and explains verified, international classification and documentation systems for side effects induced by cancer treatments, applicable in general and clinical practice and clinical research, and covers in a clearly arranged manner the whole range of treatments, including acute and chronic side effects of chemotherapy and radiotherapy, surgery, or combined therapies. The book fills a long-felt need in tumor documentation and is a major contribution to quality assurance in clinical oncology in German-speaking countries. As most parts of the book are bilingual, presenting German and English texts and terminology, it satisfies the principles of interdisciplinarity and internationality. The tabulated form chosen for presentation of classification systems and criteria facilitate the user's approach as well as application in daily work. (orig./CB) [de

Reliability and validity study of a tool to measure cancer stigma: Patient version

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Medine Yilmaz

2017-01-01

Full Text Available Objective: The aim of this methodological study is to establish the validity and reliability of the Turkish version of “A Questionnaire for Measuring Attitudes toward Cancer (Cancer Stigma - Patient version.” Methods: The sample comprised oncology patients who had active cancer treatment. The construct validity was assessed using the confirmatory and exploratory factor analysis. Results: The mean age of the participants was 54.9±12.3 years. In the confirmatory factor analysis, fit values were determined as comparative fit index = 0.93, goodness of fit index = 0.91, normed-fit index=0.91, and root mean square error of approximation RMSEA = 0.09 (P<0.05 (Kaiser–Meyer–Olkin = 0.88, χ2 = 1084.41, Df = 66, and Barletta's test P<0.000. The first factor was “impossibility of recovery and experience of social discrimination” and the second factor was “stereotypes of cancer patients.” The two-factor structure accounted for 56.74% of the variance. The Cronbach's alpha value was determined as 0.88 for the two-factor scale. Conclusions: “A questionnaire for measuring attitudes toward cancer (cancer stigma - Patient version” is a reliable and valid questionnaire to assess stigmatization of cancer in cancer patients.

Median Filter Noise Reduction of Image and Backpropagation Neural Network Model for Cervical Cancer Classification

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Wutsqa, D. U.; Marwah, M.

2017-06-01

In this paper, we consider spatial operation median filter to reduce the noise in the cervical images yielded by colposcopy tool. The backpropagation neural network (BPNN) model is applied to the colposcopy images to classify cervical cancer. The classification process requires an image extraction by using a gray level co-occurrence matrix (GLCM) method to obtain image features that are used as inputs of BPNN model. The advantage of noise reduction is evaluated by comparing the performances of BPNN models with and without spatial operation median filter. The experimental result shows that the spatial operation median filter can improve the accuracy of the BPNN model for cervical cancer classification.

A multicenter validation of an endoscopic classification with narrow band imaging for gastric precancerous and cancerous lesions

NARCIS (Netherlands)

Pimentel-Nunes, P.; Dinis-Ribeiro, M.; Soares, J. B.; Marcos-Pinto, R.; Santos, C.; Rolanda, C.; Bastos, R. P.; Areia, M.; Afonso, L.; Bergman, J.; Sharma, P.; Gotoda, T.; Henrique, R.; Moreira-Dias, L.

2012-01-01

Background and study aim: The reliability and external validity of narrow band imaging (NBI) in the stomach have not been described consistently. The aim of the current study was to describe and estimate the accuracy and reliability of a simplified classification system for NBI in the diagnosis of

Alternative Polyadenylation Patterns for Novel Gene Discovery and Classification in Cancer

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Oguzhan Begik

2017-07-01

Full Text Available Certain aspects of diagnosis, prognosis, and treatment of cancer patients are still important challenges to be addressed. Therefore, we propose a pipeline to uncover patterns of alternative polyadenylation (APA, a hidden complexity in cancer transcriptomes, to further accelerate efforts to discover novel cancer genes and pathways. Here, we analyzed expression data for 1045 cancer patients and found a significant shift in usage of poly(A signals in common tumor types (breast, colon, lung, prostate, gastric, and ovarian compared to normal tissues. Using machine-learning techniques, we further defined specific subsets of APA events to efficiently classify cancer types. Furthermore, APA patterns were associated with altered protein levels in patients, revealed by antibody-based profiling data, suggesting functional significance. Overall, our study offers a computational approach for use of APA in novel gene discovery and classification in common tumor types, with important implications in basic research, biomarker discovery, and precision medicine approaches.

Visualization and tissue classification of human breast cancer images using ultrahigh-resolution OCT (Conference Presentation)

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Yao, Xinwen; Gan, Yu; Chang, Ernest W.; Hibshoosh, Hanina; Feldman, Sheldon; Hendon, Christine P.

2017-02-01

We employed a home-built ultrahigh resolution (UHR) OCT system at 800nm to image human breast cancer sample ex vivo. The system has an axial resolution of 2.72µm and a lateral resolution of 5.52µm with an extended imaging range of 1.78mm. Over 900 UHR OCT volumes were generated on specimens from 23 breast cancer cases. With better spatial resolution, detailed structures in the breast tissue were better defined. Different types of breast cancer as well as healthy breast tissue can be well delineated from the UHR OCT images. To quantitatively evaluate the advantages of UHR OCT imaging of breast cancer, features derived from OCT intensity images were used as inputs to a machine learning model, the relevance vector machine. A trained machine learning model was employed to evaluate the performance of tissue classification based on UHR OCT images for differentiating tissue types in the breast samples, including adipose tissue, healthy stroma and cancerous region. For adipose tissue, grid-based local features were extracted from OCT intensity data, including standard deviation, entropy, and homogeneity. We showed that it was possible to enhance the classification performance on distinguishing fat tissue from non-fat tissue by using the UHR images when compared with the results based on OCT images from a commercial 1300 nm OCT system. For invasive ductal carcinoma (IDC) and normal stroma differentiation, the classification was based on frame-based features that portray signal penetration depth and tissue reflectivity. The confusing matrix indicated a sensitivity of 97.5% and a sensitivity of 77.8%.

Classification of breast cancer cytological specimen using convolutional neural network

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Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

2017-01-01

The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

Examining applying high performance genetic data feature selection and classification algorithms for colon cancer diagnosis.

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Al-Rajab, Murad; Lu, Joan; Xu, Qiang

2017-07-01

This paper examines the accuracy and efficiency (time complexity) of high performance genetic data feature selection and classification algorithms for colon cancer diagnosis. The need for this research derives from the urgent and increasing need for accurate and efficient algorithms. Colon cancer is a leading cause of death worldwide, hence it is vitally important for the cancer tissues to be expertly identified and classified in a rapid and timely manner, to assure both a fast detection of the disease and to expedite the drug discovery process. In this research, a three-phase approach was proposed and implemented: Phases One and Two examined the feature selection algorithms and classification algorithms employed separately, and Phase Three examined the performance of the combination of these. It was found from Phase One that the Particle Swarm Optimization (PSO) algorithm performed best with the colon dataset as a feature selection (29 genes selected) and from Phase Two that the Support Vector Machine (SVM) algorithm outperformed other classifications, with an accuracy of almost 86%. It was also found from Phase Three that the combined use of PSO and SVM surpassed other algorithms in accuracy and performance, and was faster in terms of time analysis (94%). It is concluded that applying feature selection algorithms prior to classification algorithms results in better accuracy than when the latter are applied alone. This conclusion is important and significant to industry and society. Copyright © 2017 Elsevier B.V. All rights reserved.

Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods

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Mark Burton

2012-01-01

Full Text Available Machine learning has increasingly been used with microarray gene expression data and for the development of classifiers using a variety of methods. However, method comparisons in cross-study datasets are very scarce. This study compares the performance of seven classification methods and the effect of voting for predicting metastasis outcome in breast cancer patients, in three situations: within the same dataset or across datasets on similar or dissimilar microarray platforms. Combining classification results from seven classifiers into one voting decision performed significantly better during internal validation as well as external validation in similar microarray platforms than the underlying classification methods. When validating between different microarray platforms, random forest, another voting-based method, proved to be the best performing method. We conclude that voting based classifiers provided an advantage with respect to classifying metastasis outcome in breast cancer patients.

Efficacy of hidden markov model over support vector machine on multiclass classification of healthy and cancerous cervical tissues

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Mukhopadhyay, Sabyasachi; Kurmi, Indrajit; Pratiher, Sawon; Mukherjee, Sukanya; Barman, Ritwik; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2018-02-01

In this paper, a comparative study between SVM and HMM has been carried out for multiclass classification of cervical healthy and cancerous tissues. In our study, the HMM methodology is more promising to produce higher accuracy in classification.

Feature selection and classification of MAQC-II breast cancer and multiple myeloma microarray gene expression data.

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Qingzhong Liu

Full Text Available Microarray data has a high dimension of variables but available datasets usually have only a small number of samples, thereby making the study of such datasets interesting and challenging. In the task of analyzing microarray data for the purpose of, e.g., predicting gene-disease association, feature selection is very important because it provides a way to handle the high dimensionality by exploiting information redundancy induced by associations among genetic markers. Judicious feature selection in microarray data analysis can result in significant reduction of cost while maintaining or improving the classification or prediction accuracy of learning machines that are employed to sort out the datasets. In this paper, we propose a gene selection method called Recursive Feature Addition (RFA, which combines supervised learning and statistical similarity measures. We compare our method with the following gene selection methods: Support Vector Machine Recursive Feature Elimination (SVMRFE, Leave-One-Out Calculation Sequential Forward Selection (LOOCSFS, Gradient based Leave-one-out Gene Selection (GLGS. To evaluate the performance of these gene selection methods, we employ several popular learning classifiers on the MicroArray Quality Control phase II on predictive modeling (MAQC-II breast cancer dataset and the MAQC-II multiple myeloma dataset. Experimental results show that gene selection is strictly paired with learning classifier. Overall, our approach outperforms other compared methods. The biological functional analysis based on the MAQC-II breast cancer dataset convinced us to apply our method for phenotype prediction. Additionally, learning classifiers also play important roles in the classification of microarray data and our experimental results indicate that the Nearest Mean Scale Classifier (NMSC is a good choice due to its prediction reliability and its stability across the three performance measurements: Testing accuracy, MCC values, and

The Reliability and Validity of Prostate Cancer Fatalism Inventory in Turkish Language.

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Aydoğdu, Nihal Gördes; Çapık, Cantürk; Ersin, Fatma; Kissal, Aygul; Bahar, Zuhal

2017-10-01

This study aimed to conduct the reliability and validity study of the Prostate Cancer Fatalism Inventory in Turkish language. The study carried out in methodological type and consisted of 171 men. The ages of the participants ranged between 40 and 82. The content validity index was determined to be 0.80, Kaiser-Meyer-Olkin value 0.825, Bartlett's test X 2  = 750.779 and p = 0.000. Then the principal component analysis was applied to the 15-item inventory. The inventory consisted of one dimension, and the load factors were over 0.30 for all items. The explained variance of the inventory was found 33.3 %. The Kuder-Richardson-20 coefficient was determined to be 0.849 and the item-total correlations ranged between 0.335 and 0.627. The Prostate Cancer Fatalism Inventory was a reliable and valid measurement tool in Turkish language. Integrating psychological strategies for prostate cancer screening may be required to strengthen the positive effects of nursing education.

Multiclass classification for skin cancer profiling based on the integration of heterogeneous gene expression series.

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Gálvez, Juan Manuel; Castillo, Daniel; Herrera, Luis Javier; San Román, Belén; Valenzuela, Olga; Ortuño, Francisco Manuel; Rojas, Ignacio

2018-01-01

Most of the research studies developed applying microarray technology to the characterization of different pathological states of any disease may fail in reaching statistically significant results. This is largely due to the small repertoire of analysed samples, and to the limitation in the number of states or pathologies usually addressed. Moreover, the influence of potential deviations on the gene expression quantification is usually disregarded. In spite of the continuous changes in omic sciences, reflected for instance in the emergence of new Next-Generation Sequencing-related technologies, the existing availability of a vast amount of gene expression microarray datasets should be properly exploited. Therefore, this work proposes a novel methodological approach involving the integration of several heterogeneous skin cancer series, and a later multiclass classifier design. This approach is thus a way to provide the clinicians with an intelligent diagnosis support tool based on the use of a robust set of selected biomarkers, which simultaneously distinguishes among different cancer-related skin states. To achieve this, a multi-platform combination of microarray datasets from Affymetrix and Illumina manufacturers was carried out. This integration is expected to strengthen the statistical robustness of the study as well as the finding of highly-reliable skin cancer biomarkers. Specifically, the designed operation pipeline has allowed the identification of a small subset of 17 differentially expressed genes (DEGs) from which to distinguish among 7 involved skin states. These genes were obtained from the assessment of a number of potential batch effects on the gene expression data. The biological interpretation of these genes was inspected in the specific literature to understand their underlying information in relation to skin cancer. Finally, in order to assess their possible effectiveness in cancer diagnosis, a cross-validation Support Vector Machines (SVM

Setting a generalized functional linear model (GFLM for the classification of different types of cancer

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Miguel Flores

2016-11-01

Full Text Available This work aims to classify the DNA sequences of healthy and malignant cancer respectively. For this, supervised and unsupervised classification methods from a functional context are used; i.e. each strand of DNA is an observation. The observations are discretized, for that reason different ways to represent these observations with functions are evaluated. In addition, an exploratory study is done: estimating the mean and variance of each functional type of cancer. For the unsupervised classification method, hierarchical clustering with different measures of functional distance is used. On the other hand, for the supervised classification method, a functional generalized linear model is used. For this model the first and second derivatives are used which are included as discriminating variables. It has been verified that one of the advantages of working in the functional context is to obtain a model to correctly classify cancers by 100%. For the implementation of the methods it has been used the fda.usc R package that includes all the techniques of functional data analysis used in this work. In addition, some that have been developed in recent decades. For more details of these techniques can be consulted Ramsay, J. O. and Silverman (2005 and Ferraty et al. (2006.

Classification of Dukes' B and C colorectal cancers using expression arrays

DEFF Research Database (Denmark)

Frederiksen, C.M.; Knudsen, Steen; Laurberg, S.

2003-01-01

Purpose. Colorectal cancer is one of the most common malignancies. Substaging of the cancer is of importance not only to prognosis but also to treatment. Classification of substages based on DNA microarray technology is currently the most promising approach. We therefore investigated if gene...... expression microarrays could be used to classify colorectal tumors. Methods. We used the Affymetrix oligonucleotide arrays to analyze the expression of more than 5,000 genes in samples from the sigmoid and upper rectum of the left colon. Five samples were from normal mucosa and five samples from each...... expression of one of the most common malignancies, colorectal cancer, now seems to be within reach. The data indicates that it is possible at least to classify Dukes' B and C colorectal tumors with microarrays....

AN ADABOOST OPTIMIZED CCFIS BASED CLASSIFICATION MODEL FOR BREAST CANCER DETECTION

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CHANDRASEKAR RAVI

2017-06-01

Full Text Available Classification is a Data Mining technique used for building a prototype of the data behaviour, using which an unseen data can be classified into one of the defined classes. Several researchers have proposed classification techniques but most of them did not emphasis much on the misclassified instances and storage space. In this paper, a classification model is proposed that takes into account the misclassified instances and storage space. The classification model is efficiently developed using a tree structure for reducing the storage complexity and uses single scan of the dataset. During the training phase, Class-based Closed Frequent ItemSets (CCFIS were mined from the training dataset in the form of a tree structure. The classification model has been developed using the CCFIS and a similarity measure based on Longest Common Subsequence (LCS. Further, the Particle Swarm Optimization algorithm is applied on the generated CCFIS, which assigns weights to the itemsets and their associated classes. Most of the classifiers are correctly classifying the common instances but they misclassify the rare instances. In view of that, AdaBoost algorithm has been used to boost the weights of the misclassified instances in the previous round so as to include them in the training phase to classify the rare instances. This improves the accuracy of the classification model. During the testing phase, the classification model is used to classify the instances of the test dataset. Breast Cancer dataset from UCI repository is used for experiment. Experimental analysis shows that the accuracy of the proposed classification model outperforms the PSOAdaBoost-Sequence classifier by 7% superior to other approaches like Naïve Bayes Classifier, Support Vector Machine Classifier, Instance Based Classifier, ID3 Classifier, J48 Classifier, etc.

Can we improve accuracy and reliability of MRI interpretation in children with optic pathway glioma? Proposal for a reproducible imaging classification

Energy Technology Data Exchange (ETDEWEB)

Lambron, Julien; Frampas, Eric; Toulgoat, Frederique [University Hospital, Department of Radiology, Nantes (France); Rakotonjanahary, Josue [University Hospital, Department of Pediatric Oncology, Angers (France); University Paris Diderot, INSERM CIE5 Robert Debre Hospital, Assistance Publique-Hopitaux de Paris (AP-HP), Paris (France); Loisel, Didier [University Hospital, Department of Radiology, Angers (France); Carli, Emilie de; Rialland, Xavier [University Hospital, Department of Pediatric Oncology, Angers (France); Delion, Matthieu [University Hospital, Department of Neurosurgery, Angers (France)

2016-02-15

Magnetic resonance (MR) images from children with optic pathway glioma (OPG) are complex. We initiated this study to evaluate the accuracy of MR imaging (MRI) interpretation and to propose a simple and reproducible imaging classification for MRI. We randomly selected 140 MRIs from among 510 MRIs performed on 104 children diagnosed with OPG in France from 1990 to 2004. These images were reviewed independently by three radiologists (F.T., 15 years of experience in neuroradiology; D.L., 25 years of experience in pediatric radiology; and J.L., 3 years of experience in radiology) using a classification derived from the Dodge and modified Dodge classifications. Intra- and interobserver reliabilities were assessed using the Bland-Altman method and the kappa coefficient. These reviews allowed the definition of reliable criteria for MRI interpretation. The reviews showed intraobserver variability and large discrepancies among the three radiologists (kappa coefficient varying from 0.11 to 1). These variabilities were too large for the interpretation to be considered reproducible over time or among observers. A consensual analysis, taking into account all observed variabilities, allowed the development of a definitive interpretation protocol. Using this revised protocol, we observed consistent intra- and interobserver results (kappa coefficient varying from 0.56 to 1). The mean interobserver difference for the solid portion of the tumor with contrast enhancement was 0.8 cm{sup 3} (limits of agreement = -16 to 17). We propose simple and precise rules for improving the accuracy and reliability of MRI interpretation for children with OPG. Further studies will be necessary to investigate the possible prognostic value of this approach. (orig.)

Five systems of psychiatric classification for preschool children: do differences in validity, usefulness and reliability make for competitive or complimentary constellations?

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Postert, Christian; Averbeck-Holocher, Marlies; Beyer, Thomas; Müller, Jörg; Furniss, Tilman

2009-03-01

DSM-IV and ICD-10 have limitations in the diagnostic classification of psychiatric disorders at preschool age (0-5 years). The publication of the Diagnostic Classification 0-3 (DC:0-3) in 1994, its basically revised second edition (DC:0-3R) in 2005 and the Research Diagnostic Criteria-Preschool Age (RDC-PA) in 2004 have provided several modifications of these manuals. Taking into account the growing empirical evidence highlighting the need for a diagnostic classification system for psychiatric disorders in preschool children, the main categorical classification systems in preschool psychiatry will be presented and discussed. The paper will focus on issues of validity, usefulness and reliability in DSM-IV, ICD-10, RDC-PA, DC:0-3, and DC:0-3R. The reasons for including or excluding postulated psychiatric disorder categories for preschool children with variable degrees of empirical evidence into the different diagnostic systems will be discussed.

Molecular Classification of Lobular Carcinoma of the Breast

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Fu, Denggang; Zuo, Qi; Huang, Qi; Su, Li; Ring, Huijun Z.; Ring, Brian Z.

2017-01-01

The morphology of breast tumors is complicated and diagnosis can be difficult. We present here a novel diagnostic model which we validate on both array-based and RNA sequencing platforms which reliably distinguishes this tumor type across multiple cohorts. We also examine how this molecular classification predicts sensitivity to common chemotherapeutics in cell-line based assays. A total of 1845 invasive breast cancer cases in six cohorts were collected, split into discovery and validation cohorts, and a classifier was created and compared to pathological diagnosis, grade and survival. In the validation cohorts the concordance of predicted diagnosis with a pathological diagnosis was 92%, and 97% when inconclusively classified cases were excluded. Tumor-derived cell lines were classified with the model as having predominantly ductal or lobular-like molecular physiologies, and sensitivity of these lines to relevant compounds was analyzed. A diagnostic tool can be created that reliably distinguishes lobular from ductal carcinoma and allows the classification of cell lines on the basis of molecular profiles associated with these tumor types. This tool may assist in improved diagnosis and aid in explorations of the response of lobular type breast tumor models to different compounds. PMID:28303886

Classification of prostate cancer grade using temporal ultrasound: in vivo feasibility study

Science.gov (United States)

Ghavidel, Sahar; Imani, Farhad; Khallaghi, Siavash; Gibson, Eli; Khojaste, Amir; Gaed, Mena; Moussa, Madeleine; Gomez, Jose A.; Siemens, D. Robert; Leveridge, Michael; Chang, Silvia; Fenster, Aaron; Ward, Aaron D.; Abolmaesumi, Purang; Mousavi, Parvin

2016-03-01

Temporal ultrasound has been shown to have high classification accuracy in differentiating cancer from benign tissue. In this paper, we extend the temporal ultrasound method to classify lower grade Prostate Cancer (PCa) from all other grades. We use a group of nine patients with mostly lower grade PCa, where cancerous regions are also limited. A critical challenge is to train a classifier with limited aggressive cancerous tissue compared to low grade cancerous tissue. To resolve the problem of imbalanced data, we use Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic samples for the minority class. We calculate spectral features of temporal ultrasound data and perform feature selection using Random Forests. In leave-one-patient-out cross-validation strategy, an area under receiver operating characteristic curve (AUC) of 0.74 is achieved with overall sensitivity and specificity of 70%. Using an unsupervised learning approach prior to proposed method improves sensitivity and AUC to 80% and 0.79. This work represents promising results to classify lower and higher grade PCa with limited cancerous training samples, using temporal ultrasound.

Rough set soft computing cancer classification and network: one stone, two birds.

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Zhang, Yue

2010-07-15

Gene expression profiling provides tremendous information to help unravel the complexity of cancer. The selection of the most informative genes from huge noise for cancer classification has taken centre stage, along with predicting the function of such identified genes and the construction of direct gene regulatory networks at different system levels with a tuneable parameter. A new study by Wang and Gotoh described a novel Variable Precision Rough Sets-rooted robust soft computing method to successfully address these problems and has yielded some new insights. The significance of this progress and its perspectives will be discussed in this article.

North American Magazine Coverage of Skin Cancer and Recreational Tanning Before and After the WHO/IARC 2009 Classification of Indoor Tanning Devices as Carcinogenic.

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McWhirter, Jennifer E; Hoffman-Goetz, Laurie

2015-09-01

The mass media is an influential source of skin cancer information for the public. In 2009, the World Health Organization's International Agency for Research on Cancer classified UV radiation from tanning devices as carcinogenic. Our objective was to determine if media coverage of skin cancer and recreational tanning increased in volume or changed in nature after this classification. We conducted a directed content analysis on 29 North American popular magazines (2007-2012) to investigate the overall volume of articles on skin cancer and recreational tanning and, more specifically, the presence of skin cancer risk factors, UV behaviors, and early detection information in article text (n = 410) and images (n = 714). The volume of coverage on skin cancer and recreational tanning did not increase significantly after the 2009 classification of tanning beds as carcinogenic. Key-related messages, including that UV exposure is a risk factor for skin cancer and that indoor tanning should be avoided, were not reported more frequently after the classification, but the promotion of the tanned look as attractive was conveyed more often in images afterwards (p skin cancer risk factors, other UV behaviors, or early detection information over time. The classification of indoor tanning beds as carcinogenic had no significant impact on the volume or nature of skin cancer and recreational tanning coverage in magazines.

Reliability and validity of Champion's Health Belief Model Scale for breast cancer screening among Malaysian women.

Science.gov (United States)

Parsa, P; Kandiah, M; Mohd Nasir, M T; Hejar, A R; Nor Afiah, M Z

2008-11-01

Breast cancer is the leading cause of cancer deaths in Malaysian women, and the use of breast self-examination (BSE), clinical breast examination (CBE) and mammography remain low in Malaysia. Therefore, there is a need to develop a valid and reliable tool to measure the beliefs that influence breast cancer screening practices. The Champion's Health Belief Model Scale (CHBMS) is a valid and reliable tool to measure beliefs about breast cancer and screening methods in the Western culture. The purpose of this study was to translate the use of CHBMS into the Malaysian context and validate the scale among Malaysian women. A random sample of 425 women teachers was taken from 24 secondary schools in Selangor state, Malaysia. The CHBMS was translated into the Malay language, validated by an expert's panel, back translated, and pretested. Analyses included descriptive statistics of all the study variables, reliability estimates, and construct validity using factor analysis. The mean age of the respondents was 37.2 (standard deviation 7.1) years. Factor analysis yielded ten factors for BSE with eigenvalue greater than 1 (four factors more than the original): confidence 1 (ability to differentiate normal and abnormal changes in the breasts), barriers to BSE, susceptibility for breast cancer, benefits of BSE, health motivation 1 (general health), seriousness 1 (fear of breast cancer), confidence 2 (ability to detect size of lumps), seriousness 2 (fear of long-term effects of breast cancer), health motivation 2 (preventive health practice), and confidence 3 (ability to perform BSE correctly). For CBE and mammography scales, seven factors each were identified. Factors for CBE scale include susceptibility, health motivation 1, benefits of CBE, seriousness 1, barriers of CBE, seriousness 2 and health motivation 2. For mammography the scale includes benefits of mammography, susceptibility, health motivation 1, seriousness 1, barriers to mammography seriousness 2 and health

Genetic Fuzzy System (GFS based wavelet co-occurrence feature selection in mammogram classification for breast cancer diagnosis

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Meenakshi M. Pawar

2016-09-01

Full Text Available Breast cancer is significant health problem diagnosed mostly in women worldwide. Therefore, early detection of breast cancer is performed with the help of digital mammography, which can reduce mortality rate. This paper presents wrapper based feature selection approach for wavelet co-occurrence feature (WCF using Genetic Fuzzy System (GFS in mammogram classification problem. The performance of GFS algorithm is explained using mini-MIAS database. WCF features are obtained from detail wavelet coefficients at each level of decomposition of mammogram image. At first level of decomposition, 18 features are applied to GFS algorithm, which selects 5 features with an average classification success rate of 39.64%. Subsequently, at second level it selects 9 features from 36 features and the classification success rate is improved to 56.75%. For third level, 16 features are selected from 54 features and average success rate is improved to 64.98%. Lastly, at fourth level 72 features are applied to GFS, which selects 16 features and thereby increasing average success rate to 89.47%. Hence, GFS algorithm is the effective way of obtaining optimal set of feature in breast cancer diagnosis.

Development of a Taiwan cancer-related fatigue cognition questionnaire: reliability and validity.

Science.gov (United States)

Lai, Shih-Chiung; Lin, Wei-Chun; Chen, Chien-Hsin; Wu, Szu-Yuan

2017-04-25

We prospectively designed a Taiwan cancer-related fatigue cognition questionnaire, version 1.0 (TCRFCQ-V1.0), for Taiwanese patients with cancer and investigated the reliability and validity of this questionnaire. The completion rate of the TCRFCQ-V1.0 was high (97% of the patients completed all items), and the rate of missing data was low (0.2%-1.1% for each item). Moreover, the Cronbach alpha value was 0.889. We eliminated 5 items because their respective Cronbach alpha values were higher than the total mean value of Cronbach's alpha. Overall, the TCRFCQ-V1.0 had adequate Cronbach alpha coefficients (range, from 0.882 to 0.889). In addition, the results of Bartlett's test were significant (chi-squared, 2390.11; p Kaiser-Meyer-Olkin statistic of 0.868. Through exploratory factor analysis, we identified 6 factors with eigenvalues of > 1, and the scree plot indicated no flattening factors. Overall, 28 items achieved a factor loading of ≥ 0.55. We enrolled patients with cancer who were aged > 18 years, had received a pathological diagnosis of cancer, and had undergone cancer treatments such as surgery, chemotherapy, radiotherapy, or concurrent chemoradiotherapy at a single institute in Taiwan. Of the identified 167 eligible patients, 161 (96.4%) were approached. Of these patients, 6 (7.2%) declined to participate and 155 (92.8%) were interviewed. The initial 43 items in the TCRFCQ-V1.0 were assessed for ceiling and floor effects. The TCRFCQ-V1.0 is a reliable and valid instrument for measuring CRF cognition in Taiwanese patients with cancer.

Classification tree analysis of second neoplasms in survivors of childhood cancer

International Nuclear Information System (INIS)

Jazbec, Janez; Todorovski, Ljupčo; Jereb, Berta

2007-01-01

Reports on childhood cancer survivors estimated cumulative probability of developing secondary neoplasms vary from 3,3% to 25% at 25 years from diagnosis, and the risk of developing another cancer to several times greater than in the general population. In our retrospective study, we have used the classification tree multivariate method on a group of 849 first cancer survivors, to identify childhood cancer patients with the greatest risk for development of secondary neoplasms. In observed group of patients, 34 develop secondary neoplasm after treatment of primary cancer. Analysis of parameters present at the treatment of first cancer, exposed two groups of patients at the special risk for secondary neoplasm. First are female patients treated for Hodgkin's disease at the age between 10 and 15 years, whose treatment included radiotherapy. Second group at special risk were male patients with acute lymphoblastic leukemia who were treated at the age between 4,6 and 6,6 years of age. The risk groups identified in our study are similar to the results of studies that used more conventional approaches. Usefulness of our approach in study of occurrence of second neoplasms should be confirmed in larger sample study, but user friendly presentation of results makes it attractive for further studies

Insights into the classification of small GTPases

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Dominik Heider

2010-05-01

Full Text Available Dominik Heider1, Sascha Hauke3, Martin Pyka4, Daniel Kessler21Department of Bioinformatics, Center for Medical Biotechnology, 2Institute of Cell Biology (Cancer Research, University of Duisburg-Essen, Essen, Germany; 3Institute of Computer Science, University of Münster, Münster, Germany; 4Interdisciplinary Center for Clinical Research, University Hospital of Münster, Münster, GermanyAbstract: In this study we used a Random Forest-based approach for an assignment of small guanosine triphosphate proteins (GTPases to specific subgroups. Small GTPases represent an important functional group of proteins that serve as molecular switches in a wide range of fundamental cellular processes, including intracellular transport, movement and signaling events. These proteins have further gained a special emphasis in cancer research, because within the last decades a huge variety of small GTPases from different subgroups could be related to the development of all types of tumors. Using a random forest approach, we were able to identify the most important amino acid positions for the classification process within the small GTPases superfamily and its subgroups. These positions are in line with the results of earlier studies and have been shown to be the essential elements for the different functionalities of the GTPase families. Furthermore, we provide an accurate and reliable software tool (GTPasePred to identify potential novel GTPases and demonstrate its application to genome sequences.Keywords: cancer, machine learning, classification, Random Forests, proteins

Reliability and Validity of the Sensory Component of the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI): A Systematic Review

Science.gov (United States)

Hales, M.; Biros, E.

2015-01-01

Background: Since 1982, the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) has been used to classify sensation of spinal cord injury (SCI) through pinprick and light touch scores. The absence of proprioception, pain, and temperature within this scale creates questions about its validity and accuracy. Objectives: To assess whether the sensory component of the ISNCSCI represents a reliable and valid measure of classification of SCI. Methods: A systematic review of studies examining the reliability and validity of the sensory component of the ISNCSCI published between 1982 and February 2013 was conducted. The electronic databases MEDLINE via Ovid, CINAHL, PEDro, and Scopus were searched for relevant articles. A secondary search of reference lists was also completed. Chosen articles were assessed according to the Oxford Centre for Evidence-Based Medicine hierarchy of evidence and critically appraised using the McMasters Critical Review Form. A statistical analysis was conducted to investigate the variability of the results given by reliability studies. Results: Twelve studies were identified: 9 reviewed reliability and 3 reviewed validity. All studies demonstrated low levels of evidence and moderate critical appraisal scores. The majority of the articles (~67%; 6/9) assessing the reliability suggested that training was positively associated with better posttest results. The results of the 3 studies that assessed the validity of the ISNCSCI scale were confounding. Conclusions: Due to the low to moderate quality of the current literature, the sensory component of the ISNCSCI requires further revision and investigation if it is to be a useful tool in clinical trials. PMID:26363591

Reliability and Validity of the Sensory Component of the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI): A Systematic Review.

Science.gov (United States)

Hales, M; Biros, E; Reznik, J E

2015-01-01

Since 1982, the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) has been used to classify sensation of spinal cord injury (SCI) through pinprick and light touch scores. The absence of proprioception, pain, and temperature within this scale creates questions about its validity and accuracy. To assess whether the sensory component of the ISNCSCI represents a reliable and valid measure of classification of SCI. A systematic review of studies examining the reliability and validity of the sensory component of the ISNCSCI published between 1982 and February 2013 was conducted. The electronic databases MEDLINE via Ovid, CINAHL, PEDro, and Scopus were searched for relevant articles. A secondary search of reference lists was also completed. Chosen articles were assessed according to the Oxford Centre for Evidence-Based Medicine hierarchy of evidence and critically appraised using the McMasters Critical Review Form. A statistical analysis was conducted to investigate the variability of the results given by reliability studies. Twelve studies were identified: 9 reviewed reliability and 3 reviewed validity. All studies demonstrated low levels of evidence and moderate critical appraisal scores. The majority of the articles (~67%; 6/9) assessing the reliability suggested that training was positively associated with better posttest results. The results of the 3 studies that assessed the validity of the ISNCSCI scale were confounding. Due to the low to moderate quality of the current literature, the sensory component of the ISNCSCI requires further revision and investigation if it is to be a useful tool in clinical trials.

Human reliability

International Nuclear Information System (INIS)

Embrey, D.E.

1987-01-01

Concepts and techniques of human reliability have been developed and are used mostly in probabilistic risk assessment. For this, the major application of human reliability assessment has been to identify the human errors which have a significant effect on the overall safety of the system and to quantify the probability of their occurrence. Some of the major issues within human reliability studies are reviewed and it is shown how these are applied to the assessment of human failures in systems. This is done under the following headings; models of human performance used in human reliability assessment, the nature of human error, classification of errors in man-machine systems, practical aspects, human reliability modelling in complex situations, quantification and examination of human reliability, judgement based approaches, holistic techniques and decision analytic approaches. (UK)

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

Science.gov (United States)

Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

2015-06-01

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Test-retest Agreement and Reliability of Quantitative Sensory Testing 1 Year After Breast Cancer Surgery

DEFF Research Database (Denmark)

Andersen, Kenneth Geving; Kehlet, Henrik; Aasvang, Eske Kvanner

2015-01-01

.5 SD) than within-patient variation (0.23 to 3.55 SD). There were no significant differences between pain and pain-free patients. The individual test-retest variability was higher on the operated side compared with the nonoperated side. DISCUSSION: The QST protocol reliability allows for group......OBJECTIVES: Quantitative sensory testing (QST) is used to assess sensory dysfunction and nerve damage by examining psychophysical responses to controlled, graded stimuli such as mechanical and thermal detection and pain thresholds. In the breast cancer population, 4 studies have used QST to examine...... persistent pain after breast cancer treatment, suggesting neuropathic pain being a prominent pain mechanism. However, the agreement and reliability of QST has not been described in the postsurgical breast cancer population, hindering exact interpretation of QST studies in this population. The aim...

[Clinical Study of 2014 ISUP New Grade Group Classification for Prostate Cancer Patients Treated by Androgen Deprivation Therapy].

Science.gov (United States)

Uno, Masahiro; Kawase, Makoto; Kato, Daiki; Ishida, Takashi; Kato, Seiichi; Fujimoto, Yoshinori

2018-01-01

The 2014 International Society of Urological Pathology (ISUP) has proposed a new grade group (GG) classification for Gleason scores (GS). The usefulness of the new GG classification was investigated with 518 prostate cancer patients who underwent androgen deprivation therapy. According to the new GG classification, Stages B‒D and the new GG classification relapse-free rate for each stage were calculated using the Kaplan‒Meier method. The new GG classification revealed a significant difference for the relapse-free rate only between some groups. Analysis using the Cox proportional hazards model indicated that the risk of relapse was higher in GGs 4 and 5 than in GG 1. The usefulness about the relapse-free rate in androgen deprivation therapy of the 2014 ISUP new grade group classification a waits future examination.

The korean version of the body image scale-reliability and validity in a sample of breast cancer patients.

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Khang, Dongwoo; Rim, Hyo-Deog; Woo, Jungmin

2013-03-01

The Body Image Scale (BIS) developed in collaboration with the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Study Group is a brief questionnaire for measuring body image concerns in patients with cancer. This study sought to assess the reliability and validity of the Korean version of the Body Image Scale (K-BIS). The participants consisted of 155 postoperative breast cancer patients (56 breast conserving surgery, 56 mastectomy, and 43 oncoplastic surgery). Subjects were evaluated using the K-BIS, the Body-Esteem Scale for Adolescents and Adults (BESAA), the Rosenberg Self-Esteem Scale (RSES), the Hospital Anxiety and Depression Scale (HADS), and the World Health Organization Quality of Life Scale Abbreviated Version (WHOQOL-BREF). Test-retest reliability and internal consistency were examined as a measure of reliability and validity was evaluated by convergent validity, discriminant validity and factor analysis. Cronbach's α value was 0.943. The total score of the K-BIS was negatively correlated with the BESAA (r=0.301, p59% variance. The K-BIS showed good reliability and validity for assessment of body image in Korean breast cancer patients.

Early detection of lung cancer from CT images: nodule segmentation and classification using deep learning

Science.gov (United States)

Sharma, Manu; Bhatt, Jignesh S.; Joshi, Manjunath V.

2018-04-01

Lung cancer is one of the most abundant causes of the cancerous deaths worldwide. It has low survival rate mainly due to the late diagnosis. With the hardware advancements in computed tomography (CT) technology, it is now possible to capture the high resolution images of lung region. However, it needs to be augmented by efficient algorithms to detect the lung cancer in the earlier stages using the acquired CT images. To this end, we propose a two-step algorithm for early detection of lung cancer. Given the CT image, we first extract the patch from the center location of the nodule and segment the lung nodule region. We propose to use Otsu method followed by morphological operations for the segmentation. This step enables accurate segmentation due to the use of data-driven threshold. Unlike other methods, we perform the segmentation without using the complete contour information of the nodule. In the second step, a deep convolutional neural network (CNN) is used for the better classification (malignant or benign) of the nodule present in the segmented patch. Accurate segmentation of even a tiny nodule followed by better classification using deep CNN enables the early detection of lung cancer. Experiments have been conducted using 6306 CT images of LIDC-IDRI database. We achieved the test accuracy of 84.13%, with the sensitivity and specificity of 91.69% and 73.16%, respectively, clearly outperforming the state-of-the-art algorithms.

Breast cancer molecular subtype classification using deep features: preliminary results

Science.gov (United States)

Zhu, Zhe; Albadawy, Ehab; Saha, Ashirbani; Zhang, Jun; Harowicz, Michael R.; Mazurowski, Maciej A.

2018-02-01

Radiogenomics is a field of investigation that attempts to examine the relationship between imaging characteris- tics of cancerous lesions and their genomic composition. This could offer a noninvasive alternative to establishing genomic characteristics of tumors and aid cancer treatment planning. While deep learning has shown its supe- riority in many detection and classification tasks, breast cancer radiogenomic data suffers from a very limited number of training examples, which renders the training of the neural network for this problem directly and with no pretraining a very difficult task. In this study, we investigated an alternative deep learning approach referred to as deep features or off-the-shelf network approach to classify breast cancer molecular subtypes using breast dynamic contrast enhanced MRIs. We used the feature maps of different convolution layers and fully connected layers as features and trained support vector machines using these features for prediction. For the feature maps that have multiple layers, max-pooling was performed along each channel. We focused on distinguishing the Luminal A subtype from other subtypes. To evaluate the models, 10 fold cross-validation was performed and the final AUC was obtained by averaging the performance of all the folds. The highest average AUC obtained was 0.64 (0.95 CI: 0.57-0.71), using the feature maps of the last fully connected layer. This indicates the promise of using this approach to predict the breast cancer molecular subtypes. Since the best performance appears in the last fully connected layer, it also implies that breast cancer molecular subtypes may relate to high level image features

Inter-rater reliability in the classification of supraspinatus tendon tears using 3D ultrasound – a question of experience?

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Giorgio Tamborrini

2016-09-01

Full Text Available Background: Three-dimensional (3D ultrasound of the shoulder is characterized by a comparable accuracy to two-dimensional (2D ultrasound. No studies investigating 2D versus 3D inter-rater reliability in the detection of supraspinatus tendon tears taking into account the level of experience of the raters have been carried out so far. Objectives: The aim of this study was to determine the inter-rater reliability in the analysis of 3D ultrasound image sets of the supraspinatus tendon between sonographer with different levels of experience. Patients and methods: Non-interventional, prospective, observational pilot study of 2309 images of 127 adult patients suffering from unilateral shoulder pain. 3D ultrasound image sets were scored by three raters independently. The intra-and interrater reliabilities were calculated. Results: There was an excellent intra-rater reliability of rater A in the overall classification of supraspinatus tendon tears (2D vs 3D κ = 0.892, pairwise reliability 93.81%, 3D scoring round 1 vs 3D scoring round 2 κ = 0.875, pairwise reliability 92.857%. The inter-rater reliability was only moderate compared to rater B on 3D (κ = 0.497, pairwise reliability 70.95% and fair compared to rater C (κ = 0.238, pairwise reliability 42.38%. Conclusions: The reliability of 3D ultrasound of the supraspinatus tendon depends on the level of experience of the sonographer. Experience in 2D ultrasound does not seem to be sufficient for the analysis of 3D ultrasound imaging sets. Therefore, for a 3D ultrasound analysis new diagnostic criteria have to be established and taught even to experienced 2D sonographers to improve reproducibility.

Breast Cancer Survival Defined by the ER/PR/HER2 Subtypes and a Surrogate Classification according to Tumor Grade and Immunohistochemical Biomarkers

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Carol A. Parise

2014-01-01

Full Text Available Introduction. ER, PR, and HER2 are routinely available in breast cancer specimens. The purpose of this study is to contrast breast cancer-specific survival for the eight ER/PR/HER2 subtypes with survival of an immunohistochemical surrogate for the molecular subtype based on the ER/PR/HER2 subtypes and tumor grade. Methods. We identified 123,780 cases of stages 1–3 primary female invasive breast cancer from California Cancer Registry. The surrogate classification was derived using ER/PR/HER2 and tumor grade. Kaplan-Meier survival analysis and Cox proportional hazards modeling were used to assess differences in survival and risk of mortality for the ER/PR/HER2 subtypes and surrogate classification within each stage. Results. The luminal B/HER2− surrogate classification had a higher risk of mortality than the luminal B/HER2+ for all stages of disease. There was no difference in risk of mortality between the ER+/PR+/HER2− and ER+/PR+/HER2+ in stage 3. With one exception in stage 3, the ER-negative subtypes all had an increased risk of mortality when compared with the ER-positive subtypes. Conclusions. Assessment of survival using ER/PR/HER2 illustrates the heterogeneity of HER2+ subtypes. The surrogate classification provides clear separation in survival and adjusted mortality but underestimates the wide variability within the subtypes that make up the classification.

Breast Cancer Survival Defined by the ER/PR/HER2 Subtypes and a Surrogate Classification according to Tumor Grade and Immunohistochemical Bio markers

International Nuclear Information System (INIS)

Parise, C. A.; Caggiano, V.

2014-01-01

ER, PR, and HER2 are routinely available in breast cancer specimens. The purpose of this study is to contrast breast cancer-specific survival for the eight ER/PR/HER2 subtypes with survival of an immunohistochemical surrogate for the molecular subtype based on the ER/PR/HER2 subtypes and tumor grade. Methods. We identified 123,780 cases of stages 1-3 primary female invasive breast cancer from California Cancer Registry. The surrogate classification was derived using ER/PR/HER2 and tumor grade. Kaplan-Meier survival analysis and Cox proportional hazards modeling were used to assess differences in survival and risk of mortality for the ER/PR/HER2 subtypes and surrogate classification within each stage. Results. The luminal B/HER2− surrogate classification had a higher risk of mortality than the luminal B/HER2+ for all stages of disease. There was no difference in risk of mortality between the ER+/PR+/HER2− and ER+/PR+/HER2+ in stage 3. With one exception in stage 3, the ER-negative subtypes all had an increased risk of mortality when compared with the ER-positive subtypes. Conclusions. Assessment of survival using ER/PR/HER2 illustrates the heterogeneity of HER2+ subtypes. The surrogate classification provides clear separation in survival and adjusted mortality but underestimates the wide variability within the subtypes that make up the classification.

Interobserver and intraobserver reliability and validity of the Vancouver classification system of periprosthetic femoral fractures after hip arthroplasty.

LENUS (Irish Health Repository)

Naqvi, Gohar A

2012-06-01

The Vancouver classification system of periprosthetic fractures has been revalidated in this study, using the radiographs of 45 patients. Three consultants and 3 trainees reviewed the radiographs independently, on 2 separate occasions, at least 2 weeks apart. Interobserver and intraobserver agreement and validity were analyzed, using weighted κ statistics. The mean κ value for interobserver agreement was found to be 0.69 (0.63-0.72) for consultants and 0.61 (0.56-0.65) for the trainees, both representing substantial agreement. Intraobserver κ values ranged from 0.74 to 0.90, showing substantial agreement. Validity analysis of 37 type B cases revealed 81% agreement within B1, B2, and B3 subgroups with a κ value of 0.68 (substantial agreement). This study has reconfirmed the reliability and validity of the Vancouver classification while it also emphasizes the intraoperative assessment of implant stability.

Training ANFIS structure using genetic algorithm for liver cancer classification based on microarray gene expression data

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Bülent Haznedar

2017-02-01

Full Text Available Classification is an important data mining technique, which is used in many fields mostly exemplified as medicine, genetics and biomedical engineering. The number of studies about classification of the datum on DNA microarray gene expression is specifically increased in recent years. However, because of the reasons as the abundance of gene numbers in the datum as microarray gene expressions and the nonlinear relations mostly across those datum, the success of conventional classification algorithms can be limited. Because of these reasons, the interest on classification methods which are based on artificial intelligence to solve the problem on classification has been gradually increased in recent times. In this study, a hybrid approach which is based on Adaptive Neuro-Fuzzy Inference System (ANFIS and Genetic Algorithm (GA are suggested in order to classify liver microarray cancer data set. Simulation results are compared with the results of other methods. According to the results obtained, it is seen that the recommended method is better than the other methods.

Classification of Cancer-related Death Certificates using Machine Learning

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Luke Butt

2013-05-01

Full Text Available BackgroundCancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities.AimsIn this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated.Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes.ResultsDeath certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032 and false negative rate (0.0297 while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers.ConclusionThe selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with

Validation of the prognostic gene portfolio, ClinicoMolecular Triad Classification, using an independent prospective breast cancer cohort and external patient populations.

Science.gov (United States)

Wang, Dong-Yu; Done, Susan J; Mc Cready, David R; Leong, Wey L

2014-07-04

Using genome-wide expression profiles of a prospective training cohort of breast cancer patients, ClinicoMolecular Triad Classification (CMTC) was recently developed to classify breast cancers into three clinically relevant groups to aid treatment decisions. CMTC was found to be both prognostic and predictive in a large external breast cancer cohort in that study. This study serves to validate the reproducibility of CMTC and its prognostic value using independent patient cohorts. An independent internal cohort (n = 284) and a new external cohort (n = 2,181) were used to validate the association of CMTC between clinicopathological factors, 12 known gene signatures, two molecular subtype classifiers, and 19 oncogenic signalling pathway activities, and to reproduce the abilities of CMTC to predict clinical outcomes of breast cancer. In addition, we also updated the outcome data of the original training cohort (n = 147). The original training cohort reached a statistically significant difference (p value of the triad classification was reproduced in the second independent internal cohort and the new external validation cohort. CMTC achieved even higher prognostic significance when all available patients were analyzed (n = 4,851). Oncogenic pathways Myc, E2F1, Ras and β-catenin were again implicated in the high-risk groups. Both prospective internal cohorts and the independent external cohorts reproduced the triad classification of CMTC and its prognostic significance. CMTC is an independent prognostic predictor, and it outperformed 12 other known prognostic gene signatures, molecular subtype classifications, and all other standard prognostic clinicopathological factors. Our results support further development of CMTC portfolio into a guide for personalized breast cancer treatments.

Laser Raman detection for oral cancer based on an adaptive Gaussian process classification method with posterior probabilities

International Nuclear Information System (INIS)

Du, Zhanwei; Yang, Yongjian; Bai, Yuan; Wang, Lijun; Su, Le; Chen, Yong; Li, Xianchang; Zhou, Xiaodong; Shen, Aiguo; Hu, Jiming; Jia, Jun

2013-01-01

The existing methods for early and differential diagnosis of oral cancer are limited due to the unapparent early symptoms and the imperfect imaging examination methods. In this paper, the classification models of oral adenocarcinoma, carcinoma tissues and a control group with just four features are established by utilizing the hybrid Gaussian process (HGP) classification algorithm, with the introduction of the mechanisms of noise reduction and posterior probability. HGP shows much better performance in the experimental results. During the experimental process, oral tissues were divided into three groups, adenocarcinoma (n = 87), carcinoma (n = 100) and the control group (n = 134). The spectral data for these groups were collected. The prospective application of the proposed HGP classification method improved the diagnostic sensitivity to 56.35% and the specificity to about 70.00%, and resulted in a Matthews correlation coefficient (MCC) of 0.36. It is proved that the utilization of HGP in LRS detection analysis for the diagnosis of oral cancer gives accurate results. The prospect of application is also satisfactory. (paper)

Laser Raman detection for oral cancer based on an adaptive Gaussian process classification method with posterior probabilities

Science.gov (United States)

Du, Zhanwei; Yang, Yongjian; Bai, Yuan; Wang, Lijun; Su, Le; Chen, Yong; Li, Xianchang; Zhou, Xiaodong; Jia, Jun; Shen, Aiguo; Hu, Jiming

2013-03-01

The existing methods for early and differential diagnosis of oral cancer are limited due to the unapparent early symptoms and the imperfect imaging examination methods. In this paper, the classification models of oral adenocarcinoma, carcinoma tissues and a control group with just four features are established by utilizing the hybrid Gaussian process (HGP) classification algorithm, with the introduction of the mechanisms of noise reduction and posterior probability. HGP shows much better performance in the experimental results. During the experimental process, oral tissues were divided into three groups, adenocarcinoma (n = 87), carcinoma (n = 100) and the control group (n = 134). The spectral data for these groups were collected. The prospective application of the proposed HGP classification method improved the diagnostic sensitivity to 56.35% and the specificity to about 70.00%, and resulted in a Matthews correlation coefficient (MCC) of 0.36. It is proved that the utilization of HGP in LRS detection analysis for the diagnosis of oral cancer gives accurate results. The prospect of application is also satisfactory.

Defuzzification Strategies for Fuzzy Classifications of Remote Sensing Data

Directory of Open Access Journals (Sweden)

Peter Hofmann

2016-06-01

Full Text Available The classes in fuzzy classification schemes are defined as fuzzy sets, partitioning the feature space through fuzzy rules, defined by fuzzy membership functions. Applying fuzzy classification schemes in remote sensing allows each pixel or segment to be an incomplete member of more than one class simultaneously, i.e., one that does not fully meet all of the classification criteria for any one of the classes and is member of more than one class simultaneously. This can lead to fuzzy, ambiguous and uncertain class assignation, which is unacceptable for many applications, indicating the need for a reliable defuzzification method. Defuzzification in remote sensing has to date, been performed by “crisp-assigning” each fuzzy-classified pixel or segment to the class for which it best fulfills the fuzzy classification rules, regardless of its classification fuzziness, uncertainty or ambiguity (maximum method. The defuzzification of an uncertain or ambiguous fuzzy classification leads to a more or less reliable crisp classification. In this paper the most common parameters for expressing classification uncertainty, fuzziness and ambiguity are analysed and discussed in terms of their ability to express the reliability of a crisp classification. This is done by means of a typical practical example from Object Based Image Analysis (OBIA.

Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules

International Nuclear Information System (INIS)

Teschendorff, Andrew E; Gomez, Sergio; Arenas, Alex; El-Ashry, Dorraya; Schmidt, Marcus; Gehrmann, Mathias; Caldas, Carlos

2010-01-01

Elucidating the activation pattern of molecular pathways across a given tumour type is a key challenge necessary for understanding the heterogeneity in clinical response and for developing novel more effective therapies. Gene expression signatures of molecular pathway activation derived from perturbation experiments in model systems as well as structural models of molecular interactions ('model signatures') constitute an important resource for estimating corresponding activation levels in tumours. However, relatively few strategies for estimating pathway activity from such model signatures exist and only few studies have used activation patterns of pathways to refine molecular classifications of cancer. Here we propose a novel network-based method for estimating pathway activation in tumours from model signatures. We find that although the pathway networks inferred from cancer expression data are highly consistent with the prior information contained in the model signatures, that they also exhibit a highly modular structure and that estimation of pathway activity is dependent on this modular structure. We apply our methodology to a panel of 438 estrogen receptor negative (ER-) and 785 estrogen receptor positive (ER+) breast cancers to infer activation patterns of important cancer related molecular pathways. We show that in ER negative basal and HER2+ breast cancer, gene expression modules reflecting T-cell helper-1 (Th1) and T-cell helper-2 (Th2) mediated immune responses play antagonistic roles as major risk factors for distant metastasis. Using Boolean interaction Cox-regression models to identify non-linear pathway combinations associated with clinical outcome, we show that simultaneous high activation of Th1 and low activation of a TGF-beta pathway module defines a subtype of particularly good prognosis and that this classification provides a better prognostic model than those based on the individual pathways. In ER+ breast cancer, we find that

COII "long fragment" reliability in characterisation and classification of forensically important flies

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Aly, Sanaa M; Mahmoud, Shereen M

2016-01-01

Molecular identification of collected flies is important in forensic entomological analysis guided with accurate evaluation of the chosen genetic marker. The selected mitochondrial DNA segments can be used to properly identify species. The aim of the present study was to determine the reliability of the 635-bp-long cytochrome oxidase II gene (COII) in identification of forensically important flies. Forty-two specimens belonging to 11 species (Calliphoridae: Chrysomya albiceps, C. rufifacies, C. megacephala, Lucilia sericata, L. cuprina; Sarcophagidae: Sarcophaga carnaria, S. dux, S. albiceps, Wohlfahrtia nuba; Muscidae: Musca domestica, M. autumnalis) were analysed. The selected marker was amplified using PCR followed by sequencing. Nucleotide sequence divergences were calculated using the K2P (Kimura two-parameter) distance model, and a NJ (neighbour-joining) phylogenetic tree was constructed. All examined specimens were assigned to the correct species, formed distinct monophyletic clades and ordered in accordance with their taxonomic classification. Intraspecific variation ranged from 0 to 1% and interspecific variation occurred between 2 and 20%. The 635-bp-long COII marker is suitable for clear differentiation and identification of forensically relevant flies.

Inter-examiner reliability of a standardized Ultra-sonographic method for classification of changes related to supraspinatus tendinopathy – a pilot study

DEFF Research Database (Denmark)

Larsen, Camilla Marie; Ingwersen, Kim Gordon; Hjarnbæk, John

2015-01-01

Inter-examiner reliability of a standardized Ultra-sonographic method for classification of changes related to supraspinatus tendinopathy – a pilot study Ingwersen KG1, 2, Hjarbaek J3, Eshøj H1, Larsen CM1, 4, Vobbe J5, Juul-Kristensen B1, 6 1Institute of Sports Science and Clinical Biomechanics......, University of Southern Denmark, Odense, Denmark. 2Physiotherapy Department, Hospital Lillebaelt, Vejle Hospital, Vejle, Denmark 3Department of Radiology, Musculoskeletal section, Odense University Hospital, Odense, Denmark 4Health Sciences Research Centre, University College Lillebaelt, Odense Denmark 5...... athletes. For optimizing rehabilitation to the different stages of tendinopathy (1) ultra-sonography (US) may be used. Reliability of such method for RT is lacking. Aims. To develop and test inter-examiner reliability of US for classifying RT. Materials and Methods. A three-phased standardized protocol...

Standardizing foot-type classification using arch index values.

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Wong, Christopher Kevin; Weil, Rich; de Boer, Emily

2012-01-01

The lack of a reliable classification standard for foot type makes drawing conclusions from existing research and clinical decisions difficult, since different foot types may move and respond to treatment differently. The purpose of this study was to determine interrater agreement for foot-type classification based on photo-box-derived arch index values. For this correlational study with two raters, a sample of 11 healthy volunteers with normal to obese body mass indices was recruited from both a community weight-loss programme and a programme in physical therapy. Arch index was calculated using AutoCAD software from footprint photographs obtained via mirrored photo-box. Classification as high-arched, normal, or low-arched foot type was based on arch index values. Reliability of the arch index was determined with intra-class correlations; agreement on foot-type classification was determined using quadratic weighted kappa (κw). Average arch index was 0.215 for one tester and 0.219 for the second tester, with an overall range of 0.017 to 0.370. Both testers classified 6 feet as low-arched, 9 feet as normal, and 7 feet as high-arched. Interrater reliability for the arch index was ICC=0.90; interrater agreement for foot-type classification was κw=0.923. Classification of foot type based on arch index values derived from plantar footprint photographs obtained via mirrored photo-box showed excellent reliability in people with varying BMI. Foot-type classification may help clinicians and researchers subdivide sample populations to better differentiate mobility, gait, or treatment effects among foot types.

Standardizing Foot-Type Classification Using Arch Index Values

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Weil, Rich; de Boer, Emily

2012-01-01

ABSTRACT Purpose: The lack of a reliable classification standard for foot type makes drawing conclusions from existing research and clinical decisions difficult, since different foot types may move and respond to treatment differently. The purpose of this study was to determine interrater agreement for foot-type classification based on photo-box-derived arch index values. Method: For this correlational study with two raters, a sample of 11 healthy volunteers with normal to obese body mass indices was recruited from both a community weight-loss programme and a programme in physical therapy. Arch index was calculated using AutoCAD software from footprint photographs obtained via mirrored photo-box. Classification as high-arched, normal, or low-arched foot type was based on arch index values. Reliability of the arch index was determined with intra-class correlations; agreement on foot-type classification was determined using quadratic weighted kappa (κw). Results: Average arch index was 0.215 for one tester and 0.219 for the second tester, with an overall range of 0.017 to 0.370. Both testers classified 6 feet as low-arched, 9 feet as normal, and 7 feet as high-arched. Interrater reliability for the arch index was ICC=0.90; interrater agreement for foot-type classification was κw=0.923. Conclusions: Classification of foot type based on arch index values derived from plantar footprint photographs obtained via mirrored photo-box showed excellent reliability in people with varying BMI. Foot-type classification may help clinicians and researchers subdivide sample populations to better differentiate mobility, gait, or treatment effects among foot types. PMID:23729964

AAPT Diagnostic Criteria for Chronic Cancer Pain Conditions.

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Paice, Judith A; Mulvey, Matt; Bennett, Michael; Dougherty, Patrick M; Farrar, John T; Mantyh, Patrick W; Miaskowski, Christine; Schmidt, Brian; Smith, Thomas J

2017-03-01

Chronic cancer pain is a serious complication of malignancy or its treatment. Currently, no comprehensive, universally accepted cancer pain classification system exists. Clarity in classification of common cancer pain syndromes would improve clinical assessment and management. Moreover, an evidence-based taxonomy would enhance cancer pain research efforts by providing consistent diagnostic criteria, ensuring comparability across clinical trials. As part of a collaborative effort between the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION) and the American Pain Society (APS), the ACTTION-APS Pain Taxonomy initiative worked to develop the characteristics of an optimal diagnostic system. After the establishment of these characteristics, a working group consisting of clinicians and clinical and basic scientists with expertise in cancer and cancer-related pain was convened to generate core diagnostic criteria for an illustrative sample of 3 chronic pain syndromes associated with cancer (ie, bone pain and pancreatic cancer pain as models of pain related to a tumor) or its treatment (ie, chemotherapy-induced peripheral neuropathy). A systematic review and synthesis was conducted to provide evidence for the dimensions that comprise this cancer pain taxonomy. Future efforts will subject these diagnostic categories and criteria to systematic empirical evaluation of their feasibility, reliability, and validity and extension to other cancer-related pain syndromes. The ACTTION-APS chronic cancer pain taxonomy provides an evidence-based classification for 3 prevalent syndromes, namely malignant bone pain, pancreatic cancer pain, and chemotherapy-induced peripheral neuropathy. This taxonomy provides consistent diagnostic criteria, common features, comorbidities, consequences, and putative mechanisms for these potentially serious cancer pain conditions that can be extended and applied with other cancer

Clinical application of a microfluidic chip for immunocapture and quantification of circulating exosomes to assist breast cancer diagnosis and molecular classification.

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Fang, Shimeng; Tian, Hongzhu; Li, Xiancheng; Jin, Dong; Li, Xiaojie; Kong, Jing; Yang, Chun; Yang, Xuesong; Lu, Yao; Luo, Yong; Lin, Bingcheng; Niu, Weidong; Liu, Tingjiao

2017-01-01

Increasing attention has been attracted by exosomes in blood-based diagnosis because cancer cells release more exosomes in serum than normal cells and these exosomes overexpress a certain number of cancer-related biomarkers. However, capture and biomarker analysis of exosomes for clinical application are technically challenging. In this study, we developed a microfluidic chip for immunocapture and quantification of circulating exosomes from small sample volume and applied this device in clinical study. Circulating EpCAM-positive exosomes were measured in 6 cases breast cancer patients and 3 healthy controls to assist diagnosis. A significant increase in the EpCAM-positive exosome level in these patients was detected, compared to healthy controls. Furthermore, we quantified circulating HER2-positive exosomes in 19 cases of breast cancer patients for molecular classification. We demonstrated that the exosomal HER2 expression levels were almost consistent with that in tumor tissues assessed by immunohistochemical staining. The microfluidic chip might provide a new platform to assist breast cancer diagnosis and molecular classification.

Validation of the prognostic gene portfolio, ClinicoMolecular Triad Classification, using an independent prospective breast cancer cohort and external patient populations

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2014-01-01

Introduction Using genome-wide expression profiles of a prospective training cohort of breast cancer patients, ClinicoMolecular Triad Classification (CMTC) was recently developed to classify breast cancers into three clinically relevant groups to aid treatment decisions. CMTC was found to be both prognostic and predictive in a large external breast cancer cohort in that study. This study serves to validate the reproducibility of CMTC and its prognostic value using independent patient cohorts. Methods An independent internal cohort (n = 284) and a new external cohort (n = 2,181) were used to validate the association of CMTC between clinicopathological factors, 12 known gene signatures, two molecular subtype classifiers, and 19 oncogenic signalling pathway activities, and to reproduce the abilities of CMTC to predict clinical outcomes of breast cancer. In addition, we also updated the outcome data of the original training cohort (n = 147). Results The original training cohort reached a statistically significant difference (p risk groups. Conclusions Both prospective internal cohorts and the independent external cohorts reproduced the triad classification of CMTC and its prognostic significance. CMTC is an independent prognostic predictor, and it outperformed 12 other known prognostic gene signatures, molecular subtype classifications, and all other standard prognostic clinicopathological factors. Our results support further development of CMTC portfolio into a guide for personalized breast cancer treatments. PMID:24996446

Gene selection for cancer classification with the help of bees.

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Moosa, Johra Muhammad; Shakur, Rameen; Kaykobad, Mohammad; Rahman, Mohammad Sohel

2016-08-10

Development of biologically relevant models from gene expression data notably, microarray data has become a topic of great interest in the field of bioinformatics and clinical genetics and oncology. Only a small number of gene expression data compared to the total number of genes explored possess a significant correlation with a certain phenotype. Gene selection enables researchers to obtain substantial insight into the genetic nature of the disease and the mechanisms responsible for it. Besides improvement of the performance of cancer classification, it can also cut down the time and cost of medical diagnoses. This study presents a modified Artificial Bee Colony Algorithm (ABC) to select minimum number of genes that are deemed to be significant for cancer along with improvement of predictive accuracy. The search equation of ABC is believed to be good at exploration but poor at exploitation. To overcome this limitation we have modified the ABC algorithm by incorporating the concept of pheromones which is one of the major components of Ant Colony Optimization (ACO) algorithm and a new operation in which successive bees communicate to share their findings. The proposed algorithm is evaluated using a suite of ten publicly available datasets after the parameters are tuned scientifically with one of the datasets. Obtained results are compared to other works that used the same datasets. The performance of the proposed method is proved to be superior. The method presented in this paper can provide subset of genes leading to more accurate classification results while the number of selected genes is smaller. Additionally, the proposed modified Artificial Bee Colony Algorithm could conceivably be applied to problems in other areas as well.

FEATURE EXTRACTION BASED WAVELET TRANSFORM IN BREAST CANCER DIAGNOSIS USING FUZZY AND NON-FUZZY CLASSIFICATION

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Pelin GORGEL

2013-01-01

Full Text Available This study helps to provide a second eye to the expert radiologists for the classification of manually extracted breast masses taken from 60 digital mammıgrams. These mammograms have been acquired from Istanbul University Faculty of Medicine Hospital and have 78 masses. The diagnosis is implemented with pre-processing by using feature extraction based Fast Wavelet Transform (FWT. Afterwards Adaptive Neuro-Fuzzy Inference System (ANFIS based fuzzy subtractive clustering and Support Vector Machines (SVM methods are used for the classification. It is a comparative study which uses these methods respectively. According to the results of the study, ANFIS based subtractive clustering produces ??% while SVM produces ??% accuracy in malignant-benign classification. The results demonstrate that the developed system could help the radiologists for a true diagnosis and decrease the number of the missing cancerous regions or unnecessary biopsies.

Assessment of fatty degeneration of the gluteal muscles in patients with THA using MRI: reliability and accuracy of the Goutallier and quartile classification systems.

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Engelken, Florian; Wassilew, Georgi I; Köhlitz, Torsten; Brockhaus, Sebastian; Hamm, Bernd; Perka, Carsten; Diederichs, und Gerd

2014-01-01

The purpose of this study was to quantify the performance of the Goutallier classification for assessing fatty degeneration of the gluteus muscles from magnetic resonance (MR) images and to compare its performance to a newly proposed system. Eighty-four hips with clinical signs of gluteal insufficiency and 50 hips from asymptomatic controls were analyzed using a standard classification system (Goutallier) and a new scoring system (Quartile). Interobserver reliability and intraobserver repeatability were determined, and accuracy was assessed by comparing readers' scores with quantitative estimates of the proportion of intramuscular fat based on MR signal intensities (gold standard). The existing Goutallier classification system and the new Quartile system performed equally well in assessing fatty degeneration of the gluteus muscles, both showing excellent levels of interrater and intrarater agreement. While the Goutallier classification system has the advantage of being widely known, the benefit of the Quartile system is that it is based on more clearly defined grades of fatty degeneration. Copyright © 2014 Elsevier Inc. All rights reserved.

Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin

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Hoadley, Katherine A; Yau, Christina; Wolf, Denise M

2014-01-01

Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform...... on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset...

Does the Modified Gartland Classification Clarify Decision Making?

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Leung, Sophia; Paryavi, Ebrahim; Herman, Martin J; Sponseller, Paul D; Abzug, Joshua M

2018-01-01

The modified Gartland classification system for pediatric supracondylar fractures is often utilized as a communication tool to aid in determining whether or not a fracture warrants operative intervention. This study sought to determine the interobserver and intraobserver reliability of the Gartland classification system, as well as to determine whether there was agreement that a fracture warranted operative intervention regardless of the classification system. A total of 200 anteroposterior and lateral radiographs of pediatric supracondylar humerus fractures were retrospectively reviewed by 3 fellowship-trained pediatric orthopaedic surgeons and 2 orthopaedic residents and then classified as type I, IIa, IIb, or III. The surgeons then recorded whether they would treat the fracture nonoperatively or operatively. The κ coefficients were calculated to determine interobserver and intraobserver reliability. Overall, the Wilkins-modified Gartland classification has low-moderate interobserver reliability (κ=0.475) and high intraobserver reliability (κ=0.777). A low interobserver reliability was found when differentiating between type IIa and IIb (κ=0.240) among attendings. There was moderate-high interobserver reliability for the decision to operate (κ=0.691) and high intraobserver reliability (κ=0.760). Decreased interobserver reliability was present for decision to operate among residents. For fractures classified as type I, the decision to operate was made 3% of the time and 27% for type IIa. The decision was made to operate 99% of the time for type IIb and 100% for type III. There is almost full agreement for the nonoperative treatment of Type I fractures and operative treatment for type III fractures. There is agreement that type IIb fractures should be treated operatively and that the majority of type IIa fractures should be treated nonoperatively. However, the interobserver reliability for differentiating between type IIa and IIb fractures is low. Our results

Reliability of a Novel CBCT-Based 3D Classification System for Maxillary Canine Impactions in Orthodontics: The KPG Index

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Visconti, Luca; Martin, Conchita

2013-01-01

The aim of this study was to evaluate both intra- and interoperator reliability of a radiological three-dimensional classification system (KPG index) for the assessment of degree of difficulty for orthodontic treatment of maxillary canine impactions. Cone beam computed tomography (CBCT) scans of fifty impacted canines, obtained using three different scanners (NewTom, Kodak, and Planmeca), were classified using the KPG index by three independent orthodontists. Measurements were repeated one month later. Based on these two sessions, several recommendations on KPG Index scoring were elaborated. After a joint calibration session, these recommendations were explained to nine orthodontists and the two measurement sessions were repeated. There was a moderate intrarater agreement in the precalibration measurement sessions. After the calibration session, both intra- and interrater agreement were almost perfect. Indexes assessed with Kodak Dental Imaging 3D module software showed a better reliability in z-axis values, whereas indexes assessed with Planmeca Romexis software showed a better reliability in x- and y-axis values. No differences were found between the CBCT scanners used. Taken together, these findings indicate that the application of the instructions elaborated during this study improved KPG index reliability, which was nevertheless variously influenced by the use of different software for images evaluation. PMID:24235889

Reliability of a Novel CBCT-Based 3D Classification System for Maxillary Canine Impactions in Orthodontics: The KPG Index

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Domenico Dalessandri

2013-01-01

Full Text Available The aim of this study was to evaluate both intra- and interoperator reliability of a radiological three-dimensional classification system (KPG index for the assessment of degree of difficulty for orthodontic treatment of maxillary canine impactions. Cone beam computed tomography (CBCT scans of fifty impacted canines, obtained using three different scanners (NewTom, Kodak, and Planmeca, were classified using the KPG index by three independent orthodontists. Measurements were repeated one month later. Based on these two sessions, several recommendations on KPG Index scoring were elaborated. After a joint calibration session, these recommendations were explained to nine orthodontists and the two measurement sessions were repeated. There was a moderate intrarater agreement in the precalibration measurement sessions. After the calibration session, both intra- and interrater agreement were almost perfect. Indexes assessed with Kodak Dental Imaging 3D module software showed a better reliability in z-axis values, whereas indexes assessed with Planmeca Romexis software showed a better reliability in x- and y-axis values. No differences were found between the CBCT scanners used. Taken together, these findings indicate that the application of the instructions elaborated during this study improved KPG index reliability, which was nevertheless variously influenced by the use of different software for images evaluation.

Body mass index: different nutritional status according to WHO, OPAS and Lipschitz classifications in gastrointestinal cancer patients.

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Barao, Katia; Forones, Nora Manoukian

2012-01-01

The body mass index (BMI) is the most common marker used on diagnoses of the nutritional status. The great advantage of this index is the easy way to measure, the low cost, the good correlation with the fat mass and the association to morbidity and mortality. To compare the BMI differences according to the WHO, OPAS and Lipschitz classification. A prospective study on 352 patients with esophageal, gastric or colorectal cancer was done. The BMI was calculated and analyzed by the classification of WHO, Lipschitz and OPAS. The mean age was 62.1 ± 12.4 years and 59% of them had more than 59 years. The BMI had not difference between the genders in patients cancer had more than 65 years. A different cut off must be used for this patients, because undernourished patients may be wrongly considered well nourished.

Inter-tester reliability of a new diagnostic classification system for patients with non-specific low back pain

DEFF Research Database (Denmark)

Petersen, Tom Erik; Olsen, Steen; Laslett, Mark

2004-01-01

Most patients referred to physiotherapy with low back pain are without a precise medical diagnosis. Identification of subgroups of non-specific low back pain patients may improve clinical outcomes and research efficiency. A pathoanatomic classification system has been developed, classifying...... modest level of total agreement (39%) for the system as a whole might indicate that the utility of the system for general screening purposes is limited, compared with the utility in identification of particular syndromes. Due to low prevalence of positive findings in some of the syndromes, future work...... should focus on testing reliability on a larger sample of patients, and testing of validity and feasibility of the system....

Developing and validating the Communication Function Classification System for individuals with cerebral palsy

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HIDECKER, MARY JO COOLEY; PANETH, NIGEL; ROSENBAUM, PETER L; KENT, RAYMOND D; LILLIE, JANET; EULENBERG, JOHN B; CHESTER, KEN; JOHNSON, BRENDA; MICHALSEN, LAUREN; EVATT, MORGAN; TAYLOR, KARA

2011-01-01

Aim The purpose of this study was to create and validate a Communication Function Classification System (CFCS) for children with cerebral palsy (CP) that can be used by a wide variety of individuals who are interested in CP. This paper reports the content validity, interrater reliability, and test–retest reliability of the CFCS for children with CP. Method An 11-member development team created comprehensive descriptions of the CFCS levels, and four nominal groups comprising 27 participants critiqued these levels. Within a Delphi survey, 112 participants commented on the clarity and usefulness of the CFCS. Interrater reliability was completed by 61 professionals and 68 parents/relatives who classified 69 children with CP aged 2 to 18 years. Test–retest reliability was completed by 48 professionals who allowed at least 2 weeks between classifications. The participants who assessed the CFCS were all relevant stakeholders: adults with CP, parents of children with CP, educators, occupational therapists, physical therapists, physicians, and speech–language pathologists. Results The interrater reliability of the CFCS was 0.66 between two professionals and 0.49 between a parent and a professional. Professional interrater reliability improved to 0.77 for classification of children older than 4 years. The test–retest reliability was 0.82. Interpretation The CFCS demonstrates content validity and shows very good test–retest reliability, good professional interrater reliability, and moderate parent–professional interrater reliability. Combining the CFCS with the Gross Motor Function Classification System and the Manual Ability Classification System contributes to a functional performance view of daily life for individuals with CP, in accordance with the World Health Organization’s International Classification of Functioning, Disability and Health. PMID:21707596

Land-cover classification with an expert classification algorithm using digital aerial photographs

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José L. de la Cruz

2010-05-01

Full Text Available The purpose of this study was to evaluate the usefulness of the spectral information of digital aerial sensors in determining land-cover classification using new digital techniques. The land covers that have been evaluated are the following, (1 bare soil, (2 cereals, including maize (Zea mays L., oats (Avena sativa L., rye (Secale cereale L., wheat (Triticum aestivum L. and barley (Hordeun vulgare L., (3 high protein crops, such as peas (Pisum sativum L. and beans (Vicia faba L., (4 alfalfa (Medicago sativa L., (5 woodlands and scrublands, including holly oak (Quercus ilex L. and common retama (Retama sphaerocarpa L., (6 urban soil, (7 olive groves (Olea europaea L. and (8 burnt crop stubble. The best result was obtained using an expert classification algorithm, achieving a reliability rate of 95%. This result showed that the images of digital airborne sensors hold considerable promise for the future in the field of digital classifications because these images contain valuable information that takes advantage of the geometric viewpoint. Moreover, new classification techniques reduce problems encountered using high-resolution images; while reliabilities are achieved that are better than those achieved with traditional methods.

On the classification techniques in data mining for microarray data classification

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Aydadenta, Husna; Adiwijaya

2018-03-01

Cancer is one of the deadly diseases, according to data from WHO by 2015 there are 8.8 million more deaths caused by cancer, and this will increase every year if not resolved earlier. Microarray data has become one of the most popular cancer-identification studies in the field of health, since microarray data can be used to look at levels of gene expression in certain cell samples that serve to analyze thousands of genes simultaneously. By using data mining technique, we can classify the sample of microarray data thus it can be identified with cancer or not. In this paper we will discuss some research using some data mining techniques using microarray data, such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5, and simulation of Random Forest algorithm with technique of reduction dimension using Relief. The result of this paper show performance measure (accuracy) from classification algorithm (SVM, ANN, Naive Bayes, kNN, C4.5, and Random Forets).The results in this paper show the accuracy of Random Forest algorithm higher than other classification algorithms (Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5). It is hoped that this paper can provide some information about the speed, accuracy, performance and computational cost generated from each Data Mining Classification Technique based on microarray data.

Clinical classification of cancer cachexia: phenotypic correlates in human skeletal muscle.

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Neil Johns

Full Text Available BACKGROUND: Cachexia affects the majority of patients with advanced cancer and is associated with a reduction in treatment tolerance, response to therapy, and duration of survival. One impediment towards the effective treatment of cachexia is a validated classification system. METHODS: 41 patients with resectable upper gastrointestinal (GI or pancreatic cancer underwent characterisation for cachexia based on weight-loss (WL and/or low muscularity (LM. Four diagnostic criteria were used >5%WL, >10%WL, LM, and LM+>2%WL. All patients underwent biopsy of the rectus muscle. Analysis included immunohistochemistry for fibre size and type, protein and nucleic acid concentration, Western blots for markers of autophagy, SMAD signalling, and inflammation. FINDINGS: Compared with non-cachectic cancer patients, patients with LM or LM+>2%WL, mean muscle fibre diameter was reduced by about 25% (p = 0.02 and p = 0.001 respectively. No significant difference in fibre diameter was observed if patients had WL alone. Regardless of classification, there was no difference in fibre number or proportion of fibre type across all myosin heavy chain isoforms. Mean muscle protein content was reduced and the ratio of RNA/DNA decreased in patients with either >5%WL or LM+>2%WL. Compared with non-cachectic patients, SMAD3 protein levels were increased in patients with >5%WL (p = 0.022 and with >10%WL, beclin (p = 0.05 and ATG5 (p = 0.01 protein levels were increased. There were no differences in phospho-NFkB or phospho-STAT3 levels across any of the groups. CONCLUSION: Muscle fibre size, biochemical composition and pathway phenotype can vary according to whether the diagnostic criteria for cachexia are based on weight loss alone, a measure of low muscularity alone or a combination of the two. For intervention trials where the primary end-point is a change in muscle mass or function, use of combined diagnostic criteria may allow identification of a more

Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles

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Wong G William

2008-06-01

Full Text Available Abstract Background Pancreatic cancer is the fourth leading cause of cancer death in the United States. Consequently, identification of clinically relevant biomarkers for the early detection of this cancer type is urgently needed. In recent years, proteomics profiling techniques combined with various data analysis methods have been successfully used to gain critical insights into processes and mechanisms underlying pathologic conditions, particularly as they relate to cancer. However, the high dimensionality of proteomics data combined with their relatively small sample sizes poses a significant challenge to current data mining methodology where many of the standard methods cannot be applied directly. Here, we propose a novel methodological framework using machine learning method, in which decision tree based classifier ensembles coupled with feature selection methods, is applied to proteomics data generated from premalignant pancreatic cancer. Results This study explores the utility of three different feature selection schemas (Student t test, Wilcoxon rank sum test and genetic algorithm to reduce the high dimensionality of a pancreatic cancer proteomic dataset. Using the top features selected from each method, we compared the prediction performances of a single decision tree algorithm C4.5 with six different decision-tree based classifier ensembles (Random forest, Stacked generalization, Bagging, Adaboost, Logitboost and Multiboost. We show that ensemble classifiers always outperform single decision tree classifier in having greater accuracies and smaller prediction errors when applied to a pancreatic cancer proteomics dataset. Conclusion In our cross validation framework, classifier ensembles generally have better classification accuracies compared to that of a single decision tree when applied to a pancreatic cancer proteomic dataset, thus suggesting its utility in future proteomics data analysis. Additionally, the use of feature selection

Mastectomy or breast conserving surgery? Factors affecting type of surgical treatment for breast cancer – a classification tree approach

International Nuclear Information System (INIS)

Martin, Michael A; Meyricke, Ramona; O'Neill, Terry; Roberts, Steven

2006-01-01

A critical choice facing breast cancer patients is which surgical treatment – mastectomy or breast conserving surgery (BCS) – is most appropriate. Several studies have investigated factors that impact the type of surgery chosen, identifying features such as place of residence, age at diagnosis, tumor size, socio-economic and racial/ethnic elements as relevant. Such assessment of 'propensity' is important in understanding issues such as a reported under-utilisation of BCS among women for whom such treatment was not contraindicated. Using Western Australian (WA) data, we further examine the factors associated with the type of surgical treatment for breast cancer using a classification tree approach. This approach deals naturally with complicated interactions between factors, and so allows flexible and interpretable models for treatment choice to be built that add to the current understanding of this complex decision process. Data was extracted from the WA Cancer Registry on women diagnosed with breast cancer in WA from 1990 to 2000. Subjects' treatment preferences were predicted from covariates using both classification trees and logistic regression. Tumor size was the primary determinant of patient choice, subjects with tumors smaller than 20 mm in diameter preferring BCS. For subjects with tumors greater than 20 mm in diameter factors such as patient age, nodal status, and tumor histology become relevant as predictors of patient choice. Classification trees perform as well as logistic regression for predicting patient choice, but are much easier to interpret for clinical use. The selected tree can inform clinicians' advice to patients

Restaging and Survival Analysis of 4036 Ovarian Cancer Patients According to the 2013 FIGO Classification for Ovarian, Fallopian Tube, and Primary Peritoneal Cancer

DEFF Research Database (Denmark)

Rosendahl, Mikkel; Høgdall, Claus Kim; Mosgaard, Berit Jul

2016-01-01

OBJECTIVE: With the 2013 International Federation of Gynecology and Obstetrics (FIGO) staging for ovarian, fallopian tube, and primary peritoneal cancer, the number of substages changed from 10 to 14. Any classification of a malignancy should easily assign patients to prognostic groups, refer....... MATERIALS AND METHODS: Demographic, surgical, histological, and survival data from 4036 ovarian cancer patients were used in the analysis. Five-year survival rates (5YSR) and hazard ratios for the old and revised FIGO staging were calculated using Kaplan-Meier curves and Cox regression. RESULTS: A total...

Reliable classification of moving waste materials with LIBS in concrete recycling.

Science.gov (United States)

Xia, Han; Bakker, M C M

2014-03-01

Effective discrimination between different waste materials is of paramount importance for inline quality inspection of recycle concrete aggregates from demolished buildings. The moving targeted materials in the concrete waste stream are wood, PVC, gypsum block, glass, brick, steel rebar, aggregate and cement paste. For each material, up to three different types were considered, while thirty particles of each material were selected. Proposed is a reliable classification methodology based on integration of the LIBS spectral emissions in a fixed time window, starting from the deployment of the laser shot. PLS-DA (multi class) and the hybrid combination PCA-Adaboost (binary class) were investigated as efficient classifiers. In addition, mean centre and auto scaling approaches were compared for both classifiers. Using 72 training spectra and 18 test spectra per material, each averaged by ten shots, only PLS-DA achieved full discrimination, and the mean centre approach made it slightly more robust. Continuing with PLS-DA, the relation between data averaging and convergence to 0.3% average error was investigated using 9-fold cross-validations. Single-shot PLS-DA presented the highest challenge and most desirable methodology, which converged with 59 PC. The degree of success in practical testing will depend on the quality of the training set and the implications of the possibly remaining false positives. © 2013 Published by Elsevier B.V.

Clinical significance of combined detection of CYFRA21-1, NSE and CEA in classification and staging of patients with lung cancer

International Nuclear Information System (INIS)

Hu He; Li Yanhua; Liang Weida; Zhang Qin

2011-01-01

To explore clinical value of combined detection of CYFRA21-1, NSE and CEA in classification and staging of patients with lung cancer, the CYFRA21-1, NSE and CEA levels in pleural effusion in 330 patients with lung cancer and in 43 patients with benign were detected by the electrochemiluminescence. The results showed that CYFRA21-1, NSE and CEA levels in pleural effusion in patients with lung cancer group were significantly higher than that of in benign group (P<0.01). The positive rate of tumor markers in different pathological type lung cancer were different,which CYFRA21-1 positive rate in squamous cell cancer group was highest with 65.5%; CEA positive rate in glands cancer group was supreme with 65.0%; the NSE positive rate in differentiation cancer group was highest with 79.5%. The positive rate in three markers combined detection was higher than that in one item detection. The tumor marker levels in lung cancer were positively related with clinical staging. The higher of tumor marker levels and the more late of clinical staging, and the clinical III∼IV period was obviously higher than that I∼II period (P<0.05). The combined detection of CYFRA21-1, NSE and CEA may enhance the positive rate in lung cancer detection, and may have significant clinical value in the classification and staging of patients with lung cancer. (authors)

The correlation study of radiological findings with pathological classification of superficial depressed (IIc type) early gastric cancer

International Nuclear Information System (INIS)

Liu Linxiang; Deng Bingxing; Liu Yujin; Iinuma, G.; Moriyama, N.

2007-01-01

Objective: To investigate the relations between radiological findings and pathological classification of superficial depressed (II c type) early gastric cancer. Methods: Radiological features in subtonic double contrast barium examination and the endoscopic pictures of early gastric cancer compared with the global pathological specimens and micro-pathological features were prospectively studied. Combined with the gastric endoscopic pictures, the sharpness of margin of the lesions, the changes of converging mucosal folds and the changes of the depressed surface on the film of double contrast barium examination were analyzed. The correlation between the radiological features and histological classification of gastric cancer including well differentiated tubular adenocarcinoma (tub1), moderately differentiated tubular adenocarcinoma (tub2), poorly differentiated adenocarcinoma (por) and signet-ring cell carcinoma (sig) were studied. Results: In 102 cases of II c type early gastric cancer, there were tub1 27 cases, tub2 11, por 26 and sig 38 cases histologically. The margin of the depressed lesions of tubl (24 cases) and tub2 (9 cases) cancers were mostly unsharply demarcated or with fine spicular border, while the margin of lesions of por(15 cases) and sig(31 cases) were mostly clearly and sharply demarcated, with statistical significance (P<0.01). The depressed surface of tub1 and tub2 lesions (17 cases) revealed little unevenness, sometimes with evenly granulations, single nodule and scar-like depression, while that of por and sig lesions (41 cases) manifested as nodules of varying sizes, with statistical significance (P<0.01). Conclusion: The radiological findings of superficial depressed early gastric cancer in different histological types were different, the possible histological type could be speculated according to the radiological findings of the lesions. (authors)

AOSpine subaxial cervical spine injury classification system

NARCIS (Netherlands)

Vaccaro, Alexander R.; Koerner, John D.; Radcliff, Kris E.; Oner, F. Cumhur; Reinhold, Maximilian; Schnake, Klaus J.; Kandziora, Frank; Fehlings, Michael G.; Dvorak, Marcel F.; Aarabi, Bizhan; Rajasekaran, Shanmuganathan; Schroeder, Gregory D.; Kepler, Christopher K.; Vialle, Luiz R.

2016-01-01

Purpose: This project describes a morphology-based subaxial cervical spine traumatic injury classification system. Using the same approach as the thoracolumbar system, the goal was to develop a comprehensive yet simple classification system with high intra- and interobserver reliability to be used

Comparison of Computational Algorithms for the Classification of Liver Cancer using SELDI Mass Spectrometry: A Case Study

Directory of Open Access Journals (Sweden)

Robert J Hickey

2007-01-01

Full Text Available Introduction: As an alternative to DNA microarrays, mass spectrometry based analysis of proteomic patterns has shown great potential in cancer diagnosis. The ultimate application of this technique in clinical settings relies on the advancement of the technology itself and the maturity of the computational tools used to analyze the data. A number of computational algorithms constructed on different principles are available for the classification of disease status based on proteomic patterns. Nevertheless, few studies have addressed the difference in the performance of these approaches. In this report, we describe a comparative case study on the classification accuracy of hepatocellular carcinoma based on the serum proteomic pattern generated from a Surface Enhanced Laser Desorption/Ionization (SELDI mass spectrometer.Methods: Nine supervised classifi cation algorithms are implemented in R software and compared for the classification accuracy.Results: We found that the support vector machine with radial function is preferable as a tool for classification of hepatocellular carcinoma using features in SELDI mass spectra. Among the rest of the methods, random forest and prediction analysis of microarrays have better performance. A permutation-based technique reveals that the support vector machine with a radial function seems intrinsically superior in learning from the training data since it has a lower prediction error than others when there is essentially no differential signal. On the other hand, the performance of the random forest and prediction analysis of microarrays rely on their capability of capturing the signals with substantial differentiation between groups.Conclusions: Our finding is similar to a previous study, where classification methods based on the Matrix Assisted Laser Desorption/Ionization (MALDI mass spectrometry are compared for the prediction accuracy of ovarian cancer. The support vector machine, random forest and prediction

Reliable classification of facial phenotypic variation in craniofacial microsomia: a comparison of physical exam and photographs.

Science.gov (United States)

Birgfeld, Craig B; Heike, Carrie L; Saltzman, Babette S; Leroux, Brian G; Evans, Kelly N; Luquetti, Daniela V

2016-03-31

Craniofacial microsomia is a common congenital condition for which children receive longitudinal, multidisciplinary team care. However, little is known about the etiology of craniofacial microsomia and few outcome studies have been published. In order to facilitate large, multicenter studies in craniofacial microsomia, we assessed the reliability of phenotypic classification based on photographs by comparison with direct physical examination. Thirty-nine children with craniofacial microsomia underwent a physical examination and photographs according to a standardized protocol. Three clinicians completed ratings during the physical examination and, at least a month later, using respective photographs for each participant. We used descriptive statistics for participant characteristics and intraclass correlation coefficients (ICCs) to assess reliability. The agreement between ratings on photographs and physical exam was greater than 80 % for all 15 categories included in the analysis. The ICC estimates were higher than 0.6 for most features. Features with the highest ICC included: presence of epibulbar dermoids, ear abnormalities, and colobomas (ICC 0.85, 0.81, and 0.80, respectively). Orbital size, presence of pits, tongue abnormalities, and strabismus had the lowest ICC, values (0.17 or less). There was not a strong tendency for either type of rating, physical exam or photograph, to be more likely to designate a feature as abnormal. The agreement between photographs and physical exam regarding the presence of a prior surgery was greater than 90 % for most features. Our results suggest that categorization of facial phenotype in children with CFM based on photographs is reliable relative to physical examination for most facial features.

ParSel: Parallel Selection of Micro-RNAs for Survival Classification in Cancers.

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Sinha, Debajyoti; Sengupta, Debarka; Bandyopadhyay, Sanghamitra

2017-07-01

It is known that tumor micro-RNAs (miRNA) can define patient survival and treatment response. We present a framework to identify miRNAs which are predictive of cancer survival. The framework attempts to rank the miRNAs by exploring their collaborative role in gene regulation. Our approach tests a significantly large number of combinatorial cases leveraging parallel computation. We carefully avoided parametric assumptions involved in evaluations of miRNA expressions but used rigorous statistical computation to assign an importance score to a miRNA. Experimental results on three cancer types namely, KIRC, OV and GBM verify that the top ranked miRNAs obtained using the proposed framework produce better classification accuracy as compared to some best practice variable selection methods. Some of these top ranked miRNA are also known to be associated with related diseases. © 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim.

Radiomic Machine Learning Classifiers for Prognostic Biomarkers of Head & Neck Cancer

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Chintan eParmar

2015-12-01

Full Text Available Introduction: Radiomics extracts and mines large number of medical imaging features in a non-invasive and cost-effective way. The underlying assumption of radiomics is that these imaging features quantify phenotypic characteristics of entire tumor. In order to enhance applicability of radiomics in clinical oncology, highly accurate and reliable machine learning approaches are required. In this radiomic study, thirteen feature selection methods and eleven machine learning classification methods were evaluated in terms of their performance and stability for predicting overall survival in head and neck cancer patients. Methods: Two independent head and neck cancer cohorts were investigated. Training cohort HN1 consisted 101 HNSCC patients. Cohort HN2 (n=95 was used for validation. A total of 440 radiomic features were extracted from the segmented tumor regions in CT images. Feature selection and classification methods were compared using an unbiased evaluation framework. Results: We observed that the three feature selection methods MRMR (AUC = 0.69, Stability = 0.66, MIFS (AUC = 0.66, Stability = 0.69, and CIFE (AUC = 0.68, Stability = 0.7 had high prognostic performance and stability. The three classifiers BY (AUC = 0.67, RSD = 11.28, RF (AUC = 0.61, RSD = 7.36, and NN (AUC = 0.62, RSD = 10.52 also showed high prognostic performance and stability. Analysis investigating performance variability indicated that the choice of classification method is the major factor driving the performance variation (29.02% of total variance. Conclusions: Our study identified prognostic and reliable machine learning methods for the prediction of overall survival of head and neck cancer patients. Identification of optimal machine-learning methods for radiomics based prognostic analyses could broaden the scope of radiomics in precision oncology and cancer care.

Correcting Fallacies in Validity, Reliability, and Classification

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Sijtsma, Klaas

2009-01-01

This article reviews three topics from test theory that continue to raise discussion and controversy and capture test theorists' and constructors' interest. The first topic concerns the discussion of the methodology of investigating and establishing construct validity; the second topic concerns reliability and its misuse, alternative definitions…

Gene selection and classification for cancer microarray data based on machine learning and similarity measures

Directory of Open Access Journals (Sweden)

Liu Qingzhong

2011-12-01

Full Text Available Abstract Background Microarray data have a high dimension of variables and a small sample size. In microarray data analyses, two important issues are how to choose genes, which provide reliable and good prediction for disease status, and how to determine the final gene set that is best for classification. Associations among genetic markers mean one can exploit information redundancy to potentially reduce classification cost in terms of time and money. Results To deal with redundant information and improve classification, we propose a gene selection method, Recursive Feature Addition, which combines supervised learning and statistical similarity measures. To determine the final optimal gene set for prediction and classification, we propose an algorithm, Lagging Prediction Peephole Optimization. By using six benchmark microarray gene expression data sets, we compared Recursive Feature Addition with recently developed gene selection methods: Support Vector Machine Recursive Feature Elimination, Leave-One-Out Calculation Sequential Forward Selection and several others. Conclusions On average, with the use of popular learning machines including Nearest Mean Scaled Classifier, Support Vector Machine, Naive Bayes Classifier and Random Forest, Recursive Feature Addition outperformed other methods. Our studies also showed that Lagging Prediction Peephole Optimization is superior to random strategy; Recursive Feature Addition with Lagging Prediction Peephole Optimization obtained better testing accuracies than the gene selection method varSelRF.

Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers

Energy Technology Data Exchange (ETDEWEB)

Eswari J, Satya; Chandrakar, Neha [National Institute of Technology Raipur, Raipur (India)

2016-04-15

Artificial neural networks (ANNs) can be used to develop a technique to classify lymph node negative breast cancer that is prone to distant metastases based on gene expression signatures. The neural network used is a multilayered feed forward network that employs back propagation algorithm. Once trained with DNA microarraybased gene expression profiles of genes that were predictive of distant metastasis recurrence of lymph node negative breast cancer, the ANNs became capable of correctly classifying all samples and recognizing the genes most appropriate to the classification. To test the ability of the trained ANN models in recognizing lymph node negative breast cancer, we analyzed additional idle samples that were not used beforehand for the training procedure and obtained the correctly classified result in the validation set. For more substantial result, bootstrapping of training and testing dataset was performed as external validation. This study illustrates the potential application of ANN for breast tumor diagnosis and the identification of candidate targets in patients for therapy.

Esophagus cancer

International Nuclear Information System (INIS)

Anon.

1989-01-01

Ways of metastatic spreading of esophagus cancer, depending on segmental division of esophagus are considered. Classification of esophagus cancer according to morphological structure, domestic clinical classification according to stages and international classification according to TNM system are presented. Diagnosis of esophagus cancer should be complex and based on results of clinical examination of patients, radiological, endoscopic and morphological investigations. Radiological, surgical and combined (preoperative radiotherapy with successive operation) methods of treatment are used in the case of esophagus cancer. Versions of preoperative radiotherapy are given. Favourable results of applying combined surgical treatment with preoperative radiotherapy are shown

COII ”long fragment” reliability in characterisation and classification of forensically important flies

Directory of Open Access Journals (Sweden)

Sanaa M. Aly

2017-01-01

Full Text Available Introduction : Molecular identification of collected flies is important in forensic entomological analysis guided with accurate evaluation of the chosen genetic marker. The selected mitochondrial DNA segments can be used to properly identify species. The aim of the present study was to determine the reliability of the 635-bp-long cytochrome oxidase II gene (COII in identification of forensically important flies. Material and methods: Forty-two specimens belonging to 11 species ( Calliphoridae: Chrysomya albiceps , C. rufifacies , C. megacephala , Lucilia sericata , L. cuprina ; Sarcophagidae: Sarcophaga carnaria , S. dux , S. albiceps , Wohlfahrtia nuba ; Muscidae: Musca domestica , M. autumnalis were analysed. The selected marker was amplified using PCR followed by sequencing. Nucleotide sequence divergences were calculated using the K2P (Kimura two-parameter distance model, and a NJ (neighbour-joining phylogenetic tree was constructed. Results : All examined specimens were assigned to the correct species, formed distinct monophyletic clades and ordered in accordance with their taxonomic classification. Intraspecific variation ranged from 0 to 1% and interspecific variation occurred between 2 and 20%. Conclusions : The 635-bp-long COII marker is suitable for clear differentiation and identification of forensically relevant flies.

Standardization of the Korean version of Mini-Mental Adjustment to Cancer (K-Mini-MAC) scale: factor structure, reliability and validity.

Science.gov (United States)

Kang, Jee In; Chung, Hyun Cheol; Kim, Se Joo; Choi, Hye Jin; Ahn, Joong Bae; Jeung, Hei-Cheul; Namkoong, Kee

2008-06-01

Mental adjustment and coping affect the physical outcome and survival as well as quality of life in cancer patients. The Mini-Mental Adjustment to Cancer (Mini-MAC) scale is a new refined, economical and reliable self-rating instrument measuring cognitive and behavioral responses to cancer. The aim of this study was to evaluate the psychometric properties of the Mini-MAC in Korean cancer patients. A total of 208 cancer patients recruited from the Yonsei Cancer Center were assessed with the Mini-MAC and the Hospital Anxiety and Depression Scale (HADS). Principal component analysis with varimax rotation for the Korean version of Mini-MAC (K-Mini-MAC) confirmed four factors. Three had psychometric properties similar to Helpless-Hopeless (HH), Anxious Preoccupation (AP) and Cognitive Avoidance (CA) of the original Mini-MAC. A novel factor, named Positive Attitude, included items of both Fatalism (FA) and Fighting Spirit (FS) from the original version. The five subscales from the original version (AP, HH, FS, FA and CA) and Positive Attitude had acceptable internal reliabilities in our sample (Cronbach's alpha coefficient 0.50-0.86; correlation coefficient of test-retest 0.68-0.88). For the validity, significant interscale correlation was observed in the original five subscales and Positive Attitude. Each subscale including Positive Attitude was also significantly related to Depression and Anxiety of HADS. As a whole, the K-Mini-MAC was a reliable, valid and acceptable tool for Korean cancer patients. These findings can provide information about the cross-cultural validity of Mini-MAC scale's factor structure. Cultural differences were also discussed.

Are preoperative histology and MRI useful for classification of endometrial cancer risk?

International Nuclear Information System (INIS)

Body, Noemie; Lavoué, Vincent; De Kerdaniel, Olivier; Foucher, Fabrice; Henno, Sébastien; Cauchois, Aurélie; Laviolle, Bruno; Leblanc, Marc; Levêque, Jean

2016-01-01

The 2010 guidelines of the French National Cancer Institute (INCa) classify patients with endometrial cancer into three risk groups for lymph node invasion and recurrence on the basis of MRI and histological analysis of an endometrial specimen obtained preoperatively. The classification guides therapeutic choices, which may include pelvic and/or para-aortic lymphadenectomy. The purpose of this study was to evaluate the diagnostic performance of preoperative assessment to help identify intermediate- or high-risk patients requiring lymphadenectomy. The study included all patients who underwent surgery for endometrial cancer between January 2010 and December 2013 at either Rennes University Hospital or Vannes Regional Hospital. The criteria for eligibility included a preoperative assessment with MRI and histological examination of an endometrial sample. A histological comparison was made between the preoperative and surgical specimens. Among the 91 patients who underwent a full preoperative assessment, the diagnosis of intermediate- or high-risk endometrial cancer was established by MRI and histology with a sensitivity of 70 %, specificity of 82 %, positive predictive value (PPV) of 87 %, negative predictive value (NPV) of 61 %, positive likelihood ratio (LR+) of 3.8 and negative likelihood ratio (LR-) of 0.3. The risk group was underestimated in 32 % of patients and overestimated in 7 % of patients. MRI underestimated endometrial cancer stage in 20 % of cases, while endometrial sampling underestimated the histological type in 4 % of cases and the grade in 9 % of cases. The preoperative assessment overestimated or underestimated the risk of recurrence in nearly 40 % of cases, with errors in lesion type, grade or stage. Erroneous preoperative risk assessment leads to suboptimal initial surgical management of patients with endometrial cancer

Classification of Ovarian Cancer Surgery Facilitates Treatment Decisions in a Gynecological Multidisciplinary Team

DEFF Research Database (Denmark)

Bjørn, Signe Frahm; Schnack, Tine Henrichsen; Lajer, Henrik

2017-01-01

multidisciplinary team (MDT) decisions. Materials and Methods Four hundred eighteen women diagnosed with ovarian cancers (n = 351) or borderline tumors (n = 66) were selected for primary debulking surgery from January 2008 to July 2013. At an MDT meeting, women were allocated into 3 groups named "pre-COVA" 1 to 3...... classifying the expected extent of the primary surgery and need for postoperative care. On the basis of the operative procedures performed, women were allocated into 1 of the 3 corresponding COVA 1 to 3 groups. The outcome measure was the predictive value of the pre-COVA score compared with the actual COVA......-COVA classification predicted the actual COVA group in 79 (49%) FIGO stages I to IIIB and in 85 (45%) FIGO stages IIIC to IV. Conclusions The COVA classification system is a simple and useful tool in the MDT setting where specialists make treatment decisions based on advanced technology. The use of pre...

Automated reliability assessment for spectroscopic redshift measurements

Science.gov (United States)

Jamal, S.; Le Brun, V.; Le Fèvre, O.; Vibert, D.; Schmitt, A.; Surace, C.; Copin, Y.; Garilli, B.; Moresco, M.; Pozzetti, L.

2018-03-01

Context. Future large-scale surveys, such as the ESA Euclid mission, will produce a large set of galaxy redshifts (≥106) that will require fully automated data-processing pipelines to analyze the data, extract crucial information and ensure that all requirements are met. A fundamental element in these pipelines is to associate to each galaxy redshift measurement a quality, or reliability, estimate. Aim. In this work, we introduce a new approach to automate the spectroscopic redshift reliability assessment based on machine learning (ML) and characteristics of the redshift probability density function. Methods: We propose to rephrase the spectroscopic redshift estimation into a Bayesian framework, in order to incorporate all sources of information and uncertainties related to the redshift estimation process and produce a redshift posterior probability density function (PDF). To automate the assessment of a reliability flag, we exploit key features in the redshift posterior PDF and machine learning algorithms. Results: As a working example, public data from the VIMOS VLT Deep Survey is exploited to present and test this new methodology. We first tried to reproduce the existing reliability flags using supervised classification in order to describe different types of redshift PDFs, but due to the subjective definition of these flags (classification accuracy 58%), we soon opted for a new homogeneous partitioning of the data into distinct clusters via unsupervised classification. After assessing the accuracy of the new clusters via resubstitution and test predictions (classification accuracy 98%), we projected unlabeled data from preliminary mock simulations for the Euclid space mission into this mapping to predict their redshift reliability labels. Conclusions: Through the development of a methodology in which a system can build its own experience to assess the quality of a parameter, we are able to set a preliminary basis of an automated reliability assessment for

Reliability of the Dutch-language version of the Communication Function Classification System and its association with language comprehension and method of communication.

Science.gov (United States)

Vander Zwart, Karlijn E; Geytenbeek, Joke J; de Kleijn, Maaike; Oostrom, Kim J; Gorter, Jan Willem; Hidecker, Mary Jo Cooley; Vermeulen, R Jeroen

2016-02-01

The aims of this study were to determine the intra- and interrater reliability of the Dutch-language version of the Communication Function Classification System (CFCS-NL) and to investigate the association between the CFCS level and (1) spoken language comprehension and (2) preferred method of communication in children with cerebral palsy (CP). Participants were 93 children with CP (50 males, 43 females; mean age 7y, SD 2y 6mo, range 2y 9mo-12y 10mo; unilateral spastic [n=22], bilateral spastic [n=51], dyskinetic [n=15], ataxic [n=3], not specified [n=2]; Gross Motor Function Classification System level I [n=16], II [n=14], III, [n=7], IV [n=24], V [n=31], unknown [n=1]), recruited from rehabilitation centres throughout the Netherlands. Because some centres only contributed to part of the study, different numbers of participants are presented for different aspects of the study. Parents and speech and language therapists (SLTs) classified the communication level using the CFCS. Kappa was used to determine the intra- and interrater reliability. Spearman's correlation coefficient was used to determine the association between CFCS level and spoken language comprehension, and Fisher's exact test was used to examine the association between the CFCS level and method of communication. Interrater reliability of the CFCS-NL between parents and SLTs was fair (r=0.54), between SLTs good (r=0.78), and the intrarater (SLT) reliability very good (r=0.85). The association between the CFCS and spoken language comprehension was strong for SLTs (r=0.63) and moderate for parents (r=0.51). There was a statistically significant difference between the CFCS level and the preferred method of communication of the child (pcommunication in children with CP. Preferably, professionals should classify the child's CFCS level in collaboration with the parents to acquire the most comprehensive information about the everyday communication of the child in various situations both with familiar and

Hyperspectral Imaging and SPA-LDA Quantitative Analysis for Detection of Colon Cancer Tissue

Science.gov (United States)

Yuan, X.; Zhang, D.; Wang, Ch.; Dai, B.; Zhao, M.; Li, B.

2018-05-01

Hyperspectral imaging (HSI) has been demonstrated to provide a rapid, precise, and noninvasive method for cancer detection. However, because HSI contains many data, quantitative analysis is often necessary to distill information useful for distinguishing cancerous from normal tissue. To demonstrate that HSI with our proposed algorithm can make this distinction, we built a Vis-NIR HSI setup and made many spectral images of colon tissues, and then used a successive projection algorithm (SPA) to analyze the hyperspectral image data of the tissues. This was used to build an identification model based on linear discrimination analysis (LDA) using the relative reflectance values of the effective wavelengths. Other tissues were used as a prediction set to verify the reliability of the identification model. The results suggest that Vis-NIR hyperspectral images, together with the spectroscopic classification method, provide a new approach for reliable and safe diagnosis of colon cancer and could lead to advances in cancer diagnosis generally.

A revised 3-column classification approach for the surgical planning of extended lateral tibial plateau fractures.

Science.gov (United States)

Hoekstra, H; Kempenaers, K; Nijs, S

2017-10-01

Variable angle locking compression plates allow for lateral buttress and support of the posterolateral joint surface of tibial plateau fractures. This gives room for improvement of the surgical 3-column classification approach. Our aim was to revise and validate the 3-column classification approach to better guide the surgical planning of tibial plateau fractures extending into the posterolateral corner. In contrast to the 3-column classification approach, in the revised approach the posterior border of the lateral column in the revised approach lies posterior instead of anterior of the fibula. According to the revised 3-column classification approach, extended lateral column fractures are defined as single lateral column fractures extending posteriorly into the posterolateral corner. CT-images of 36 patients were reviewed and classified twice online according to Schatzker and revised 3-column classification approach by five observers. The intraobserver reliability was calculated using the Cohen's kappa and the interobserver reliability was calculated using the Fleiss' kappa. The intraobserver reliability showed substantial agreement according to Landis and Koch for both Schatzker and the revised 3-column classification approach (0.746 vs. 0.782 p = 0.37, Schatzker vs. revised 3-column, respectively). However, the interobserver reliability of the revised 3-column classification approach was significantly higher as compared to the Schatzker classification (0.531 vs. 0.669 p column, respectively). With the introduction of variable angle locking compression plates, the revised 3-column classification approach is a very helpful tool in the preoperative surgical planning of tibial plateau fractures, in particular, lateral column fractures that extend into the posterolateral corner. The revised 3-column classification approach is rather a practical supplement to the Schatzker classification. It has a significantly higher interobserver reliability as compared to the

A pilot study to explore the feasibility of using theClinical Care Classification System for developing a reliable costing method for nursing services.

Science.gov (United States)

Dykes, Patricia C; Wantland, Dean; Whittenburg, Luann; Lipsitz, Stuart; Saba, Virginia K

2013-01-01

While nursing activities represent a significant proportion of inpatient care, there are no reliable methods for determining nursing costs based on the actual services provided by the nursing staff. Capture of data to support accurate measurement and reporting on the cost of nursing services is fundamental to effective resource utilization. Adopting standard terminologies that support tracking both the quality and the cost of care could reduce the data entry burden on direct care providers. This pilot study evaluated the feasibility of using a standardized nursing terminology, the Clinical Care Classification System (CCC), for developing a reliable costing method for nursing services. Two different approaches are explored; the Relative Value Unit RVU and the simple cost-to-time methods. We found that the simple cost-to-time method was more accurate and more transparent in its derivation than the RVU method and may support a more consistent and reliable approach for costing nursing services.

Breast cancer surgery and diagnosis-related groups (DRGs): patient classification and hospital reimbursement in 11 European countries.

Science.gov (United States)

Scheller-Kreinsen, David; Quentin, Wilm; Geissler, Alexander; Busse, Reinhard

2013-10-01

Researchers from eleven countries (i.e. Austria, England, Estonia, Finland, France, Germany, Ireland, Netherlands, Poland, Spain, and Sweden) compared how their DRG systems deal with breast cancer surgery patients. DRG algorithms and indicators of resource consumption were assessed for those DRGs that individually contain at least 1% of all breast cancer surgery patients. Six standardised case vignettes were defined and quasi prices according to national DRG-based hospital payment systems were ascertained. European DRG systems classify breast cancer surgery patients according to different sets of classification variables into three to seven DRGs. Quasi prices for an index case treated with partial mastectomy range from €577 in Poland to €5780 in the Netherlands. Countries award their highest payments for very different kinds of patients. Breast cancer specialists and national DRG authorities should consider how other countries' DRG systems classify breast cancer patients in order to identify potential scope for improvement and to ensure fair and appropriate reimbursement. Copyright © 2012 Elsevier Ltd. All rights reserved.

Profiling cancer

DEFF Research Database (Denmark)

Ciro, Marco; Bracken, Adrian P; Helin, Kristian

2003-01-01

In the past couple of years, several very exciting studies have demonstrated the enormous power of gene-expression profiling for cancer classification and prediction of patient survival. In addition to promising a more accurate classification of cancer and therefore better treatment of patients......, gene-expression profiling can result in the identification of novel potential targets for cancer therapy and a better understanding of the molecular mechanisms leading to cancer....

Establishing magnetic resonance imaging as an accurate and reliable tool to diagnose and monitor esophageal cancer in a rat model.

Directory of Open Access Journals (Sweden)

Juliann E Kosovec

Full Text Available OBJECTIVE: To assess the reliability of magnetic resonance imaging (MRI for detection of esophageal cancer in the Levrat model of end-to-side esophagojejunostomy. BACKGROUND: The Levrat model has proven utility in terms of its ability to replicate Barrett's carcinogenesis by inducing gastroduodenoesophageal reflux (GDER. Due to lack of data on the utility of non-invasive methods for detection of esophageal cancer, treatment efficacy studies have been limited, as adenocarcinoma histology has only been validated post-mortem. It would therefore be of great value if the validity and reliability of MRI could be established in this setting. METHODS: Chronic GDER reflux was induced in 19 male Sprague-Dawley rats using the modified Levrat model. At 40 weeks post-surgery, all animals underwent endoscopy, MRI scanning, and post-mortem histological analysis of the esophagus and anastomosis. With post-mortem histology serving as the gold standard, assessment of presence of esophageal cancer was made by five esophageal specialists and five radiologists on endoscopy and MRI, respectively. RESULTS: The accuracy of MRI and endoscopic analysis to correctly identify cancer vs. no cancer was 85.3% and 50.5%, respectively. ROC curves demonstrated that MRI rating had an AUC of 0.966 (p<0.001 and endoscopy rating had an AUC of 0.534 (p = 0.804. The sensitivity and specificity of MRI for identifying cancer vs. no-cancer was 89.1% and 80% respectively, as compared to 45.5% and 57.5% for endoscopy. False positive rates of MRI and endoscopy were 20% and 42.5%, respectively. CONCLUSIONS: MRI is a more reliable diagnostic method than endoscopy in the Levrat model. The non-invasiveness of the tool and its potential to volumetrically quantify the size and number of tumors likely makes it even more useful in evaluating novel agents and their efficacy in treatment studies of esophageal cancer.

Application of objective clinical human reliability analysis (OCHRA) in assessment of technical performance in laparoscopic rectal cancer surgery.

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Foster, J D; Miskovic, D; Allison, A S; Conti, J A; Ockrim, J; Cooper, E J; Hanna, G B; Francis, N K

2016-06-01

Laparoscopic rectal resection is technically challenging, with outcomes dependent upon technical performance. No robust objective assessment tool exists for laparoscopic rectal resection surgery. This study aimed to investigate the application of the objective clinical human reliability analysis (OCHRA) technique for assessing technical performance of laparoscopic rectal surgery and explore the validity and reliability of this technique. Laparoscopic rectal cancer resection operations were described in the format of a hierarchical task analysis. Potential technical errors were defined. The OCHRA technique was used to identify technical errors enacted in videos of twenty consecutive laparoscopic rectal cancer resection operations from a single site. The procedural task, spatial location, and circumstances of all identified errors were logged. Clinical validity was assessed through correlation with clinical outcomes; reliability was assessed by test-retest. A total of 335 execution errors identified, with a median 15 per operation. More errors were observed during pelvic tasks compared with abdominal tasks (p technical performance of laparoscopic rectal surgery.

Psychosocial Adjustment to Illness Scale: Factor structure, reliability, and validity assessment in a sample of Greek breast cancer patients.

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Kolokotroni, Philippa; Anagnostopoulos, Fotios; Missitzis, Ioannis

2017-07-01

The study and measurement of psychosocial adjustment is important for evaluating patients' well-being, and assessing the illness's course, treatment's success, and patients' recovery. In this study, internal consistency reliability and construct validity of the Greek version of the Psychosocial Adjustment to Illness Scale-Self-Report (PAIS-SR) were examined. Demographic and psychosocial data were collected from a sample of 243 women with breast cancer, recruited from September 2011 to December 2012. With some exceptions in specific items, the original conceptually-derived PAIS-SR subscales emerged in a seven-factor solution. Social Environment, Job and Household Duties, and Psychological Distress accounted for more of the total variance than other subscales. PAIS-SR showed good internal consistency reliability, with Cronbach's alpha coefficients >0.62. Correlations of PAIS-SR domains with measures of quality of life and posttraumatic stress symptoms supported the convergent validity of the PAIS-SR and its significance for cancer research. The Greek version of the PAIS-SR has acceptable internal consistency reliability and construct validity, as well as satisfactory convergent validity. Results provide some suggestions for the development of programs to evaluate adjustment status and implement psychosocial interventions among breast cancer survivors.

Improving breast cancer classification with mammography, supported on an appropriate variable selection analysis

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Pérez, Noel; Guevara, Miguel A.; Silva, Augusto

2013-02-01

This work addresses the issue of variable selection within the context of breast cancer classification with mammography. A comprehensive repository of feature vectors was used including a hybrid subset gathering image-based and clinical features. It aimed to gather experimental evidence of variable selection in terms of cardinality, type and find a classification scheme that provides the best performance over the Area Under Receiver Operating Characteristics Curve (AUC) scores using the ranked features subset. We evaluated and classified a total of 300 subsets of features formed by the application of Chi-Square Discretization, Information-Gain, One-Rule and RELIEF methods in association with Feed-Forward Backpropagation Neural Network (FFBP), Support Vector Machine (SVM) and Decision Tree J48 (DTJ48) Machine Learning Algorithms (MLA) for a comparative performance evaluation based on AUC scores. A variable selection analysis was performed for Single-View Ranking and Multi-View Ranking groups of features. Features subsets representing Microcalcifications (MCs), Masses and both MCs and Masses lesions achieved AUC scores of 0.91, 0.954 and 0.934 respectively. Experimental evidence demonstrated that classification performance was improved by combining image-based and clinical features. The most important clinical and image-based features were StromaDistortion and Circularity respectively. Other less important but worth to use due to its consistency were Contrast, Perimeter, Microcalcification, Correlation and Elongation.

Reliability in content analysis: The case of semantic feature norms classification.

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Bolognesi, Marianna; Pilgram, Roosmaryn; van den Heerik, Romy

2017-12-01

Semantic feature norms (e.g., STIMULUS: car → RESPONSE: ) are commonly used in cognitive psychology to look into salient aspects of given concepts. Semantic features are typically collected in experimental settings and then manually annotated by the researchers into feature types (e.g., perceptual features, taxonomic features, etc.) by means of content analyses-that is, by using taxonomies of feature types and having independent coders perform the annotation task. However, the ways in which such content analyses are typically performed and reported are not consistent across the literature. This constitutes a serious methodological problem that might undermine the theoretical claims based on such annotations. In this study, we first offer a review of some of the released datasets of annotated semantic feature norms and the related taxonomies used for content analysis. We then provide theoretical and methodological insights in relation to the content analysis methodology. Finally, we apply content analysis to a new dataset of semantic features and show how the method should be applied in order to deliver reliable annotations and replicable coding schemes. We tackle the following issues: (1) taxonomy structure, (2) the description of categories, (3) coder training, and (4) sustainability of the coding scheme-that is, comparison of the annotations provided by trained versus novice coders. The outcomes of the project are threefold: We provide methodological guidelines for semantic feature classification; we provide a revised and adapted taxonomy that can (arguably) be applied to both concrete and abstract concepts; and we provide a dataset of annotated semantic feature norms.

A Discrete Wavelet Based Feature Extraction and Hybrid Classification Technique for Microarray Data Analysis

Directory of Open Access Journals (Sweden)

Jaison Bennet

2014-01-01

Full Text Available Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN, naive Bayes, and support vector machine (SVM. Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT and moving window technique (MWT is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection.

Reliability, Validity, and Minimal Detectable Change of Balance Evaluation Systems Test and Its Short Versions in Older Cancer Survivors: A Pilot Study.

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Huang, Min H; Miller, Kara; Smith, Kristin; Fredrickson, Kayle; Shilling, Tracy

2016-01-01

Cancer is primarily a disease of older adults. About 77% of all cancers are diagnosed in persons aged 55 years and older. Cancer and its treatment can cause diverse sequelae impacting body systems underlying balance control. No study has examined the psychometric properties of balance assessment tools in older cancer survivors, presenting a significant challenge in the selection of outcome measures for clinicians treating this fast-growing population. This study aimed to determine the reliability, validity, and minimal detectable change (MDC) of the Balance Evaluation System Test (BESTest), Mini-Balance Evaluation Systems Test (Mini-BESTest), and Brief-Balance Evaluation Systems Test (Brief-BESTest) in community-dwelling older cancer survivors. This study was a cross-sectional design. Twenty breast and 8 prostate cancer survivors participated [age (SD) = 68.4 (8.13) years]. The BESTest and Activity-specific Balance Confidence (ABC) Scale were administered during the first session. Scores of Mini-BESTest and Brief-BESTest were extracted on the basis of the scores of BESTest. The BESTest was repeated within 1 to 2 weeks by the same rater to determine the test-retest reliability. For the analysis of the inter-rater reliability, 21 participants were randomly selected to be evaluated by 2 raters. A primary rater administered the test. The 2 raters independently and concurrently scored the performance of the participants. Each rater recorded the ratings separately on the scoring sheet. No discussion among the raters was allowed throughout the testing. Intraclass correlation coefficients (ICCs), standard error of measurement, minimal detectable change (MDC), and Bland-Altman plots were calculated. Concurrent validity of these balance tests with the ABC Scale was examined using the Spearman correlation. The BESTest, Mini-BESTest, and Brief-BESTest had high test-retest (ICC = 0.90-0.94) and interrater reliability (ICC = 0.86-0.96), small standard error of measurement (0

Towards a genetic classification of uranium deposits

International Nuclear Information System (INIS)

Cuney, M.

2009-01-01

As the IAEA's uranium deposit classification is based on the deposit nature and morphology, some deposits which have been formed by very different genetic processes and located in very different geological environments, are grouped according to this classification. In order to build up a reliable genetic classification based on the mechanism at the origin of the formation of the deposit, the author presents the five main categories according to which uranium deposits can be classified: magmatic, hydrothermal, evapotranspiration, syn-sedimentary, and infiltration of meteoric water

Validity and reliability of the persian version of templer death anxiety scale in family caregivers of cancer patients.

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Soleimani, Mohammad Ali; Bahrami, Nasim; Yaghoobzadeh, Ameneh; Banihashemi, Hedieh; Nia, Hamid Sharif; Haghdoost, Ali Akbar

2016-01-01

Due to increasing recognition of the importance of death anxiety for understanding human nature, it is important that researchers who investigate death anxiety have reliable and valid methodology to measure. The purpose of this study was to evaluate the validity and reliability of the Persian version of Templer Death Anxiety Scale (TDAS) in family caregivers of cancer patients. A sample of 326 caregivers of cancer patients completed a 15-item questionnaire. Principal components analysis (PCA) followed by a varimax rotation was used to assess factor structure of the DAS. The construct validity of the scale was assessed using exploratory and confirmatory factor analyses. Convergent and discriminant validity were also examined. Reliability was assessed with Cronbach's alpha coefficients and construction reliability. Based on the results of the PCA and consideration of the meaning of our items, a three-factor solution, explaining 60.38% of the variance, was identified. A confirmatory factor analysis (CFA) then supported the adequacy of the three-domain structure of the DAS. Goodness-of-fit indices showed an acceptable fit overall with the full model {χ(2)(df) = 262.32 (61), χ(2)/df = 2.04 [adjusted goodness of fit index (AGFI) = 0.922, parsimonious comparative fit index (PCFI) = 0.703, normed fit Index (NFI) = 0.912, CMIN/DF = 2.048, root mean square error of approximation (RMSEA) = 0.055]}. Convergent and discriminant validity were shown with construct fulfilled. The Cronbach's alpha and construct reliability were greater than 0.70. The findings show that the Persian version of the TDAS has a three-factor structure and acceptable validity and reliability.

Nottingham Prognostic Index in Triple-Negative Breast Cancer: a reliable prognostic tool?

International Nuclear Information System (INIS)

Albergaria, André; Ricardo, Sara; Milanezi, Fernanda; Carneiro, Vítor; Amendoeira, Isabel; Vieira, Daniella; Cameselle-Teijeiro, Jorge; Schmitt, Fernando

2011-01-01

A breast cancer prognostic tool should ideally be applicable to all types of invasive breast lesions. A number of studies have shown histopathological grade to be an independent prognostic factor in breast cancer, adding prognostic power to nodal stage and tumour size. The Nottingham Prognostic Index has been shown to accurately predict patient outcome in stratified groups with a follow-up period of 15 years after primary diagnosis of breast cancer. Clinically, breast tumours that lack the expression of Oestrogen Receptor, Progesterone Receptor and Human Epidermal growth factor Receptor 2 (HER2) are identified as presenting a 'triple-negative' phenotype or as triple-negative breast cancers. These poor outcome tumours represent an easily recognisable prognostic group of breast cancer with aggressive behaviour that currently lack the benefit of available systemic therapy. There are conflicting results on the prevalence of lymph node metastasis at the time of diagnosis in triple-negative breast cancer patients but it is currently accepted that triple-negative breast cancer does not metastasize to axillary nodes and bones as frequently as the non-triple-negative carcinomas, favouring instead, a preferentially haematogenous spread. Hypothetically, this particular tumour dissemination pattern would impair the reliability of using Nottingham Prognostic Index as a tool for triple-negative breast cancer prognostication. The present study tested the effectiveness of the Nottingham Prognostic Index in stratifying breast cancer patients of different subtypes with special emphasis in a triple-negative breast cancer patient subset versus non- triple-negative breast cancer. We demonstrated that besides the fact that TNBC disseminate to axillary lymph nodes as frequently as luminal or HER2 tumours, we also showed that TNBC are larger in size compared with other subtypes and almost all grade 3. Additionally, survival curves demonstrated that these prognostic factors are

Reliability studies of diagnostic methods in Indian traditional Ayurveda medicine: An overview

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Kurande, Vrinda Hitendra; Waagepetersen, Rasmus; Toft, Egon; Prasad, Ramjee

2013-01-01

Recently, a need to develop supportive new scientific evidence for contemporary Ayurveda has emerged. One of the research objectives is an assessment of the reliability of diagnoses and treatment. Reliability is a quantitative measure of consistency. It is a crucial issue in classification (such as prakriti classification), method development (pulse diagnosis), quality assurance for diagnosis and treatment and in the conduct of clinical studies. Several reliability studies are conducted in western medicine. The investigation of the reliability of traditional Chinese, Japanese and Sasang medicine diagnoses is in the formative stage. However, reliability studies in Ayurveda are in the preliminary stage. In this paper, examples are provided to illustrate relevant concepts of reliability studies of diagnostic methods and their implication in practice, education, and training. An introduction to reliability estimates and different study designs and statistical analysis is given for future studies in Ayurveda. PMID:23930037

Post-operative rotator cuff integrity, based on Sugaya's classification, can reflect abduction muscle strength of the shoulder.

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Yoshida, Masahito; Collin, Phillipe; Josseaume, Thierry; Lädermann, Alexandre; Goto, Hideyuki; Sugimoto, Katumasa; Otsuka, Takanobu

2018-01-01

Magnetic resonance (MR) imaging is common in structural and qualitative assessment of the rotator cuff post-operatively. Rotator cuff integrity has been thought to be associated with clinical outcome. The purpose of this study was to evaluate the inter-observer reliability of cuff integrity (Sugaya's classification) and assess the correlation between Sugaya's classification and the clinical outcome. It was hypothesized that Sugaya's classification would show good reliability and good correlation with the clinical outcome. Post-operative MR images were taken two years post-operatively, following arthroscopic rotator cuff repair. For assessment of inter-rater reliability, all radiographic evaluations for the supraspinatus muscle were done by two orthopaedic surgeons and one radiologist. Rotator cuff integrity was classified into five categories, according to Sugaya's classification. Fatty infiltration was graded into four categories, based on the Fuchs' classification grading system. Muscle hypotrophy was graded as four grades, according to the scale proposed by Warner. The clinical outcome was assessed according to the constant scoring system pre-operatively and 2 years post-operatively. Of the sixty-two consecutive patients with full-thickness rotator cuff tears, fifty-two patients were reviewed in this study. These subjects included twenty-three men and twenty-nine women, with an average age of fifty-seven years. In terms of the inter-rater reliability between orthopaedic surgeons, Sugaya's classification showed the highest agreement [ICC (2.1) = 0.82] for rotator cuff integrity. The grade of fatty infiltration and muscle atrophy demonstrated good agreement, respectively (0.722 and 0.758). With regard to the inter-rater reliability between orthopaedic surgeon and radiologist, Sugaya's classification showed good reliability [ICC (2.1) = 0.70]. On the other hand, fatty infiltration and muscle hypotrophy classifications demonstrated fair and moderate agreement

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

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Vidić, Igor; Egnell, Liv; Jerome, Neil P; Teruel, Jose R; Sjøbakk, Torill E; Østlie, Agnes; Fjøsne, Hans E; Bathen, Tone F; Goa, Pål Erik

2018-05-01

Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). Prospective. Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+). Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values. Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping. Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons. For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity. Our

Can-CSC-GBE: Developing Cost-sensitive Classifier with Gentleboost Ensemble for breast cancer classification using protein amino acids and imbalanced data.

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Ali, Safdar; Majid, Abdul; Javed, Syed Gibran; Sattar, Mohsin

2016-06-01

Early prediction of breast cancer is important for effective treatment and survival. We developed an effective Cost-Sensitive Classifier with GentleBoost Ensemble (Can-CSC-GBE) for the classification of breast cancer using protein amino acid features. In this work, first, discriminant information of the protein sequences related to breast tissue is extracted. Then, the physicochemical properties hydrophobicity and hydrophilicity of amino acids are employed to generate molecule descriptors in different feature spaces. For comparison, we obtained results by combining Cost-Sensitive learning with conventional ensemble of AdaBoostM1 and Bagging. The proposed Can-CSC-GBE system has effectively reduced the misclassification costs and thereby improved the overall classification performance. Our novel approach has highlighted promising results as compared to the state-of-the-art ensemble approaches. Copyright © 2016 Elsevier Ltd. All rights reserved.

Nuclear power plant systems, structures and components and their safety classification

International Nuclear Information System (INIS)

2000-01-01

The assurance of a nuclear power plant's safety is based on the reliable functioning of the plant as well as on its appropriate maintenance and operation. To ensure the reliability of operation, special attention shall be paid to the design, manufacturing, commissioning and operation of the plant and its components. To control these functions the nuclear power plant is divided into structural and functional entities, i.e. systems. A systems safety class is determined by its safety significance. Safety class specifies the procedures to be employed in plant design, construction, monitoring and operation. The classification document contains all documentation related to the classification of the nuclear power plant. The principles of safety classification and the procedures pertaining to the classification document are presented in this guide. In the Appendix of the guide, examples of systems most typical of each safety class are given to clarify the safety classification principles

Evaluation of Urinary Tract Dilation Classification System for Grading Postnatal Hydronephrosis.

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Hodhod, Amr; Capolicchio, John-Paul; Jednak, Roman; El-Sherif, Eid; El-Doray, Abd El-Alim; El-Sherbiny, Mohamed

2016-03-01

We assessed the reliability and validity of the Urinary Tract Dilation classification system as a new grading system for postnatal hydronephrosis. We retrospectively reviewed charts of patients who presented with hydronephrosis from 2008 to 2013. We included patients diagnosed prenatally and those with hydronephrosis discovered incidentally during the first year of life. We excluded cases involving urinary tract infection, neurogenic bladder and chromosomal anomalies, those associated with extraurinary congenital malformations and those with followup of less than 24 months without resolution. Hydronephrosis was graded postnatally using the Society for Fetal Urology system, and then the management protocol was chosen. All units were regraded using the Urinary Tract Dilation classification system and compared to the Society for Fetal Urology system to assess reliability. Univariate and multivariate analyses were performed to assess the validity of the Urinary Tract Dilation classification system in predicting hydronephrosis resolution and surgical intervention. A total of 490 patients (730 renal units) were eligible to participate. The Urinary Tract Dilation classification system was reliable in the assessment of hydronephrosis (parallel forms 0.92). Hydronephrosis resolved in 357 units (49%), and 86 units (12%) were managed by surgical intervention. The remainder of renal units demonstrated stable or improved hydronephrosis. Multivariate analysis revealed that the likelihood of surgical intervention was predicted independently by Urinary Tract Dilation classification system risk group, while Society for Fetal Urology grades were predictive of likelihood of resolution. The Urinary Tract Dilation classification system is reliable for evaluation of postnatal hydronephrosis and is valid in predicting surgical intervention. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Validity, reliability and understanding of the EORTC-C30 and EORTC-BR23, quality of life questionnaires specific for breast cancer

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Fernanda Alessandra Silva Michels

2013-06-01

Full Text Available Objective: To validate and assess reliability and understanding of the EORTC–C30 quality of life questionnaire and its breast cancer specific module, the EORTC-BR23. Methods: This study was conducted at the AC Camargo Cancer Hospital, São Paulo, Brazil. A total of 100 women diagnosed with breast cancer were interviewed. Internal consistency, confirmatory factorial analysis, convergent validity, construct validity and degree of understanding were examined. Reliability was assessed by comparison of means at times 1 and 2, inter-class coefficient and Bland-Altman graphics. Results: Cronbach’s alpha ranged from 0.72 to 0.86 for the EORTC-C30 and from 0.78 to 0.83 for the EORTC-BR23 questionnaire. Most questions were confirmed in the confirmatory factorial analysis. In the construct validity analysis, the questionnaires were capable of differentiating patients with or without lymphedema, apart from the symptom scales of both questionnaires. Both questionnaires presented a significant correlation in most domains of the SF-36, in the convergent validity analysis. Only a few criticisms were reported concerning questions, and the mean grade of understanding was high (C30 = 4.91 and BR23 = 4.89. The questionnaires presented good rates of reliability, with the exception of the functional scale of the C30 and the symptom scale of the BR23. Conclusions: The EORTC-C30 and EORTC-BR23 quality of life questionnaires were validated, presented good rates of reliability and are easily understood, allowing them to be used in Brazil to assess quality of life among women with breast cancer.

Impact of full field digital mammography on the classification and mammographic characteristics of interval breast cancers

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Knox, Mark, E-mail: marktknox@gmail.com; O’Brien, Angela, E-mail: angelaobrien@doctors.org.uk; Szabó, Endre, E-mail: endrebacsi@freemail.hu; Smith, Clare S., E-mail: csmith@mater.ie; Fenlon, Helen M., E-mail: helen.fenlon@cancerscreening.ie; McNicholas, Michelle M., E-mail: michelle.mcnicholas@cancerscreening.ie; Flanagan, Fidelma L., E-mail: fidelma.flanagan@cancerscreening.ie

2015-06-15

Highlights: • Digital mammography has changed the presentation of interval breast cancer. • Less interval breast cancers are associated with microcalcifications following FFDM. • Interval breast cancer audit remains a key feature of any breast screening program. - Abstract: Objective: Full field digital mammography (FFDM) is increasingly replacing screen film mammography (SFM) in breast screening programs. Interval breast cancers are an issue in all screening programs and the purpose of our study is to assess the impact of FFDM on the classification of interval breast cancers at independent blind review and to compare the mammographic features of interval cancers at FFDM and SFM. Materials and methods: This study included 138 cases of interval breast cancer, 76 following an FFDM screening examination and 62 following screening with SFM. The prior screening mammogram was assessed by each of five consultant breast radiologists who were blinded to the site of subsequent cancer. Subsequent review of the diagnostic mammogram was performed and cases were classified as missed, minimal signs, occult or true interval. Mammographic features of the interval cancer at diagnosis and any abnormality identified on the prior screening mammogram were recorded. Results: The percentages of cancers classified as missed at FFDM and SFM did not differ significantly, 10.5% (8 of 76) at FFDM and 8.1% (5 of 62) at SFM (p = .77). There were significantly less interval cancers presenting as microcalcifications (alone or in association with another abnormality) following screening with FFDM, 16% (12 of 76) than following a SFM examination, 32% (20 of 62) (p = .02). Conclusion: Interval breast cancers continue to pose a problem at FFDM. The switch to FFDM has changed the mammographic presentation of interval breast cancer, with less interval cancers presenting in association with microcalcifications.

Impact of full field digital mammography on the classification and mammographic characteristics of interval breast cancers

International Nuclear Information System (INIS)

Knox, Mark; O’Brien, Angela; Szabó, Endre; Smith, Clare S.; Fenlon, Helen M.; McNicholas, Michelle M.; Flanagan, Fidelma L.

2015-01-01

Highlights: • Digital mammography has changed the presentation of interval breast cancer. • Less interval breast cancers are associated with microcalcifications following FFDM. • Interval breast cancer audit remains a key feature of any breast screening program. - Abstract: Objective: Full field digital mammography (FFDM) is increasingly replacing screen film mammography (SFM) in breast screening programs. Interval breast cancers are an issue in all screening programs and the purpose of our study is to assess the impact of FFDM on the classification of interval breast cancers at independent blind review and to compare the mammographic features of interval cancers at FFDM and SFM. Materials and methods: This study included 138 cases of interval breast cancer, 76 following an FFDM screening examination and 62 following screening with SFM. The prior screening mammogram was assessed by each of five consultant breast radiologists who were blinded to the site of subsequent cancer. Subsequent review of the diagnostic mammogram was performed and cases were classified as missed, minimal signs, occult or true interval. Mammographic features of the interval cancer at diagnosis and any abnormality identified on the prior screening mammogram were recorded. Results: The percentages of cancers classified as missed at FFDM and SFM did not differ significantly, 10.5% (8 of 76) at FFDM and 8.1% (5 of 62) at SFM (p = .77). There were significantly less interval cancers presenting as microcalcifications (alone or in association with another abnormality) following screening with FFDM, 16% (12 of 76) than following a SFM examination, 32% (20 of 62) (p = .02). Conclusion: Interval breast cancers continue to pose a problem at FFDM. The switch to FFDM has changed the mammographic presentation of interval breast cancer, with less interval cancers presenting in association with microcalcifications

Prognostic Performance and Reproducibility of the 1973 and 2004/2016 World Health Organization Grading Classification Systems in Non-muscle-invasive Bladder Cancer: A European Association of Urology Non-muscle Invasive Bladder Cancer Guidelines Panel Systematic Review.

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Soukup, Viktor; Čapoun, Otakar; Cohen, Daniel; Hernández, Virginia; Babjuk, Marek; Burger, Max; Compérat, Eva; Gontero, Paolo; Lam, Thomas; MacLennan, Steven; Mostafid, A Hugh; Palou, Joan; van Rhijn, Bas W G; Rouprêt, Morgan; Shariat, Shahrokh F; Sylvester, Richard; Yuan, Yuhong; Zigeuner, Richard

2017-11-01

Tumour grade is an important prognostic indicator in non-muscle-invasive bladder cancer (NMIBC). Histopathological classifications are limited by interobserver variability (reproducibility), which may have prognostic implications. European Association of Urology NMIBC guidelines suggest concurrent use of both 1973 and 2004/2016 World Health Organization (WHO) classifications. To compare the prognostic performance and reproducibility of the 1973 and 2004/2016 WHO grading systems for NMIBC. A systematic literature search was undertaken incorporating Medline, Embase, and the Cochrane Library. Studies were critically appraised for risk of bias (QUIPS). For prognosis, the primary outcome was progression to muscle-invasive or metastatic disease. Secondary outcomes were disease recurrence, and overall and cancer-specific survival. For reproducibility, the primary outcome was interobserver variability between pathologists. Secondary outcome was intraobserver variability (repeatability) by the same pathologist. Of 3593 articles identified, 20 were included in the prognostic review; three were eligible for the reproducibility review. Increasing tumour grade in both classifications was associated with higher disease progression and recurrence rates. Progression rates in grade 1 patients were similar to those in low-grade patients; progression rates in grade 3 patients were higher than those in high-grade patients. Survival data were limited. Reproducibility of the 2004/2016 system was marginally better than that of the 1973 system. Two studies on repeatability showed conflicting results. Most studies had a moderate to high risk of bias. Current grading classifications in NMIBC are suboptimal. The 1973 system identifies more aggressive tumours. Intra- and interobserver variability was slightly less in the 2004/2016 classification. We could not confirm that the 2004/2016 classification outperforms the 1973 classification in prediction of recurrence and progression. This article

Compensatory neurofuzzy model for discrete data classification in biomedical

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Ceylan, Rahime

2015-03-01

Biomedical data is separated to two main sections: signals and discrete data. So, studies in this area are about biomedical signal classification or biomedical discrete data classification. There are artificial intelligence models which are relevant to classification of ECG, EMG or EEG signals. In same way, in literature, many models exist for classification of discrete data taken as value of samples which can be results of blood analysis or biopsy in medical process. Each algorithm could not achieve high accuracy rate on classification of signal and discrete data. In this study, compensatory neurofuzzy network model is presented for classification of discrete data in biomedical pattern recognition area. The compensatory neurofuzzy network has a hybrid and binary classifier. In this system, the parameters of fuzzy systems are updated by backpropagation algorithm. The realized classifier model is conducted to two benchmark datasets (Wisconsin Breast Cancer dataset and Pima Indian Diabetes dataset). Experimental studies show that compensatory neurofuzzy network model achieved 96.11% accuracy rate in classification of breast cancer dataset and 69.08% accuracy rate was obtained in experiments made on diabetes dataset with only 10 iterations.

Penalized feature selection and classification in bioinformatics

OpenAIRE

Ma, Shuangge; Huang, Jian

2008-01-01

In bioinformatics studies, supervised classification with high-dimensional input variables is frequently encountered. Examples routinely arise in genomic, epigenetic and proteomic studies. Feature selection can be employed along with classifier construction to avoid over-fitting, to generate more reliable classifier and to provide more insights into the underlying causal relationships. In this article, we provide a review of several recently developed penalized feature selection and classific...

Is our Ground-Truth for Traffic Classification Reliable?

DEFF Research Database (Denmark)

Carela-Español, Valentín; Bujlow, Tomasz; Barlet-Ros, Pere

2014-01-01

. In order to evaluate these tools we have carefully built a labeled dataset of more than 500 000 flows, which contains traffic from popular applications. Our results present PACE, a commercial tool, as the most reliable solution for ground-truth generation. However, among the open-source tools available...

Reliability studies of diagnostic methods in Indian traditional Ayurveda medicine

DEFF Research Database (Denmark)

Kurande, Vrinda Hitendra; Waagepetersen, Rasmus; Toft, Egon

2013-01-01

as prakriti classification), method development (pulse diagnosis), quality assurance for diagnosis and treatment and in the conduct of clinical studies. Several reliability studies are conducted in western medicine. The investigation of the reliability of traditional Chinese, Japanese and Sasang medicine...

The use of the SF-36 questionnaire in adult survivors of childhood cancer: evaluation of data quality, score reliability, and scaling assumptions

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Winter David L

2006-10-01

Full Text Available Abstract Background The SF-36 has been used in a number of previous studies that have investigated the health status of childhood cancer survivors, but it never has been evaluated regarding data quality, scaling assumptions, and reliability in this population. As health status among childhood cancer survivors is being increasingly investigated, it is important that the measurement instruments are reliable, validated and appropriate for use in this population. The aim of this paper was to determine whether the SF-36 questionnaire is a valid and reliable instrument in assessing self-perceived health status of adult survivors of childhood cancer. Methods We examined the SF-36 to see how it performed with respect to (1 data completeness, (2 distribution of the scale scores, (3 item-internal consistency, (4 item-discriminant validity, (5 internal consistency, and (6 scaling assumptions. For this investigation we used SF-36 data from a population-based study of 10,189 adult survivors of childhood cancer. Results Overall, missing values ranged per item from 0.5 to 2.9 percent. Ceiling effects were found to be highest in the role limitation-physical (76.7% and role limitation-emotional (76.5% scales. All correlations between items and their hypothesised scales exceeded the suggested standard of 0.40 for satisfactory item-consistency. Across all scales, the Cronbach's alpha coefficient of reliability was found to be higher than the suggested value of 0.70. Consistent across all cancer groups, the physical health related scale scores correlated strongly with the Physical Component Summary (PCS scale scores and weakly with the Mental Component Summary (MCS scale scores. Also, the mental health and role limitation-emotional scales correlated strongly with the MCS scale score and weakly with the PCS scale score. Moderate to strong correlations with both summary scores were found for the general health perception, energy/vitality, and social functioning

Intra- and interrater reliability of the Chicago Classification of achalasia subtypes in pediatric high-resolution esophageal manometry (HRM) recordings.

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Singendonk, M M J; Rosen, R; Oors, J; Rommel, N; van Wijk, M P; Benninga, M A; Nurko, S; Omari, T I

2017-11-01

Subtyping achalasia by high-resolution manometry (HRM) is clinically relevant as response to therapy and prognosis have shown to vary accordingly. The aim of this study was to assess inter- and intrarater reliability of diagnosing achalasia and achalasia subtyping in children using the Chicago Classification (CC) V3.0. Six observers analyzed 40 pediatric HRM recordings (22 achalasia and 18 non-achalasia) twice by using dedicated analysis software (ManoView 3.0, Given Imaging, Los Angeles, CA, USA). Integrated relaxation pressure (IRP4s), distal contractile integral (DCI), intrabolus pressurization pattern (IBP), and distal latency (DL) were extracted and analyzed hierarchically. Cohen's κ (2 raters) and Fleiss' κ (>2 raters) and the intraclass correlation coefficient (ICC) were used for categorical and ordinal data, respectively. Based on the results of dedicated analysis software only, intra- and interrater reliability was excellent and moderate (κ=0.89 and κ=0.52, respectively) for differentiating achalasia from non-achalasia. For subtyping achalasia, reliability decreased to substantial and fair (κ=0.72 and κ=0.28, respectively). When observers were allowed to change the software-driven diagnosis according to their own interpretation of the manometric patterns, intra- and interrater reliability increased for diagnosing achalasia (κ=0.98 and κ=0.92, respectively) and for subtyping achalasia (κ=0.79 and κ=0.58, respectively). Intra- and interrater agreement for diagnosing achalasia when using HRM and the CC was very good to excellent when results of automated analysis software were interpreted by experienced observers. More variability was seen when relying solely on the software-driven diagnosis and for subtyping achalasia. Therefore, diagnosing and subtyping achalasia should be performed in pediatric motility centers with significant expertise. © 2017 John Wiley & Sons Ltd.

Reliability and validity of needle biopsy evaluation of breast-abnormalities using the B-categorization – design and objectives of the Diagnosis Optimisation Study (DIOS

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Schmidt-Pokrzywniak Andrea

2007-06-01

Full Text Available Abstract Background The planned nationwide implementation of mammography screening 2007 in Germany will increase the occurrence of mammographically detected breast abnormalities. These abnormalities are normally evaluated by minimal invasive core biopsy. To minimize false positive and false negative histological findings, quality assurance of the pathological evaluation of the biopsies is essential. Various guidelines for quality assurance in breast cancer diagnosis recommend applying the B-classification for histopathological categorization. However, to date there are only few studies that reported results about reliability and validity of B-classification. Therefore, objectives of our study are to determine the inter- and intraobserver variability (reliability study and construct and predictive validity (validity study of core biopsy evaluation of breast abnormalities. This paper describes the design and objectives of the DIOS Study. Methods/Design All consecutive asymptomatic and symptomatic women with breast imaging abnormalities who are referred to the University Hospital of Halle for core breast biopsy over a period of 24 months are eligible. According to the sample size calculation we need 800 women for the study. All patients in the study population underwent clinical and radiological examination. Core biopsy is performed by stereotactic-, ultrasound- or magnetic resonance (MR guided automated gun method or vacuum assisted method. The histopathologic agreement (intra- and interobserver of pathologists and the histopathologic validity will be evaluated. Two reference standards are implemented, a reference pathologist and in case of suspicious or malignant findings the histopathologic result of excision biopsy. Furthermore, a self administrated questionnaire which contains questions about potential risk factors of breast cancer, is sent to the participants approximately two weeks after core biopsy. This enables us to run a case

Reliable nanomaterial classification of powders using the volume-specific surface area method

International Nuclear Information System (INIS)

Wohlleben, Wendel; Mielke, Johannes; Bianchin, Alvise; Ghanem, Antoine; Freiberger, Harald; Rauscher, Hubert; Gemeinert, Marion; Hodoroaba, Vasile-Dan

2017-01-01

The volume-specific surface area (VSSA) of a particulate material is one of two apparently very different metrics recommended by the European Commission for a definition of “nanomaterial” for regulatory purposes: specifically, the VSSA metric may classify nanomaterials and non-nanomaterials differently than the median size in number metrics, depending on the chemical composition, size, polydispersity, shape, porosity, and aggregation of the particles in the powder. Here we evaluate the extent of agreement between classification by electron microscopy (EM) and classification by VSSA on a large set of diverse particulate substances that represent all the anticipated challenges except mixtures of different substances. EM and VSSA are determined in multiple labs to assess also the level of reproducibility. Based on the results obtained on highly characterized benchmark materials from the NanoDefine EU FP7 project, we derive a tiered screening strategy for the purpose of implementing the definition of nanomaterials. We finally apply the screening strategy to further industrial materials, which were classified correctly and left only borderline cases for EM. On platelet-shaped nanomaterials, VSSA is essential to prevent false-negative classification by EM. On porous materials, approaches involving extended adsorption isotherms prevent false positive classification by VSSA. We find no false negatives by VSSA, neither in Tier 1 nor in Tier 2, despite real-world industrial polydispersity and diverse composition, shape, and coatings. The VSSA screening strategy is recommended for inclusion in a technical guidance for the implementation of the definition.

Reliable nanomaterial classification of powders using the volume-specific surface area method

Energy Technology Data Exchange (ETDEWEB)

Wohlleben, Wendel, E-mail: wendel.wohlleben@basf.com [Department of Material Physics, BASF SE (Germany); Mielke, Johannes [BAM–Federal Institute for Materials Research and Testing (Germany); Bianchin, Alvise [MBN Nanomaterialia s.p.a (Italy); Ghanem, Antoine [R& I Centre Brussels, Solvay (Belgium); Freiberger, Harald [Department of Material Physics, BASF SE (Germany); Rauscher, Hubert [European Commission, Nanobiosciences Unit, Joint Research Centre (Italy); Gemeinert, Marion; Hodoroaba, Vasile-Dan, E-mail: dan.hodoroaba@bam.de [BAM–Federal Institute for Materials Research and Testing (Germany)

2017-02-15

The volume-specific surface area (VSSA) of a particulate material is one of two apparently very different metrics recommended by the European Commission for a definition of “nanomaterial” for regulatory purposes: specifically, the VSSA metric may classify nanomaterials and non-nanomaterials differently than the median size in number metrics, depending on the chemical composition, size, polydispersity, shape, porosity, and aggregation of the particles in the powder. Here we evaluate the extent of agreement between classification by electron microscopy (EM) and classification by VSSA on a large set of diverse particulate substances that represent all the anticipated challenges except mixtures of different substances. EM and VSSA are determined in multiple labs to assess also the level of reproducibility. Based on the results obtained on highly characterized benchmark materials from the NanoDefine EU FP7 project, we derive a tiered screening strategy for the purpose of implementing the definition of nanomaterials. We finally apply the screening strategy to further industrial materials, which were classified correctly and left only borderline cases for EM. On platelet-shaped nanomaterials, VSSA is essential to prevent false-negative classification by EM. On porous materials, approaches involving extended adsorption isotherms prevent false positive classification by VSSA. We find no false negatives by VSSA, neither in Tier 1 nor in Tier 2, despite real-world industrial polydispersity and diverse composition, shape, and coatings. The VSSA screening strategy is recommended for inclusion in a technical guidance for the implementation of the definition.

Test-Retest Reliability of the Short-Form Survivor Unmet Needs Survey.

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Taylor, Karen; Bulsara, Max; Monterosso, Leanne

2018-01-01

Reliable and valid needs assessment measures are important assessment tools in cancer survivorship care. A new 30-item short-form version of the Survivor Unmet Needs Survey (SF-SUNS) was developed and validated with cancer survivors, including hematology cancer survivors; however, test-retest reliability has not been established. The objective of this study was to assess the test-retest reliability of the SF-SUNS with a cohort of lymphoma survivors ( n = 40). Test-retest reliability of the SF-SUNS was conducted at two time points: baseline (time 1) and 5 days later (time 2). Test-retest data were collected from lymphoma cancer survivors ( n = 40) in a large tertiary cancer center in Western Australia. Intraclass correlation analyses compared data at time 1 (baseline) and time 2 (5 days later). Cronbach's alpha analyses were performed to assess the internal consistency at both time points. The majority (23/30, 77%) of items achieved test-retest reliability scores 0.45-0.74 (fair to good). A high degree of overall internal consistency was demonstrated (time 1 = 0.92, time 2 = 0.95), with scores 0.65-0.94 across subscales for both time points. Mixed test-retest reliability of the SF-SUNS was established. Our results indicate the SF-SUNS is responsive to the changing needs of lymphoma cancer survivors. Routine use of cancer survivorship specific needs-based assessments is required in oncology care today. Nurses are well placed to administer these assessments and provide tailored information and resources. Further assessment of test-retest reliability in hematology and other cancer cohorts is warranted.

An Adaptive Classification Strategy for Reliable Locomotion Mode Recognition

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Ming Liu

2017-09-01

Full Text Available Algorithms for locomotion mode recognition (LMR based on surface electromyography and mechanical sensors have recently been developed and could be used for the neural control of powered prosthetic legs. However, the variations in input signals, caused by physical changes at the sensor interface and human physiological changes, may threaten the reliability of these algorithms. This study aimed to investigate the effectiveness of applying adaptive pattern classifiers for LMR. Three adaptive classifiers, i.e., entropy-based adaptation (EBA, LearnIng From Testing data (LIFT, and Transductive Support Vector Machine (TSVM, were compared and offline evaluated using data collected from two able-bodied subjects and one transfemoral amputee. The offline analysis indicated that the adaptive classifier could effectively maintain or restore the performance of the LMR algorithm when gradual signal variations occurred. EBA and LIFT were recommended because of their better performance and higher computational efficiency. Finally, the EBA was implemented for real-time human-in-the-loop prosthesis control. The online evaluation showed that the applied EBA effectively adapted to changes in input signals across sessions and yielded more reliable prosthesis control over time, compared with the LMR without adaptation. The developed novel adaptive strategy may further enhance the reliability of neurally-controlled prosthetic legs.

Mapping Green Spaces in Bishkek—How Reliable can Spatial Analysis Be?

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Peter Hofmann

2011-05-01

Full Text Available Within urban areas, green spaces play a critically important role in the quality of life. They have remarkable impact on the local microclimate and the regional climate of the city. Quantifying the ‘greenness’ of urban areas allows comparing urban areas at several levels, as well as monitoring the evolution of green spaces in urban areas, thus serving as a tool for urban and developmental planning. Different categories of vegetation have different impacts on recreation potential and microclimate, as well as on the individual perception of green spaces. However, when quantifying the ‘greenness’ of urban areas the reliability of the underlying information is important in order to qualify analysis results. The reliability of geo-information derived from remote sensing data is usually assessed by ground truth validation or by comparison with other reference data. When applying methods of object based image analysis (OBIA and fuzzy classification, the degrees of fuzzy membership per object in general describe to what degree an object fits (prototypical class descriptions. Thus, analyzing the fuzzy membership degrees can contribute to the estimation of reliability and stability of classification results, even when no reference data are available. This paper presents an object based method using fuzzy class assignments to outline and classify three different classes of vegetation from GeoEye imagery. The classification result, its reliability and stability are evaluated using the reference-free parameters Best Classification Result and Classification Stability as introduced by Benz et al. in 2004 and implemented in the software package eCognition (www.ecognition.com. To demonstrate the application potentials of results a scenario for quantifying urban ‘greenness’ is presented.

Structural and reliability analysis of quality of relationship index in cancer patients.

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Cousson-Gélie, Florence; de Chalvron, Stéphanie; Zozaya, Carole; Lafaye, Anaïs

2013-01-01

Among psychosocial factors affecting emotional adjustment and quality of life, social support is one of the most important and widely studied in cancer patients, but little is known about the perception of support in specific significant relationships in patients with cancer. This study examined the psychometric properties of the Quality of Relationship Inventory (QRI) by evaluating its factor structure and its convergent and discriminant validity in a sample of cancer patients. A total of 388 patients completed the QRI. Convergent validity was evaluated by testing the correlations between the QRI subscales and measures of general social support, anxiety and depression symptoms. Discriminant validity was examined by testing group comparison. The QRI's longitudinal invariance across time was also tested. Principal axis factor analysis with promax rotation identified three factors accounting for 42.99% of variance: perceived social support, depth, and interpersonal conflict. Estimates of reliability with McDonald's ω coefficient were satisfactory for all the QRI subscales (ω ranging from 0.75 - 0.85). Satisfaction from general social support was negatively correlated with the interpersonal conflict subscale and positively with the depth subscale. The interpersonal conflict and social support scales were correlated with depression and anxiety scores. We also found a relative stability of QRI subscales (measured 3 months after the first evaluation) and differences between partner status and gender groups. The Quality of Relationship Inventory is a valid tool for assessing the quality of social support in a particular relationship with cancer patients.

Validity and reliability of the Japanese version of the Caregiver Reaction Assessment Scale (CRA-J) for community-dwelling cancer patients.

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Misawa, Tomoyo; Miyashita, Mitsunori; Kawa, Masako; Abe, Koji; Abe, Mayumi; Nakayama, Yasuko; Given, Charles W

2009-01-01

The aim of this study was to validate the Caregiver Reaction Assessment (CRA) among caregivers of community-dwelling advanced cancer patients in Japan. A cross-sectional questionnaire was administered to advanced cancer patients and their caregivers who were cared for at day hospices and home palliative care services. We translated the CRA into Japanese, and then verified factor validity, reliability, construct validity, concurrent validity, and known groups' validity. To address construct and concurrent validity, we calculated Pearson's correlation coefficient between the Japanese version of the CRA and the Burden Index of Caregivers (BIC). To address known groups' validity, we used the t test or analysis of variance (ANOVA). A total of 57 caregivers participated in the study. Five factors were extracted (''impact on schedule,'' ''caregiver's self-esteem,'' ''lack of family support,'' ''impact on health,'' and ''impact on finances'') and reliability was good. Construct and concurrent validity among the subscales of the BIC were good. Regarding known groups validity, the subscale score of ''impact on schedule'' for the groups that cared 6 hours or more per day was higher than the other group (P = .04). The CRA-J is valid and reliable. This scale is useful for caregivers of cancer patients in Japan.

Classification of instability after reverse shoulder arthroplasty guides surgical management and outcomes.

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Abdelfattah, Adham; Otto, Randall J; Simon, Peter; Christmas, Kaitlyn N; Tanner, Gregory; LaMartina, Joey; Levy, Jonathan C; Cuff, Derek J; Mighell, Mark A; Frankle, Mark A

2018-04-01

Revision of unstable reverse shoulder arthroplasty (RSA) remains a significant challenge. The purpose of this study was to determine the reliability of a new treatment-guiding classification for instability after RSA, to describe the clinical outcomes of patients stabilized operatively, and to identify those with higher risk of recurrence. All patients undergoing revision for instability after RSA were identified at our institution. Demographic, clinical, radiographic, and intraoperative data were collected. A classification was developed using all identified causes of instability after RSA and allocating them to 1 of 3 defined treatment-guiding categories. Eight surgeons reviewed all data and applied the classification scheme to each case. Interobserver and intraobserver reliability was used to evaluate the classification scheme. Preoperative clinical outcomes were compared with final follow-up in stabilized shoulders. Forty-three revision cases in 34 patients met the inclusion for study. Five patients remained unstable after revision. Persistent instability most commonly occurred in persistent deltoid dysfunction and postoperative acromial fractures but also in 1 case of soft tissue impingement. Twenty-one patients remained stable at minimum 2 years of follow-up and had significant improvement of clinical outcome scores and range of motion. Reliability of the classification scheme showed substantial and almost perfect interobserver and intraobserver agreement among all the participants (κ = 0.699 and κ = 0.851, respectively). Instability after RSA can be successfully treated with revision surgery using the reliable treatment-guiding classification scheme presented herein. However, more understanding is needed for patients with greater risk of recurrent instability after revision surgery. Copyright © 2017 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

[Molecular classification of breast cancer patients obtained through the technique of chromogenic in situ hybridization (CISH)].

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Fernández, Angel; Reigosa, Aldo

2013-12-01

Breast cancer is a heterogeneous disease composed of a growing number of biological subtypes, with substantial variability of the disease progression within each category. The aim of this research was to classify the samples object of study according to the molecular classes of breast cancer: luminal A, luminal B, HER2 and triple negative, as a result of the state of HER2 amplification obtained by the technique of chromogenic in situ hybridization (CISH). The sample consisted of 200 biopsies fixed in 10% formalin, processed by standard techniques up to paraffin embedding, corresponding to patients diagnosed with invasive ductal carcinoma of the breast. These biopsies were obtained from patients from private practice and the Institute of Oncology "Dr. Miguel Pérez Carreño", for immunohistochemistry (IHC) of hormone receptors and HER2 made in the Hospital Metropolitano del Norte, Valencia, Venezuela. The molecular classification of the patient's tumors considering the expression of estrogen and progesterone receptors by IHC and HER2 amplification by CISH, allowed those cases originally classified as unknown, since they had an indeterminate (2+) outcome for HER2 expression by IHC, to be grouped into the different molecular classes. Also, this classification permitted that some cases, initially considered as belonging to a molecular class, were assigned to another class, after the revaluation of the HER2 status by CISH.

CDX2 prognostic value in stage II/III resected colon cancer is related to CMS classification.

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Pilati, C; Taieb, J; Balogoun, R; Marisa, L; de Reyniès, A; Laurent-Puig, P

2017-05-01

Caudal-type homeobox transcription factor 2 (CDX2) is involved in colon cancer (CC) oncogenesis and has been proposed as a prognostic biomarker in patients with stage II or III CC. We analyzed CDX2 expression in a series of 469 CC typed for the new international consensus molecular subtype (CMS) classification, and we confirmed results in a series of 90 CC. Here, we show that lack of CDX2 expression is only present in the mesenchymal subgroup (CMS4) and in MSI-immune tumors (CMS1) and not in CMS2 and CMS3 colon cancer. Although CDX2 expression was a globally independent prognostic factor, loss of CDX2 expression is not associated with a worse prognosis in the CMS1 group, but is highly prognostic in CMS4 patients for both relapse free and overall survival. Similarly, lack of CDX2 expression was a bad prognostic factor in MSS patients, but not in MSI. Our work suggests that combination of the consensual CMS classification and lack of CDX2 expression could be a useful marker to identify CMS4/CDX2-negative patients with a very poor prognosis. © The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Early esophageal cancer detection using RF classifiers

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Janse, Markus H. A.; van der Sommen, Fons; Zinger, Svitlana; Schoon, Erik J.; de With, Peter H. N.

2016-03-01

Esophageal cancer is one of the fastest rising forms of cancer in the Western world. Using High-Definition (HD) endoscopy, gastroenterology experts can identify esophageal cancer at an early stage. Recent research shows that early cancer can be found using a state-of-the-art computer-aided detection (CADe) system based on analyzing static HD endoscopic images. Our research aims at extending this system by applying Random Forest (RF) classification, which introduces a confidence measure for detected cancer regions. To visualize this data, we propose a novel automated annotation system, employing the unique characteristics of the previous confidence measure. This approach allows reliable modeling of multi-expert knowledge and provides essential data for real-time video processing, to enable future use of the system in a clinical setting. The performance of the CADe system is evaluated on a 39-patient dataset, containing 100 images annotated by 5 expert gastroenterologists. The proposed system reaches a precision of 75% and recall of 90%, thereby improving the state-of-the-art results by 11 and 6 percentage points, respectively.

PI-RADS version 2: quantitative analysis aids reliable interpretation of diffusion-weighted imaging for prostate cancer

Energy Technology Data Exchange (ETDEWEB)

Park, Sung Yoon; Jung, Dae Chul; Oh, Young Taik [Yonsei University College of Medicine, Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Seoul (Korea, Republic of); Shin, Su-Jin [Yonsei University College of Medicine, Department of Pathology, Seoul (Korea, Republic of); Hanyang University College of Medicine, Department of Pathology, Seoul (Korea, Republic of); Cho, Nam Hoon [Yonsei University College of Medicine, Department of Pathology, Seoul (Korea, Republic of); Choi, Young Deuk; Rha, Koon Ho; Hong, Sung Joon [Yonsei University College of Medicine, Department of Urology, Seoul (Korea, Republic of)

2017-07-15

To determine whether apparent diffusion coefficient (ADC) ratio aids reliable interpretation of diffusion-weighted imaging (DWI) for prostate cancer (PCa). Seventy-six consecutive patients with PCa who underwent DWI and surgery were included. Based on pathologic tumour location, two readers independently performed DWI scoring according to the revised Prostate Imaging Reporting and Data System (PI-RADSv2). ADC ratios of benign to cancerous prostatic tissue were then measured independently and compared between cases showing concordant and discordant DWI scores ≥4. Area under the curve (AUC) and threshold of ADC ratio were analyzed for DWI scores ≥4. The rate of inter-reader disagreement for DWI score ≥4 was 11.8% (9/76). ADC ratios were higher in concordant vs. discordant DWI scores ≥4 (median, 1.7 vs. 1.1-1.2; p < 0.001). For DWI scores ≥4, the AUCs of ADC ratios were 0.970 for reader 1 and 0.959 for reader 2. In patients with an ADC ratio >1.3, the rate of inter-reader disagreement for DWI score ≥4 decreased to 5.9-6.0%. An ADC ratio >1.3 yielded 100% (reader 1, 54/54; reader 2, 51/51) positive predictive value for clinically significant cancer. ADC ratios may be useful for reliable interpretation of DWI score ≥4 in PI-RADSv2. (orig.)

Guidelines for Reporting Reliability and Agreement Studies (GRRAS) were proposed

DEFF Research Database (Denmark)

Kottner, Jan; Audigé, Laurent; Brorson, Stig

2011-01-01

Results of reliability and agreement studies are intended to provide information about the amount of error inherent in any diagnosis, score, or measurement. The level of reliability and agreement among users of scales, instruments, or classifications is widely unknown. Therefore, there is a need ......, standards, or guidelines for reporting reliability and agreement in the health care and medical field are lacking. The objective was to develop guidelines for reporting reliability and agreement studies....

Measuring health-related quality of life in children with cancer living in mainland China: feasibility, reliability and validity of the Chinese mandarin version of PedsQL 4.0 Generic Core Scales and 3.0 Cancer Module

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Ji Yi

2011-11-01

Full Text Available Abstract Background The Pediatric Quality of Life Inventory (PedsQL is widely used instrument to measure pediatric health-related quality of life (HRQOL for children aged 2 to 18 years. The purpose of the current study was to investigate the feasibility, reliability and validity of the Chinese mandarin version of the PedsQL 4.0 Generic Core Scales and 3.0 Cancer Module in a group of Chinese children with cancer. Methods The PedsQL 4.0 Genetic Core Scales and the PedsQL 3.0 Cancer Module were administered to children with cancer (aged 5-18 years and parents of such children (aged 2-18 years. For comparison, a survey on a demographically group-matched sample of the general population with children (aged 5-18 and parents of children (aged 2-18 years was conducted with the PedsQL 4.0 Genetic Core Scales. Result The minimal mean percentage of missing item responses (except the School Functioning scale supported the feasibility of the PedsQL 4.0 Generic Core Scales and 3.0 Cancer Module for Chinese children with cancer. Most of the scales showed satisfactory reliability with Cronbach's α of exceeding 0.70, and all scales demonstrated sufficient test-retest reliability. Assessing the clinical validity of the questionnaires, statistically significant difference was found between healthy children and children with cancer, and between children on-treatment versus off-treatment ≥12 months. Positive significant correlations were observed between the scores of the PedsQL 4.0 Generic Core Scale and the PedsQL 3.0 Cancer Module. Exploratory factor analysis demonstrated sufficient factorial validity. Moderate to good agreement was found between child self- and parent proxy-reports. Conclusion The findings support the feasibility, reliability and validity of the Chinese Mandarin version of PedsQL 4.0 Generic Core Scales and 3.0 Cancer Module in children with cancer living in mainland China.

Is Preoperative Neutrophil Lymphocyte Ratio a Reliable Prognostic Parameter for Localized Prostate Cancer?

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Tümay İpekçi

2017-12-01

Full Text Available Objective: In spite of all efforts, prostate cancer is still the 2nd highest cause of cancer-related deaths in men. For this reason new developments are needed in diagnosis, treatment and follow-up of prostate cancer. Neutrophil/lymphocyte (N/L ratio is a cheap and effective parameter used for research into many solid tumors; but there are not enough studies on the reliability of this parameter in prostate cancer. In this study we researched the efficacy of N/L ratio in localized prostate cancer. Materials and Methods: Between March 9, 2012 and April 23, 2017, the data of 140 patients who underwent radical prostatectomy with localized prostate cancer were screened retrospectively. The patients’ ages, preoperative prostate specific antigen (PSA and N/L ratio, pathologic stage, pathologic Gleason score, tumor volume, lymph node involvement, surgical margin positivity and presence or absence of 3rd month biochemical recurrence were noted. The correlations between N/L ratio with age, PSA, pathologic parameters, surgical margin positivity and biochemical recurrence were investigated. Results: The mean age of patients was 63.0±5.9 years, mean PSA value was 10.8±8.5 ng/mL and mean N/L ratio was 2.5±1.9. There was no correlation found between N/L ratio and PSA, pathologic stage, Gleason score, lymph node involvement, tumor volume, surgical margin positivity and biochemical recurrence (p>0.05. Conclusion: In our study investigating 140 patients with localized prostate cancer, we did not identify any correlation between N/L ratio and PSA, surgical stage and Gleason score, surgical margin positivity, and 3rd month biochemical recurrence. When the literature is investigated, it appears that N/L ratio is effective for metastatic prostate cancer. To provide a more accurate judgment of the role of N/L ratio in localized prostate cancer, there is a need for new studies with broader patient series.

Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression.

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Shayan, Zahra; Mohammad Gholi Mezerji, Naser; Shayan, Leila; Naseri, Parisa

2015-11-03

Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification.

Science.gov (United States)

Travis, William D; Brambilla, Elisabeth; Nicholson, Andrew G; Yatabe, Yasushi; Austin, John H M; Beasley, Mary Beth; Chirieac, Lucian R; Dacic, Sanja; Duhig, Edwina; Flieder, Douglas B; Geisinger, Kim; Hirsch, Fred R; Ishikawa, Yuichi; Kerr, Keith M; Noguchi, Masayuki; Pelosi, Giuseppe; Powell, Charles A; Tsao, Ming Sound; Wistuba, Ignacio

2015-09-01

The 2015 World Health Organization (WHO) Classification of Tumors of the Lung, Pleura, Thymus and Heart has just been published with numerous important changes from the 2004 WHO classification. The most significant changes in this edition involve (1) use of immunohistochemistry throughout the classification, (2) a new emphasis on genetic studies, in particular, integration of molecular testing to help personalize treatment strategies for advanced lung cancer patients, (3) a new classification for small biopsies and cytology similar to that proposed in the 2011 Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification, (4) a completely different approach to lung adenocarcinoma as proposed by the 2011 Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification, (5) restricting the diagnosis of large cell carcinoma only to resected tumors that lack any clear morphologic or immunohistochemical differentiation with reclassification of the remaining former large cell carcinoma subtypes into different categories, (6) reclassifying squamous cell carcinomas into keratinizing, nonkeratinizing, and basaloid subtypes with the nonkeratinizing tumors requiring immunohistochemistry proof of squamous differentiation, (7) grouping of neuroendocrine tumors together in one category, (8) adding NUT carcinoma, (9) changing the term sclerosing hemangioma to sclerosing pneumocytoma, (10) changing the name hamartoma to "pulmonary hamartoma," (11) creating a group of PEComatous tumors that include (a) lymphangioleiomyomatosis, (b) PEComa, benign (with clear cell tumor as a variant) and (c) PEComa, malignant, (12) introducing the entity pulmonary myxoid sarcoma with an EWSR1-CREB1 translocation, (13) adding the entities myoepithelioma and myoepithelial carcinomas, which can show EWSR1 gene rearrangements, (14) recognition of usefulness of WWTR1-CAMTA1 fusions in diagnosis of epithelioid

Reliability of an e-PRO Tool of EORTC QLQ-C30 for Measurement of Health-Related Quality of Life in Patients With Breast Cancer: Prospective Randomized Trial.

Science.gov (United States)

Wallwiener, Markus; Matthies, Lina; Simoes, Elisabeth; Keilmann, Lucia; Hartkopf, Andreas D; Sokolov, Alexander N; Walter, Christina B; Sickenberger, Nina; Wallwiener, Stephanie; Feisst, Manuel; Gass, Paul; Fasching, Peter A; Lux, Michael P; Wallwiener, Diethelm; Taran, Florin-Andrei; Rom, Joachim; Schneeweiss, Andreas; Graf, Joachim; Brucker, Sara Y

2017-09-14

Breast cancer represents the most common malignant disease in women worldwide. As currently systematic palliative treatment only has a limited effect on survival rates, the concept of health-related quality of life (HRQoL) is gaining more and more importance in the therapy setting of metastatic breast cancer. One of the major patient-reported outcomes (PROs) for measuring HRQoL in patients with breast cancer is provided by the European Organization for Research and Treatment of Cancer (EORTC). Currently, paper-based surveys still predominate, as only a few reliable and validated electronic-based questionnaires are available. Facing the possibilities associated with evolving digitalization in medicine, validation of electronic versions of well-established PRO is essential in order to contribute to comprehensive and holistic oncological care and to ensure high quality in cancer research. The aim of this study was to analyze the reliability of a tablet-based measuring application for EORTC QLQ-C30 in German language in patients with adjuvant and (curative) metastatic breast cancer. Paper- and tablet-based questionnaires were completed by a total of 106 female patients with adjuvant and metastatic breast cancer recruited as part of the e-PROCOM study. All patients were required to complete the electronic- (e-PRO) and paper-based versions of the HRQoL EORTC QLQ-C30 questionnaire. A frequency analysis was performed to determine descriptive sociodemographic characteristics. Both dimensions of reliability (parallel forms reliability [Wilcoxon test] and test of internal consistency [Spearman rho and agreement rates for single items, Pearson correlation and Kendall tau for each scale]) were analyzed. High correlations were shown for both dimensions of reliability (parallel forms reliability and internal consistency) in the patient's response behavior between paper- and electronic-based questionnaires. Regarding the test of parallel forms reliability, no significant

An Entropy-based gene selection method for cancer classification using microarray data

Directory of Open Access Journals (Sweden)

Krishnan Arun

2005-03-01

Full Text Available Abstract Background Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of non-redundant but relevant genes is difficult. The selected gene set should be small enough to allow diagnosis even in regular clinical laboratories and ideally identify genes involved in cancer-specific regulatory pathways. Here an entropy-based method is proposed that selects genes related to the different cancer classes while at the same time reducing the redundancy among the genes. Results The present study identifies a subset of features by maximizing the relevance and minimizing the redundancy of the selected genes. A merit called normalized mutual information is employed to measure the relevance and the redundancy of the genes. In order to find a more representative subset of features, an iterative procedure is adopted that incorporates an initial clustering followed by data partitioning and the application of the algorithm to each of the partitions. A leave-one-out approach then selects the most commonly selected genes across all the different runs and the gene selection algorithm is applied again to pare down the list of selected genes until a minimal subset is obtained that gives a satisfactory accuracy of classification. The algorithm was applied to three different data sets and the results obtained were compared to work done by others using the same data sets Conclusion This study presents an entropy-based iterative algorithm for selecting genes from microarray data that are able to classify various cancer sub-types with high accuracy. In addition, the feature set obtained is very compact, that is, the redundancy between genes is reduced to a large extent. This implies that classifiers can be built with a smaller subset of genes.

Cancer cell detection and classification using transformation invariant template learning methods

International Nuclear Information System (INIS)

Talware, Rajendra; Abhyankar, Aditya

2011-01-01

In traditional cancer cell detection, pathologists examine biopsies to make diagnostic assessments, largely based on cell morphology and tissue distribution. The process of image acquisition is very much subjective and the pattern undergoes unknown or random transformations during data acquisition (e.g. variation in illumination, orientation, translation and perspective) results in high degree of variability. Transformed Component Analysis (TCA) incorporates a discrete, hidden variable that accounts for transformations and uses the Expectation Maximization (EM) algorithm to jointly extract components and normalize for transformations. Further the TEMPLAR framework developed takes advantage of hierarchical pattern models and adds probabilistic modeling for local transformations. Pattern classification is based on Expectation Maximization algorithm and General Likelihood Ratio Tests (GLRT). Performance of TEMPLAR is certainly improved by defining area of interest on slide a priori. Performance can be further enhanced by making the kernel function adaptive during learning. (author)

Thermographic image analysis for classification of ACL rupture disease, bone cancer, and feline hyperthyroid, with Gabor filters

Science.gov (United States)

Alvandipour, Mehrdad; Umbaugh, Scott E.; Mishra, Deependra K.; Dahal, Rohini; Lama, Norsang; Marino, Dominic J.; Sackman, Joseph

2017-05-01

Thermography and pattern classification techniques are used to classify three different pathologies in veterinary images. Thermographic images of both normal and diseased animals were provided by the Long Island Veterinary Specialists (LIVS). The three pathologies are ACL rupture disease, bone cancer, and feline hyperthyroid. The diagnosis of these diseases usually involves radiology and laboratory tests while the method that we propose uses thermographic images and image analysis techniques and is intended for use as a prescreening tool. Images in each category of pathologies are first filtered by Gabor filters and then various features are extracted and used for classification into normal and abnormal classes. Gabor filters are linear filters that can be characterized by the two parameters wavelength λ and orientation θ. With two different wavelength and five different orientations, a total of ten different filters were studied. Different combinations of camera views, filters, feature vectors, normalization methods, and classification methods, produce different tests that were examined and the sensitivity, specificity and success rate for each test were produced. Using the Gabor features alone, sensitivity, specificity, and overall success rates of 85% for each of the pathologies was achieved.

Interval-value Based Particle Swarm Optimization algorithm for cancer-type specific gene selection and sample classification

Directory of Open Access Journals (Sweden)

D. Ramyachitra

2015-09-01

Full Text Available Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM, K-nearest neighbor (KNN, Interval Valued Classification (IVC and the improvised Interval Value based Particle Swarm Optimization (IVPSO algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.

Interval-value Based Particle Swarm Optimization algorithm for cancer-type specific gene selection and sample classification.

Science.gov (United States)

Ramyachitra, D; Sofia, M; Manikandan, P

2015-09-01

Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM), K-nearest neighbor (KNN), Interval Valued Classification (IVC) and the improvised Interval Value based Particle Swarm Optimization (IVPSO) algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.

[Magnetic resonance semiotics of prostate cancer according to the PI-RADS classification. The clinical diagnostic algorithm of a study].

Science.gov (United States)

Korobkin, A S; Shariya, M A; Chaban, A S; Voskanvan, G A; Vinarov, A Z

2015-01-01

to elaborate the magnetic resonance imaging (MRI) signs of prostate cancer (PC) in accordance with the PI-RADS classification during multiparametric MRI (mpMRI). A total of 89 men aged 20 to 82 years were examined. A control group consisted of 8 (9%) healthy volunteers younger than 30 years of age with no urological history to obtain control images and MRI plots and 20 (22.5%) men aged 26-76 years, whose morphological changes were inflammatory and hyperplastic. The second age-matched group included 61 (68.5%) patients diagnosed with prostate cancer at morphological examination. A set of studies included digital rectal examination, serum prostate-specific antigen, and transrectal ultrasound-guided prostate biopsy. All the patients underwent prostate mpMRI applying a 3.0 T Achieva MRI scanner (Philips, the Netherlands). The patients have been found to have mpMRI signs that were typical of PC; its MRI semiotics according to the PI-RADS classification is presented. Each mpMRI procedure has been determined to be of importance and informative value in detecting PC. The comprehensive mpMRI approach to diagnosing PC improves the quality and diagnostic value of prostate MRI.

Reliability analysis and operator modelling

International Nuclear Information System (INIS)

Hollnagel, Erik

1996-01-01

The paper considers the state of operator modelling in reliability analysis. Operator models are needed in reliability analysis because operators are needed in process control systems. HRA methods must therefore be able to account both for human performance variability and for the dynamics of the interaction. A selected set of first generation HRA approaches is briefly described in terms of the operator model they use, their classification principle, and the actual method they propose. In addition, two examples of second generation methods are also considered. It is concluded that first generation HRA methods generally have very simplistic operator models, either referring to the time-reliability relationship or to elementary information processing concepts. It is argued that second generation HRA methods must recognise that cognition is embedded in a context, and be able to account for that in the way human reliability is analysed and assessed

Superpixel-based classification of gastric chromoendoscopy images

Science.gov (United States)

Boschetto, Davide; Grisan, Enrico

2017-03-01

Chromoendoscopy (CH) is a gastroenterology imaging modality that involves the staining of tissues with methylene blue, which reacts with the internal walls of the gastrointestinal tract, improving the visual contrast in mucosal surfaces and thus enhancing a doctor's ability to screen precancerous lesions or early cancer. This technique helps identify areas that can be targeted for biopsy or treatment and in this work we will focus on gastric cancer detection. Gastric chromoendoscopy for cancer detection has several taxonomies available, one of which classifies CH images into three classes (normal, metaplasia, dysplasia) based on color, shape and regularity of pit patterns. Computer-assisted diagnosis is desirable to help us improve the reliability of the tissue classification and abnormalities detection. However, traditional computer vision methodologies, mainly segmentation, do not translate well to the specific visual characteristics of a gastroenterology imaging scenario. We propose the exploitation of a first unsupervised segmentation via superpixel, which groups pixels into perceptually meaningful atomic regions, used to replace the rigid structure of the pixel grid. For each superpixel, a set of features is extracted and then fed to a random forest based classifier, which computes a model used to predict the class of each superpixel. The average general accuracy of our model is 92.05% in the pixel domain (86.62% in the superpixel domain), while detection accuracies on the normal and abnormal class are respectively 85.71% and 95%. Eventually, the whole image class can be predicted image through a majority vote on each superpixel's predicted class.

Implementation of several mathematical algorithms to breast tissue density classification

International Nuclear Information System (INIS)

Quintana, C.; Redondo, M.; Tirao, G.

2014-01-01

The accuracy of mammographic abnormality detection methods is strongly dependent on breast tissue characteristics, where a dense breast tissue can hide lesions causing cancer to be detected at later stages. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. This paper presents the implementation and the performance of different mathematical algorithms designed to standardize the categorization of mammographic images, according to the American College of Radiology classifications. These mathematical techniques are based on intrinsic properties calculations and on comparison with an ideal homogeneous image (joint entropy, mutual information, normalized cross correlation and index Q) as categorization parameters. The algorithms evaluation was performed on 100 cases of the mammographic data sets provided by the Ministerio de Salud de la Provincia de Córdoba, Argentina—Programa de Prevención del Cáncer de Mama (Department of Public Health, Córdoba, Argentina, Breast Cancer Prevention Program). The obtained breast classifications were compared with the expert medical diagnostics, showing a good performance. The implemented algorithms revealed a high potentiality to classify breasts into tissue density categories. - Highlights: • Breast density classification can be obtained by suitable mathematical algorithms. • Mathematical processing help radiologists to obtain the BI-RADS classification. • The entropy and joint entropy show high performance for density classification

Weakly supervised classification in high energy physics

Energy Technology Data Exchange (ETDEWEB)

Dery, Lucio Mwinmaarong [Physics Department, Stanford University,Stanford, CA, 94305 (United States); Nachman, Benjamin [Physics Division, Lawrence Berkeley National Laboratory,1 Cyclotron Rd, Berkeley, CA, 94720 (United States); Rubbo, Francesco; Schwartzman, Ariel [SLAC National Accelerator Laboratory, Stanford University,2575 Sand Hill Rd, Menlo Park, CA, 94025 (United States)

2017-05-29

As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics — quark versus gluon tagging — we show that weakly supervised classification can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Weakly supervised classification is a general procedure that can be applied to a wide variety of learning problems to boost performance and robustness when detailed simulations are not reliable or not available.

Weakly supervised classification in high energy physics

International Nuclear Information System (INIS)

Dery, Lucio Mwinmaarong; Nachman, Benjamin; Rubbo, Francesco; Schwartzman, Ariel

2017-01-01

As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics — quark versus gluon tagging — we show that weakly supervised classification can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Weakly supervised classification is a general procedure that can be applied to a wide variety of learning problems to boost performance and robustness when detailed simulations are not reliable or not available.

Oral epithelial dysplasia classification systems

DEFF Research Database (Denmark)

Warnakulasuriya, S; Reibel, J; Bouquot, J

2008-01-01

At a workshop coordinated by the WHO Collaborating Centre for Oral Cancer and Precancer in the United Kingdom issues related to potentially malignant disorders of the oral cavity were discussed by an expert group. The consensus views of the Working Group are presented in a series of papers....... In this report, we review the oral epithelial dysplasia classification systems. The three classification schemes [oral epithelial dysplasia scoring system, squamous intraepithelial neoplasia and Ljubljana classification] were presented and the Working Group recommended epithelial dysplasia grading for routine...... use. Although most oral pathologists possibly recognize and accept the criteria for grading epithelial dysplasia, firstly based on architectural features and then of cytology, there is great variability in their interpretation of the presence, degree and significance of the individual criteria...

Collection and classification of human reliability data for use in probabilistic safety assessments. Final report of a co-ordinated research programme 1995-1998

International Nuclear Information System (INIS)

1998-10-01

One of the most important lessons from abnormal events in NPPs is that they often result from incorrect human action. The awareness of the importance of human factors and human reliability has increased significantly over 10-15 years primarily owing to the fact that some major incidents (nuclear or non-nuclear) have had significant human error contributions. Each of these incidents have revealed different types of human errors, some of which were not generally recognized prior to the incident. The analysis of these events led to wide recognition of the fact that more information about human actions and errors is needed to improve the safety and operation of nuclear power plants. At the same time, the need or proper human reliability data was recognised in view of probabilistic safety assessment (PSA). No PSA study can be regarded as complete and accurate without adequate incorporation of human reliability analysis (HRA). In order to support incorporation of human reliability data into PSA the IAEA established a coordinated research programme with the objective to develop a common data base structure for human errors that might have important contributions to risk in different types of reactors. This report is a product of four years of coordinated research and describes the data collection and classification schemes currently in use in Member States as well as an outlook into future, discussing what types of data might be needed to support the new improved HRA methods which are currently under development

Measurement of the Inter-Rater Reliability Rate Is Mandatory for Improving the Quality of a Medical Database: Experience with the Paulista Lung Cancer Registry.

Science.gov (United States)

Lauricella, Leticia L; Costa, Priscila B; Salati, Michele; Pego-Fernandes, Paulo M; Terra, Ricardo M

2018-06-01

Database quality measurement should be considered a mandatory step to ensure an adequate level of confidence in data used for research and quality improvement. Several metrics have been described in the literature, but no standardized approach has been established. We aimed to describe a methodological approach applied to measure the quality and inter-rater reliability of a regional multicentric thoracic surgical database (Paulista Lung Cancer Registry). Data from the first 3 years of the Paulista Lung Cancer Registry underwent an audit process with 3 metrics: completeness, consistency, and inter-rater reliability. The first 2 methods were applied to the whole data set, and the last method was calculated using 100 cases randomized for direct auditing. Inter-rater reliability was evaluated using percentage of agreement between the data collector and auditor and through calculation of Cohen's κ and intraclass correlation. The overall completeness per section ranged from 0.88 to 1.00, and the overall consistency was 0.96. Inter-rater reliability showed many variables with high disagreement (>10%). For numerical variables, intraclass correlation was a better metric than inter-rater reliability. Cohen's κ showed that most variables had moderate to substantial agreement. The methodological approach applied to the Paulista Lung Cancer Registry showed that completeness and consistency metrics did not sufficiently reflect the real quality status of a database. The inter-rater reliability associated with κ and intraclass correlation was a better quality metric than completeness and consistency metrics because it could determine the reliability of specific variables used in research or benchmark reports. This report can be a paradigm for future studies of data quality measurement. Copyright © 2018 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Clinically orientated classification incorporating shoulder balance for the surgical treatment of adolescent idiopathic scoliosis.

Science.gov (United States)

Elsebaie, H B; Dannawi, Z; Altaf, F; Zaidan, A; Al Mukhtar, M; Shaw, M J; Gibson, A; Noordeen, H

2016-02-01

The achievement of shoulder balance is an important measure of successful scoliosis surgery. No previously described classification system has taken shoulder balance into account. We propose a simple classification system for AIS based on two components which include the curve type and shoulder level. Altogether, three curve types have been defined according to the size and location of the curves, each curve pattern is subdivided into type A or B depending on the shoulder level. This classification was tested for interobserver reproducibility and intraobserver reliability. A retrospective analysis of the radiographs of 232 consecutive cases of AIS patients treated surgically between 2005 and 2009 was also performed. Three major types and six subtypes were identified. Type I accounted for 30 %, type II 28 % and type III 42 %. The retrospective analysis showed three patients developed a decompensation that required extension of the fusion. One case developed worsening of shoulder balance requiring further surgery. This classification was tested for interobserver and intraobserver reliability. The mean kappa coefficients for interobserver reproducibility ranged from 0.89 to 0.952, while the mean kappa value for intraobserver reliability was 0.964 indicating a good-to-excellent reliability. The treatment algorithm guides the spinal surgeon to achieve optimal curve correction and postoperative shoulder balance whilst fusing the smallest number of spinal segments. The high interobserver reproducibility and intraobserver reliability makes it an invaluable tool to describe scoliosis curves in everyday clinical practice.

Stratification and Prognostic Relevance of Jass’s Molecular Classification of Colorectal Cancer

International Nuclear Information System (INIS)

Zlobec, Inti; Bihl, Michel P.; Foerster, Anja; Rufle, Alex; Terracciano, Luigi; Lugli, Alessandro

2012-01-01

Background: The current proposed model of colorectal tumorigenesis is based primarily on CpG island methylator phenotype (CIMP), microsatellite instability (MSI), KRAS, BRAF, and methylation status of 0-6-Methylguanine DNA Methyltransferase (MGMT) and classifies tumors into five subgroups. The aim of this study is to validate this molecular classification and test its prognostic relevance. Methods: Three hundred two patients were included in this study. Molecular analysis was performed for five CIMP-related promoters (CRABP1, MLH1, p16INK4a, CACNA1G, NEUROG1), MGMT, MSI, KRAS, and BRAF. Methylation in at least 4 promoters or in one to three promoters was considered CIMP-high and CIMP-low (CIMP-H/L), respectively. Results: CIMP-H, CIMP-L, and CIMP-negative were found in 7.1, 43, and 49.9% cases, respectively. One hundred twenty-three tumors (41%) could not be classified into any one of the proposed molecular subgroups, including 107 CIMP-L, 14 CIMP-H, and two CIMP-negative cases. The 10 year survival rate for CIMP-high patients [22.6% (95%CI: 7–43)] was significantly lower than for CIMP-L or CIMP-negative (p = 0.0295). Only the combined analysis of BRAF and CIMP (negative versus L/H) led to distinct prognostic subgroups. Conclusion: Although CIMP status has an effect on outcome, our results underline the need for standardized definitions of low- and high-level CIMP, which clearly hinders an effective prognostic and molecular classification of colorectal cancer.

Stratification and Prognostic Relevance of Jass’s Molecular Classification of Colorectal Cancer

Energy Technology Data Exchange (ETDEWEB)

Zlobec, Inti [Institute of Pathology, University of Bern, Bern (Switzerland); Institute for Pathology, University Hospital Basel, Basel (Switzerland); Bihl, Michel P.; Foerster, Anja; Rufle, Alex; Terracciano, Luigi [Institute for Pathology, University Hospital Basel, Basel (Switzerland); Lugli, Alessandro, E-mail: inti.zlobec@pathology.unibe.ch [Institute of Pathology, University of Bern, Bern (Switzerland); Institute for Pathology, University Hospital Basel, Basel (Switzerland)

2012-02-27

Background: The current proposed model of colorectal tumorigenesis is based primarily on CpG island methylator phenotype (CIMP), microsatellite instability (MSI), KRAS, BRAF, and methylation status of 0-6-Methylguanine DNA Methyltransferase (MGMT) and classifies tumors into five subgroups. The aim of this study is to validate this molecular classification and test its prognostic relevance. Methods: Three hundred two patients were included in this study. Molecular analysis was performed for five CIMP-related promoters (CRABP1, MLH1, p16INK4a, CACNA1G, NEUROG1), MGMT, MSI, KRAS, and BRAF. Methylation in at least 4 promoters or in one to three promoters was considered CIMP-high and CIMP-low (CIMP-H/L), respectively. Results: CIMP-H, CIMP-L, and CIMP-negative were found in 7.1, 43, and 49.9% cases, respectively. One hundred twenty-three tumors (41%) could not be classified into any one of the proposed molecular subgroups, including 107 CIMP-L, 14 CIMP-H, and two CIMP-negative cases. The 10 year survival rate for CIMP-high patients [22.6% (95%CI: 7–43)] was significantly lower than for CIMP-L or CIMP-negative (p = 0.0295). Only the combined analysis of BRAF and CIMP (negative versus L/H) led to distinct prognostic subgroups. Conclusion: Although CIMP status has an effect on outcome, our results underline the need for standardized definitions of low- and high-level CIMP, which clearly hinders an effective prognostic and molecular classification of colorectal cancer.

Stratification and prognostic relevance of Jass’s molecular classification of colorectal cancer

Directory of Open Access Journals (Sweden)

Inti eZlobec

2012-02-01

Full Text Available Background: The current proposed model of colorectal tumorigenesis is based primarily on CpG island methylator phenotype (CIMP, microsatellite instability (MSI, KRAS, BRAF, and methylation status of 0-6-Methylguanine DNA Methyltransferase (MGMT and classifies tumors into 5 subgroups. The aim of this study is to validate this molecular classification and test its prognostic relevance. Methods: 302 patients were included in this study. Molecular analysis was performed for 5 CIMP-related promoters (CRABP1, MLH1, p16INK4a, CACNA1G, NEUROG1, MGMT, MSI, KRAS and BRAF. Tumors were CIMP-high or CIMP-low if ≥4 and 1-3 promoters were methylated, respectively. Results: CIMP-high, CIMP-low and CIMP–negative were found in 7.1%, 43% and 49.9% cases, respectively. 123 tumors (41% could not be classified into any one of the proposed molecular subgroups, including 107 CIMP-low, 14 CIMP-high and 2 CIMP-negative cases. The 10-year survival rate for CIMP-high patients (22.6% (95%CI: 7-43 was significantly lower than for CIMP-low or CIMP-negative (p=0.0295. Only the combined analysis of BRAF and CIMP (negative versus low/high led to distinct prognostic subgroups. Conclusion: Although CIMP status has an effect on outcome, our results underline the need for standardized definitions of low- and high-level CIMP, which clearly hinders an effective prognostic and molecular classification of colorectal cancer.

Automotive System for Remote Surface Classification.

Science.gov (United States)

Bystrov, Aleksandr; Hoare, Edward; Tran, Thuy-Yung; Clarke, Nigel; Gashinova, Marina; Cherniakov, Mikhail

2017-04-01

In this paper we shall discuss a novel approach to road surface recognition, based on the analysis of backscattered microwave and ultrasonic signals. The novelty of our method is sonar and polarimetric radar data fusion, extraction of features for separate swathes of illuminated surface (segmentation), and using of multi-stage artificial neural network for surface classification. The developed system consists of 24 GHz radar and 40 kHz ultrasonic sensor. The features are extracted from backscattered signals and then the procedures of principal component analysis and supervised classification are applied to feature data. The special attention is paid to multi-stage artificial neural network which allows an overall increase in classification accuracy. The proposed technique was tested for recognition of a large number of real surfaces in different weather conditions with the average accuracy of correct classification of 95%. The obtained results thereby demonstrate that the use of proposed system architecture and statistical methods allow for reliable discrimination of various road surfaces in real conditions.

What should an ideal spinal injury classification system consist of? A methodological review and conceptual proposal for future classifications.

NARCIS (Netherlands)

Middendorp, J.J. van; Audige, L.; Hanson, B.; Chapman, J.R.; Hosman, A.J.F.

2010-01-01

Since Bohler published the first categorization of spinal injuries based on plain radiographic examinations in 1929, numerous classifications have been proposed. Despite all these efforts, however, only a few have been tested for reliability and validity. This methodological, conceptual review

The validity and reliability of the 'Cancer Caregiving Tasks, Consequences and Needs Questionnaire' (CaTCoN)

DEFF Research Database (Denmark)

Lund, Line; Ross, Lone; Petersen, Morten A

2014-01-01

and reliability of the multi-item scales in the CaTCoN using psychometric analyses as well as tests of convergent and discriminant validity with the existing instruments FAMCARE and Family Inventory of Needs (FIN). Material and methods. Based on theoretical considerations, a subscale structure in the Ca......TCoN and the existing questionnaires FAMCARE and FIN. Conclusion. Taken together the psychometric analyses and tests of convergent and discriminant validity indicate that the validity and reliability of the CaTCoN are satisfactory.......Background. Caregivers are often involved in and affected by the patient's disease. The questionnaire 'Cancer Caregiving Tasks, Consequences and Needs Questionnaire' (CaTCoN) was developed to measure caregivers' experiences. The aim of this study is to evaluate the construct validity...

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

Directory of Open Access Journals (Sweden)

Hala Alshamlan

2015-01-01

Full Text Available An artificial bee colony (ABC is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR, and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO. The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.

Science.gov (United States)

Alshamlan, Hala; Badr, Ghada; Alohali, Yousef

2015-01-01

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

Interobserver Agreement of the Eaton-Glickel Classification for Trapeziometacarpal and Scaphotrapezial Arthrosis.

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Becker, Stéphanie J E; Bruinsma, Wendy E; Guitton, Thierry G; van der Horst, Chantal M A M; Strackee, Simon D; Ring, David

2016-04-01

To determine whether simplification of the Eaton-Glickel (E-G) classification of trapeziometacarpal (TMC) joint arthrosis (eliminating evaluation of the scaphotrapezial [ST] joint) and information about the patient's symptoms and examination influence interobserver reliability. We also tested the null hypotheses that no patient and/or surgeon factors affect radiographic rating of TMC joint arthrosis and that no surgeon factors affect the radiographic rating of ST joint arthrosis. In an on-line survey, 92 hand surgeons rated TMC joint arthrosis and ST joint arthrosis separately on 30 radiographs (Robert, true lateral, and oblique views) according to the (modified) E-G classification. We randomly assigned 42 observers to review radiographs alone and also informed 50 of the patient's symptoms and examination. Information about symptoms and examination was randomized. Interobserver reliability was determined with the s* statistic. Because of the hierarchical data structure, cross-classified ordinal multilevel regression analyses were performed to identify factors associated with the severity of arthrosis. Shortening the E-G classification to the first 3 stages significantly improved the interobserver reliability, which approached substantial agreement. Providing clinical information to observers marginally improved interobserver reliability. Factors associated with a lower E-G stage for TMC joint arthrosis, among observers who rated the severity of TMC joint arthrosis based on radiographs and clinical information, included female surgeon, practice setting, supervising surgical trainees in the operating room, self-reported number of patients with TMC joint arthrosis typically treated annually, male patient, higher patient age, pain limiting daily activities, and shoulder sign. A self-reported larger number of patients with TMC joint arthrosis treated annually was the only variable associated with a higher modified E-G classification to rate ST joint arthrosis. Our

Efficient Fingercode Classification

Science.gov (United States)

Sun, Hong-Wei; Law, Kwok-Yan; Gollmann, Dieter; Chung, Siu-Leung; Li, Jian-Bin; Sun, Jia-Guang

In this paper, we present an efficient fingerprint classification algorithm which is an essential component in many critical security application systems e. g. systems in the e-government and e-finance domains. Fingerprint identification is one of the most important security requirements in homeland security systems such as personnel screening and anti-money laundering. The problem of fingerprint identification involves searching (matching) the fingerprint of a person against each of the fingerprints of all registered persons. To enhance performance and reliability, a common approach is to reduce the search space by firstly classifying the fingerprints and then performing the search in the respective class. Jain et al. proposed a fingerprint classification algorithm based on a two-stage classifier, which uses a K-nearest neighbor classifier in its first stage. The fingerprint classification algorithm is based on the fingercode representation which is an encoding of fingerprints that has been demonstrated to be an effective fingerprint biometric scheme because of its ability to capture both local and global details in a fingerprint image. We enhance this approach by improving the efficiency of the K-nearest neighbor classifier for fingercode-based fingerprint classification. Our research firstly investigates the various fast search algorithms in vector quantization (VQ) and the potential application in fingerprint classification, and then proposes two efficient algorithms based on the pyramid-based search algorithms in VQ. Experimental results on DB1 of FVC 2004 demonstrate that our algorithms can outperform the full search algorithm and the original pyramid-based search algorithms in terms of computational efficiency without sacrificing accuracy.

Precision of lumbar intervertebral measurements: does a computer-assisted technique improve reliability?

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Pearson, Adam M; Spratt, Kevin F; Genuario, James; McGough, William; Kosman, Katherine; Lurie, Jon; Sengupta, Dilip K

2011-04-01

Comparison of intra- and interobserver reliability of digitized manual and computer-assisted intervertebral motion measurements and classification of "instability." To determine if computer-assisted measurement of lumbar intervertebral motion on flexion-extension radiographs improves reliability compared with digitized manual measurements. Many studies have questioned the reliability of manual intervertebral measurements, although few have compared the reliability of computer-assisted and manual measurements on lumbar flexion-extension radiographs. Intervertebral rotation, anterior-posterior (AP) translation, and change in anterior and posterior disc height were measured with a digitized manual technique by three physicians and by three other observers using computer-assisted quantitative motion analysis (QMA) software. Each observer measured 30 sets of digital flexion-extension radiographs (L1-S1) twice. Shrout-Fleiss intraclass correlation coefficients for intra- and interobserver reliabilities were computed. The stability of each level was also classified (instability defined as >4 mm AP translation or 10° rotation), and the intra- and interobserver reliabilities of the two methods were compared using adjusted percent agreement (APA). Intraobserver reliability intraclass correlation coefficients were substantially higher for the QMA technique THAN the digitized manual technique across all measurements: rotation 0.997 versus 0.870, AP translation 0.959 versus 0.557, change in anterior disc height 0.962 versus 0.770, and change in posterior disc height 0.951 versus 0.283. The same pattern was observed for interobserver reliability (rotation 0.962 vs. 0.693, AP translation 0.862 vs. 0.151, change in anterior disc height 0.862 vs. 0.373, and change in posterior disc height 0.730 vs. 0.300). The QMA technique was also more reliable for the classification of "instability." Intraobserver APAs ranged from 87 to 97% for QMA versus 60% to 73% for digitized manual

International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma.

Science.gov (United States)

Travis, William D; Brambilla, Elisabeth; Noguchi, Masayuki; Nicholson, Andrew G; Geisinger, Kim R; Yatabe, Yasushi; Beer, David G; Powell, Charles A; Riely, Gregory J; Van Schil, Paul E; Garg, Kavita; Austin, John H M; Asamura, Hisao; Rusch, Valerie W; Hirsch, Fred R; Scagliotti, Giorgio; Mitsudomi, Tetsuya; Huber, Rudolf M; Ishikawa, Yuichi; Jett, James; Sanchez-Cespedes, Montserrat; Sculier, Jean-Paul; Takahashi, Takashi; Tsuboi, Masahiro; Vansteenkiste, Johan; Wistuba, Ignacio; Yang, Pan-Chyr; Aberle, Denise; Brambilla, Christian; Flieder, Douglas; Franklin, Wilbur; Gazdar, Adi; Gould, Michael; Hasleton, Philip; Henderson, Douglas; Johnson, Bruce; Johnson, David; Kerr, Keith; Kuriyama, Keiko; Lee, Jin Soo; Miller, Vincent A; Petersen, Iver; Roggli, Victor; Rosell, Rafael; Saijo, Nagahiro; Thunnissen, Erik; Tsao, Ming; Yankelewitz, David

2011-02-01

Adenocarcinoma is the most common histologic type of lung cancer. To address advances in oncology, molecular biology, pathology, radiology, and surgery of lung adenocarcinoma, an international multidisciplinary classification was sponsored by the International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society. This new adenocarcinoma classification is needed to provide uniform terminology and diagnostic criteria, especially for bronchioloalveolar carcinoma (BAC), the overall approach to small nonresection cancer specimens, and for multidisciplinary strategic management of tissue for molecular and immunohistochemical studies. An international core panel of experts representing all three societies was formed with oncologists/pulmonologists, pathologists, radiologists, molecular biologists, and thoracic surgeons. A systematic review was performed under the guidance of the American Thoracic Society Documents Development and Implementation Committee. The search strategy identified 11,368 citations of which 312 articles met specified eligibility criteria and were retrieved for full text review. A series of meetings were held to discuss the development of the new classification, to develop the recommendations, and to write the current document. Recommendations for key questions were graded by strength and quality of the evidence according to the Grades of Recommendation, Assessment, Development, and Evaluation approach. The classification addresses both resection specimens, and small biopsies and cytology. The terms BAC and mixed subtype adenocarcinoma are no longer used. For resection specimens, new concepts are introduced such as adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) for small solitary adenocarcinomas with either pure lepidic growth (AIS) or predominant lepidic growth with ≤ 5 mm invasion (MIA) to define patients who, if they undergo complete resection, will have 100% or near 100

Current Trends in the Molecular Classification of Renal Neoplasms

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Andrew N. Young

2006-01-01

Full Text Available Renal cell carcinoma (RCC is the most common form of kidney cancer in adults. RCC is a significant challenge for pathologic diagnosis and clinical management. The primary approach to diagnosis is by light microscopy, using the World Health Organization (WHO classification system, which defines histopathologic tumor subtypes with distinct clinical behavior and underlying genetic mutations. However, light microscopic diagnosis of RCC subtypes is often difficult due to variable histology. In addition, the clinical behavior of RCC is highly variable and therapeutic response rates are poor. Few clinical assays are available to predict outcome in RCC or correlate behavior with histology. Therefore, novel RCC classification systems based on gene expression should be useful for diagnosis, prognosis, and treatment. Recent microarray studies have shown that renal tumors are characterized by distinct gene expression profiles, which can be used to discover novel diagnostic and prognostic biomarkers. Here, we review clinical features of kidney cancer, the WHO classification system, and the growing role of molecular classification for diagnosis, prognosis, and therapy of this disease.

A fingerprint classification algorithm based on combination of local and global information

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Liu, Chongjin; Fu, Xiang; Bian, Junjie; Feng, Jufu

2011-12-01

Fingerprint recognition is one of the most important technologies in biometric identification and has been wildly applied in commercial and forensic areas. Fingerprint classification, as the fundamental procedure in fingerprint recognition, can sharply decrease the quantity for fingerprint matching and improve the efficiency of fingerprint recognition. Most fingerprint classification algorithms are based on the number and position of singular points. Because the singular points detecting method only considers the local information commonly, the classification algorithms are sensitive to noise. In this paper, we propose a novel fingerprint classification algorithm combining the local and global information of fingerprint. Firstly we use local information to detect singular points and measure their quality considering orientation structure and image texture in adjacent areas. Furthermore the global orientation model is adopted to measure the reliability of singular points group. Finally the local quality and global reliability is weighted to classify fingerprint. Experiments demonstrate the accuracy and effectivity of our algorithm especially for the poor quality fingerprint images.

Assessing Unmet Information Needs of Breast Cancer Survivors: Exploratory Study of Online Health Forums Using Text Classification and Retrieval.

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McRoy, Susan; Rastegar-Mojarad, Majid; Wang, Yanshan; Ruddy, Kathryn J; Haddad, Tufia C; Liu, Hongfang

2018-05-15

Patient education materials given to breast cancer survivors may not be a good fit for their information needs. Needs may change over time, be forgotten, or be misreported, for a variety of reasons. An automated content analysis of survivors' postings to online health forums can identify expressed information needs over a span of time and be repeated regularly at low cost. Identifying these unmet needs can guide improvements to existing education materials and the creation of new resources. The primary goals of this project are to assess the unmet information needs of breast cancer survivors from their own perspectives and to identify gaps between information needs and current education materials. This approach employs computational methods for content modeling and supervised text classification to data from online health forums to identify explicit and implicit requests for health-related information. Potential gaps between needs and education materials are identified using techniques from information retrieval. We provide a new taxonomy for the classification of sentences in online health forum data. 260 postings from two online health forums were selected, yielding 4179 sentences for coding. After annotation of data and training alternative one-versus-others classifiers, a random forest-based approach achieved F1 scores from 66% (Other, dataset2) to 90% (Medical, dataset1) on the primary information types. 136 expressions of need were used to generate queries to indexed education materials. Upon examination of the best two pages retrieved for each query, 12% (17/136) of queries were found to have relevant content by all coders, and 33% (45/136) were judged to have relevant content by at least one. Text from online health forums can be analyzed effectively using automated methods. Our analysis confirms that breast cancer survivors have many information needs that are not covered by the written documents they typically receive, as our results suggest that at most

The classification of lung cancers and their degree of malignancy by FTIR, PCA-LDA analysis, and a physics-based computational model.

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Kaznowska, E; Depciuch, J; Łach, K; Kołodziej, M; Koziorowska, A; Vongsvivut, J; Zawlik, I; Cholewa, M; Cebulski, J

2018-08-15

Lung cancer has the highest mortality rate of all malignant tumours. The current effects of cancer treatment, as well as its diagnostics, are unsatisfactory. Therefore it is very important to introduce modern diagnostic tools, which will allow for rapid classification of lung cancers and their degree of malignancy. For this purpose, the authors propose the use of Fourier Transform InfraRed (FTIR) spectroscopy combined with Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) and a physics-based computational model. The results obtained for lung cancer tissues, adenocarcinoma and squamous cell carcinoma FTIR spectra, show a shift in wavenumbers compared to control tissue FTIR spectra. Furthermore, in the FTIR spectra of adenocarcinoma there are no peaks corresponding to glutamate or phospholipid functional groups. Moreover, in the case of G2 and G3 malignancy of adenocarcinoma lung cancer, the absence of an OH groups peak was noticed. Thus, it seems that FTIR spectroscopy is a valuable tool to classify lung cancer and to determine the degree of its malignancy. Copyright © 2018 Elsevier B.V. All rights reserved.

Development and evaluation of automated systems for detection and classification of banded chromosomes: current status and future perspectives

International Nuclear Information System (INIS)

Wang Xingwei; Zheng Bin; Wood, Marc; Li Shibo; Chen Wei; Liu Hong

2005-01-01

Automated detection and classification of banded chromosomes may help clinicians diagnose cancers and other genetic disorders at an early stage more efficiently and accurately. However, developing such an automated system (including both a high-speed microscopic image scanning device and related computer-assisted schemes) is quite a challenging and difficult task. Since the 1980s, great research efforts have been made to develop fast and more reliable methods to assist clinical technicians in performing this important and time-consuming task. A number of computer-assisted methods including classical statistical methods, artificial neural networks and knowledge-based fuzzy logic systems, have been applied and tested. Based on the initial test using limited datasets, encouraging results in algorithm and system development have been demonstrated. Despite the significant research effort and progress made over the last two decades, computer-assisted chromosome detection and classification systems have not been routinely accepted and used in clinical laboratories. Further research and development is needed

Development and evaluation of automated systems for detection and classification of banded chromosomes: current status and future perspectives

Energy Technology Data Exchange (ETDEWEB)

Wang Xingwei [Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, OK (United States); Zheng Bin [Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA (United States); Wood, Marc [Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, OK (United States); Li Shibo [Department of Pediatrics, University of Oklahoma Medical Center, Oklahoma City, OK (United States); Chen Wei [Department of Physics and Engineering, University of Central Oklahoma, Edmond, OK (United States); Liu Hong [Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, OK (United States)

2005-08-07

Automated detection and classification of banded chromosomes may help clinicians diagnose cancers and other genetic disorders at an early stage more efficiently and accurately. However, developing such an automated system (including both a high-speed microscopic image scanning device and related computer-assisted schemes) is quite a challenging and difficult task. Since the 1980s, great research efforts have been made to develop fast and more reliable methods to assist clinical technicians in performing this important and time-consuming task. A number of computer-assisted methods including classical statistical methods, artificial neural networks and knowledge-based fuzzy logic systems, have been applied and tested. Based on the initial test using limited datasets, encouraging results in algorithm and system development have been demonstrated. Despite the significant research effort and progress made over the last two decades, computer-assisted chromosome detection and classification systems have not been routinely accepted and used in clinical laboratories. Further research and development is needed.

Clinical and Pathological Staging Validation in the Eighth Edition of the TNM Classification for Lung Cancer: Correlation between Solid Size on Thin-Section Computed Tomography and Invasive Size in Pathological Findings in the New T Classification.

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Aokage, Keiju; Miyoshi, Tomohiro; Ishii, Genichiro; Kusumoto, Masahiro; Nomura, Shogo; Katsumata, Shinya; Sekihara, Keigo; Hishida, Tomoyuki; Tsuboi, Masahiro

2017-09-01

The aim of this study was to validate the new eighth edition of the TNM classification and to elucidate whether radiological solid size corresponds to pathological invasive size incorporated in this T factor. We analyzed the data on 1792 patients who underwent complete resection from 2003 to 2011 at the National Cancer Center Hospital East, Japan. We reevaluated preoperative thin-section computed tomography (TSCT) to determine solid size and pathological invasive size using the fourth edition of the WHO classification and reclassified them according to the new TNM classification. The discriminative power of survival curves by the seventh edition was compared with that by the eighth edition by using concordance probability estimates and Akaike's information criteria calculated using a univariable Cox regression model. Pearson's correlation coefficient was calculated to elucidate the correlation between radiological solid size using TSCT and pathological invasive size. The overall survival curves in the eighth edition were well distinct at each clinical and pathological stage. The 5-year survival rates of patients with clinical and pathological stage 0 newly defined were both 100%. The concordance probability estimate and Akaike's information criterion values of the eighth edition were higher than those of the seventh edition in discriminatory power for overall survival. Solid size on TSCT scan and pathological invasive size showed a positive linear relationship, and Pearson's correlation coefficient was calculated as 0.83, which indicated strong correlation. This TNM classification will be feasible regarding patient survival, and radiological solid size correlates significantly with pathological invasive size as a new T factor. Copyright © 2017 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

A collection of annotated and harmonized human breast cancer transcriptome datasets, including immunologic classification [version 2; referees: 2 approved

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Jessica Roelands

2018-02-01

Full Text Available The increased application of high-throughput approaches in translational research has expanded the number of publicly available data repositories. Gathering additional valuable information contained in the datasets represents a crucial opportunity in the biomedical field. To facilitate and stimulate utilization of these datasets, we have recently developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB. In this note, we describe a curated compendium of 13 public datasets on human breast cancer, representing a total of 2142 transcriptome profiles. We classified the samples according to different immune based classification systems and integrated this information into the datasets. Annotated and harmonized datasets were uploaded to GXB. Study samples were categorized in different groups based on their immunologic tumor response profiles, intrinsic molecular subtypes and multiple clinical parameters. Ranked gene lists were generated based on relevant group comparisons. In this data note, we demonstrate the utility of GXB to evaluate the expression of a gene of interest, find differential gene expression between groups and investigate potential associations between variables with a specific focus on immunologic classification in breast cancer. This interactive resource is publicly available online at: http://breastcancer.gxbsidra.org/dm3/geneBrowser/list.

Risk factors and classifications of hilar cholangiocarcinoma.

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Suarez-Munoz, Miguel Angel; Fernandez-Aguilar, Jose Luis; Sanchez-Perez, Belinda; Perez-Daga, Jose Antonio; Garcia-Albiach, Beatriz; Pulido-Roa, Ysabel; Marin-Camero, Naiara; Santoyo-Santoyo, Julio

2013-07-15

Cholangiocarcinoma is the second most common primary malignant tumor of the liver. Perihilar cholangiocarcinoma or Klatskin tumor represents more than 50% of all biliary tract cholangiocarcinomas. A wide range of risk factors have been identified among patients with Perihilar cholangiocarcinoma including advanced age, male gender, primary sclerosing cholangitis, choledochal cysts, cholelithiasis, cholecystitis, parasitic infection (Opisthorchis viverrini and Clonorchis sinensis), inflammatory bowel disease, alcoholic cirrhosis, nonalcoholic cirrhosis, chronic pancreatitis and metabolic syndrome. Various classifications have been used to describe the pathologic and radiologic appearance of cholangiocarcinoma. The three systems most commonly used to evaluate Perihilar cholangiocarcinoma are the Bismuth-Corlette (BC) system, the Memorial Sloan-Kettering Cancer Center and the TNM classification. The BC classification provides preoperative assessment of local spread. The Memorial Sloan-Kettering cancer center proposes a staging system according to three factors related to local tumor extent: the location and extent of bile duct involvement, the presence or absence of portal venous invasion, and the presence or absence of hepatic lobar atrophy. The TNM classification, besides the usual descriptors, tumor, node and metastases, provides additional information concerning the possibility for the residual tumor (R) and the histological grade (G). Recently, in 2011, a new consensus classification for the Perihilar cholangiocarcinoma had been published. The consensus was organised by the European Hepato-Pancreato-Biliary Association which identified the need for a new staging system for this type of tumors. The classification includes information concerning biliary or vascular (portal or arterial) involvement, lymph node status or metastases, but also other essential aspects related to the surgical risk, such as remnant hepatic volume or the possibility of underlying disease.

Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data.

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Glaab, Enrico; Bacardit, Jaume; Garibaldi, Jonathan M; Krasnogor, Natalio

2012-01-01

Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL's classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes.

Finding Combination of Features from Promoter Regions for Ovarian Cancer-related Gene Group Classification

KAUST Repository

Olayan, Rawan S.

2012-01-01

In classification problems, it is always important to use the suitable combination of features that will be employed by classifiers. Generating the right combination of features usually results in good classifiers. In the situation when the problem is not well understood, data items are usually described by many features in the hope that some of these may be the relevant or most relevant ones. In this study, we focus on one such problem related to genes implicated in ovarian cancer (OC). We try to recognize two important OC-related gene groups: oncogenes, which support the development and progression of OC, and oncosuppressors, which oppose such tendencies. For this, we use the properties of promoters of these genes. We identified potential “regulatory features” that characterize OC-related oncogenes and oncosuppressors promoters. In our study, we used 211 oncogenes and 39 oncosuppressors. For these, we identified 538 characteristic sequence motifs from their promoters. Promoters are annotated by these motifs and derived feature vectors used to develop classification models. We made a comparison of a number of classification models in their ability to distinguish oncogenes from oncosuppressors. Based on 10-fold cross-validation, the resultant model was able to separate the two classes with sensitivity of 96% and specificity of 100% with the complete set of features. Moreover, we developed another recognition model where we attempted to distinguish oncogenes and oncosuppressors as one group from other OC-related genes. That model achieved accuracy of 82%. We believe that the results of this study will help in discovering other OC-related oncogenes and oncosuppressors not identified as yet.

Finding Combination of Features from Promoter Regions for Ovarian Cancer-related Gene Group Classification

KAUST Repository

Olayan, Rawan S.

2012-12-01

In classification problems, it is always important to use the suitable combination of features that will be employed by classifiers. Generating the right combination of features usually results in good classifiers. In the situation when the problem is not well understood, data items are usually described by many features in the hope that some of these may be the relevant or most relevant ones. In this study, we focus on one such problem related to genes implicated in ovarian cancer (OC). We try to recognize two important OC-related gene groups: oncogenes, which support the development and progression of OC, and oncosuppressors, which oppose such tendencies. For this, we use the properties of promoters of these genes. We identified potential “regulatory features” that characterize OC-related oncogenes and oncosuppressors promoters. In our study, we used 211 oncogenes and 39 oncosuppressors. For these, we identified 538 characteristic sequence motifs from their promoters. Promoters are annotated by these motifs and derived feature vectors used to develop classification models. We made a comparison of a number of classification models in their ability to distinguish oncogenes from oncosuppressors. Based on 10-fold cross-validation, the resultant model was able to separate the two classes with sensitivity of 96% and specificity of 100% with the complete set of features. Moreover, we developed another recognition model where we attempted to distinguish oncogenes and oncosuppressors as one group from other OC-related genes. That model achieved accuracy of 82%. We believe that the results of this study will help in discovering other OC-related oncogenes and oncosuppressors not identified as yet.

Usefulness of the classification technique of cerebral artery for 2D/3D registration

International Nuclear Information System (INIS)

Takemura, Akihiro; Suzuki, Masayuki; Kikuchi, Yuzo; Okumura, Yusuke; Harauchi, Hajime

2007-01-01

Several papers have proposed 2D/3D registration methods of the cerebral artery using magnetic resonance angiography (MRA) and digital subtraction angiography (DSA). Since differences between vessels in a DSA image and MRA volume data cause registration failure, we previously proposed a method to extract vessels from MRA volume data using a technique based on classification of the cerebral artery. In this paper, we evaluated the usefulness of this classification technique by evaluating the reliability of this 2D/3D registration method. This classification method divides the cerebral artery in MRA volume data into 12 segments. According to the results of the classification, structures corresponding to vessels on a DSA image can then be extracted. We applied the 2D/3D registration with/without classification to 16 pairs of MRA volume data and DSA images obtained from six patients. The registration results were scored into four levels (Excellent, Good, Fair and Poor). The rates of successful registration (>fair) were 37.5% for registration without classification and 81.3% for that with classification. These findings suggested that there was a low percentage of incorrectly extracted voxels and we could facilitate reliable registration. Thus, the classification technique was shown to be useful for feature-based 2D/3D registration. (author)

An Efficient Ensemble Learning Method for Gene Microarray Classification

Directory of Open Access Journals (Sweden)

Alireza Osareh

2013-01-01

Full Text Available The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

Mechanism-based classification and physical therapy management of persons with cancer pain: A prospective case series

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Senthil P Kumar

2013-01-01

Full Text Available Context: Mechanism-based classification (MBC was established with current evidence and physical therapy (PT management methods for both cancer and for noncancer pain. Aims: This study aims to describe the efficacy of MBC-based PT in persons with primary complaints of cancer pain. Settings and Design: A prospective case series of patients who attended the physiotherapy department of a multispecialty university-affiliated teaching hospital. Material and Methods: A total of 24 adults (18 female, 6 male aged 47.5 ± 10.6 years, with primary diagnosis of heterogeneous group of cancer, chief complaints of chronic disabling pain were included in the study on their consent for participation The patients were evaluated and classified on the basis of five predominant mechanisms for pain. Physical therapy interventions were recommended based on mechanisms identified and home program was prescribed with a patient log to ensure compliance. Treatments were given in five consecutive weekly sessions for five weeks each of 30 min duration. Statistical Analysis Used: Pre-post comparisons for pain severity (PS and pain interference (PI subscales of Brief pain inventory-Cancer pain (BPI-CP and, European organization for research and treatment in cancer-quality of life questionnaire (EORTC-QLQ-C30 were done using Wilcoxon signed-rank test at 95% confidence interval using SPSS for Windows version 16.0 (SPSS Inc, Chicago, IL. Results: There were statistically significant ( P < 0.05 reduction in pain severity, pain interference and total BPI-CP scores, and the EORTC-QLQ-C30. Conclusion: MBC-PT was effective for improving BPI-CP and EORTC-QLQ-C30 scores in people with cancer pain.

Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm

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E. Parvinnia

2014-01-01

Full Text Available Electroencephalogram (EEG signals are often used to diagnose diseases such as seizure, alzheimer, and schizophrenia. One main problem with the recorded EEG samples is that they are not equally reliable due to the artifacts at the time of recording. EEG signal classification algorithms should have a mechanism to handle this issue. It seems that using adaptive classifiers can be useful for the biological signals such as EEG. In this paper, a general adaptive method named weighted distance nearest neighbor (WDNN is applied for EEG signal classification to tackle this problem. This classification algorithm assigns a weight to each training sample to control its influence in classifying test samples. The weights of training samples are used to find the nearest neighbor of an input query pattern. To assess the performance of this scheme, EEG signals of thirteen schizophrenic patients and eighteen normal subjects are analyzed for the classification of these two groups. Several features including, fractal dimension, band power and autoregressive (AR model are extracted from EEG signals. The classification results are evaluated using Leave one (subject out cross validation for reliable estimation. The results indicate that combination of WDNN and selected features can significantly outperform the basic nearest-neighbor and the other methods proposed in the past for the classification of these two groups. Therefore, this method can be a complementary tool for specialists to distinguish schizophrenia disorder.

Support vector machine and principal component analysis for microarray data classification

Science.gov (United States)

Astuti, Widi; Adiwijaya

2018-03-01

Cancer is a leading cause of death worldwide although a significant proportion of it can be cured if it is detected early. In recent decades, technology called microarray takes an important role in the diagnosis of cancer. By using data mining technique, microarray data classification can be performed to improve the accuracy of cancer diagnosis compared to traditional techniques. The characteristic of microarray data is small sample but it has huge dimension. Since that, there is a challenge for researcher to provide solutions for microarray data classification with high performance in both accuracy and running time. This research proposed the usage of Principal Component Analysis (PCA) as a dimension reduction method along with Support Vector Method (SVM) optimized by kernel functions as a classifier for microarray data classification. The proposed scheme was applied on seven data sets using 5-fold cross validation and then evaluation and analysis conducted on term of both accuracy and running time. The result showed that the scheme can obtained 100% accuracy for Ovarian and Lung Cancer data when Linear and Cubic kernel functions are used. In term of running time, PCA greatly reduced the running time for every data sets.

Inter- and intra-rater reliability of nasal auscultation in daycare children.

Science.gov (United States)

Santos, Rita; Silva Alexandrino, Ana; Tomé, David; Melo, Cristina; Mesquita Montes, António; Costa, Daniel; Pinto Ferreira, João

2018-02-01

The aim of this study was to assess nasal auscultation's intra- and inter-rater reliability and to analyze ear and respiratory clinical condition according to nasal auscultation. Cross-sectional study performed in 125 children aged up to 3 years old attending daycare centers. Nasal auscultation, tympanometry and Paediatric Respiratory Severity Score (PRSS) were applied to all children. Nasal sounds were classified by an expert panel in order to determine nasal auscultation's intra and inter- rater reliability. The classification of nasal sounds was assessed against tympanometric and PRSS values. Nasal auscultation revealed substantial inter-rater (K=0.75) and intra-rater (K=0.69; K=0.61 and K=0.72) reliability. Children with a "non-obstructed" classification revealed a lower peak pressure (t=-3.599, Pauscultation revealed substantial intra- and inter-rater reliability. Nasal auscultation exhibited important differences according to ear and respiratory clinical conditions. Nasal auscultation in pediatrics seems to be an original topic as well as a simple method that can be used to identify early signs of nasopharyngeal obstruction.

Classification of samples into two or more ordered populations with application to a cancer trial.

Science.gov (United States)

Conde, D; Fernández, M A; Rueda, C; Salvador, B

2012-12-10

In many applications, especially in cancer treatment and diagnosis, investigators are interested in classifying patients into various diagnosis groups on the basis of molecular data such as gene expression or proteomic data. Often, some of the diagnosis groups are known to be related to higher or lower values of some of the predictors. The standard methods of classifying patients into various groups do not take into account the underlying order. This could potentially result in high misclassification rates, especially when the number of groups is larger than two. In this article, we develop classification procedures that exploit the underlying order among the mean values of the predictor variables and the diagnostic groups by using ideas from order-restricted inference. We generalize the existing methodology on discrimination under restrictions and provide empirical evidence to demonstrate that the proposed methodology improves over the existing unrestricted methodology. The proposed methodology is applied to a bladder cancer data set where the researchers are interested in classifying patients into various groups. Copyright © 2012 John Wiley & Sons, Ltd.

Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality

Directory of Open Access Journals (Sweden)

Fang Yang

2017-01-01

Full Text Available Esophageal cancer is one of the fastest rising types of cancers in China. The Kazak nationality is the highest-risk group in Xinjiang. In this work, an effective computer-aided diagnostic system is developed to assist physicians in interpreting digital X-ray image features and improving the quality of diagnosis. The modules of the proposed system include image preprocessing, feature extraction, feature selection, image classification, and performance evaluation. 300 original esophageal X-ray images were resized to a region of interest and then enhanced by the median filter and histogram equalization method. 37 features from textural, frequency, and complexity domains were extracted. Both sequential forward selection and principal component analysis methods were employed to select the discriminative features for classification. Then, support vector machine and K-nearest neighbors were applied to classify the esophageal cancer images with respect to their specific types. The classification performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, precision, and recall, respectively. Experimental results show that the classification performance of the proposed system outperforms the conventional visual inspection approaches in terms of diagnostic quality and processing time. Therefore, the proposed computer-aided diagnostic system is promising for the diagnostics of esophageal cancer.

Equipment Reliability Process in Krsko NPP

International Nuclear Information System (INIS)

Gluhak, M.

2016-01-01

To ensure long-term safe and reliable plant operation, equipment operability and availability must also be ensured by setting a group of processes to be established within the nuclear power plant. Equipment reliability process represents the integration and coordination of important equipment reliability activities into one process, which enables equipment performance and condition monitoring, preventive maintenance activities development, implementation and optimization, continuous improvement of the processes and long term planning. The initiative for introducing systematic approach for equipment reliability assuring came from US nuclear industry guided by INPO (Institute of Nuclear Power Operations) and by participation of several US nuclear utilities. As a result of the initiative, first edition of INPO document AP-913, 'Equipment Reliability Process Description' was issued and it became a basic document for implementation of equipment reliability process for the whole nuclear industry. The scope of equipment reliability process in Krsko NPP consists of following programs: equipment criticality classification, preventive maintenance program, corrective action program, system health reports and long-term investment plan. By implementation, supervision and continuous improvement of those programs, guided by more than thirty years of operating experience, Krsko NPP will continue to be on a track of safe and reliable operation until the end of prolonged life time. (author).

The potential prognostic value of connexin 26 and 46 expression in neoadjuvant-treated breast cancer

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Teleki Ivett

2013-02-01

Full Text Available Abstract Background Several classification systems are available to assess pathological response to neoadjuvant chemotherapy in breast cancer, but reliable biomarkers to predict the efficiency of primary systemic therapy (PST are still missing. Deregulation of gap junction channel forming connexins (Cx has been implicated in carcinogenesis and tumour progression through loss of cell cycle control. In this study we correlated Cx expression and cell proliferation with disease survival and pathological response to neoadjuvant chemotherapy in breast cancers using existing classification systems. Methods The expression of Cx26, Cx32, Cx43, Cx46 and Ki67 was evaluated in 96 breast cancer patients prior to and after neoadjuvant chemotherapy using duplicate cores in tissue microarrays (TMA. Cx plaques of Results In our cohort dominated by hormone receptor (ER/PR positive and HER2 negative cases, only the CPS-EG classification showed prognostic relevance: cases with scores 1–2 had significantly better overall survival (p=0.015 than cases with scores 3–5. Pre-chemotherapy Cx43 expression correlated positively with hormone receptor status both before and after chemotherapy and had a negative correlation with HER2 expression pre-chemotherapy. There was a positive correlation between Cx32 and HER2 expression pre-chemotherapy and between Cx32 and Ki67 expression post-chemotherapy. A negative correlation was found between post-chemotherapy Cx46 and Ki67 expression. Decreased post-chemotherapy Cx26 expression (20% pre- and post-chemotherapy correlated with significantly better survival in the intermediate prognostic subgroups of EWGBSP TR2b (ppre-chemo=0.006; Sataloff TB (ppre-chemo=0.005; ppost-chemo=0.029 and in Miller-Payne G3 (ppre-chemo=0.002; ppost-chemo=0.012 classifications. Pre-chemotherapy, Cx46 expression was the only marker that correlated with overall survival within these subgroups. Conclusion Our results suggest that Cx46 and Cx26 expression

Lenke and King classification systems for adolescent idiopathic scoliosis: interobserver agreement and postoperative results

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Hosseinpour-Feizi H

2011-12-01

Full Text Available Hojjat Hosseinpour-Feizi, Jafar Soleimanpour, Jafar Ganjpour Sales, Ali ArzroumchilarDepartment of Orthopedics, Shohada Hospital, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, IranPurpose: The aim of this study was to investigate the interobserver agreement of the Lenke and King classifications for adolescent idiopathic scoliosis, and to compare the results of surgery performed based on classification of the scoliosis according to each of these classification systems.Methods: The study was conducted in Shohada Hospital in Tabriz, Iran, between 2009 and 2010. First, a reliability assessment was undertaken to assess interobserver agreement of the Lenke and King classifications for adolescent idiopathic scoliosis. Second, postoperative efficacy and safety of surgery performed based on the Lenke and King classifications were compared. Kappa coefficients of agreement were calculated to assess the agreement. Outcomes were compared using bivariate tests and repeated measures analysis of variance.Results: A low to moderate interobserver agreement was observed for the King classification; the Lenke classification yielded mostly high agreement coefficients. The outcome of surgery was not found to be substantially different between the two systems.Conclusion: Based on the results, the Lenke classification method seems advantageous. This takes into consideration the Lenke classification’s priority in providing details of curvatures in different anatomical surfaces to explain precise intensity of scoliosis, that it has higher interobserver agreement scores, and also that it leads to noninferior postoperative results compared with the King classification method.Keywords: test reliability, scoliosis classification, postoperative efficacy, adolescents

Reliability of Patient-Led Screening with the Malnutrition Screening Tool: Agreement between Patient and Health Care Professional Scores in the Cancer Care Ambulatory Setting.

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Di Bella, Alexandra; Blake, Claire; Young, Adrienne; Pelecanos, Anita; Brown, Teresa

2018-02-01

The prevalence of malnutrition in patients with cancer is reported as high as 60% to 80%, and malnutrition is associated with lower survival, reduced response to treatment, and poorer functional status. The Malnutrition Screening Tool (MST) is a validated tool when administered by health care professionals; however, it has not been evaluated for patient-led screening. This study aims to assess the reliability of patient-led MST screening through assessment of inter-rater reliability between patient-led and dietitian-researcher-led screening and intra-rater reliability between an initial and a repeat patient screening. This cross-sectional study included 208 adults attending ambulatory cancer care services in a metropolitan teaching hospital in Queensland, Australia, in October 2016 (n=160 inter-rater reliability; n=48 intra-rater reliability measured in a separate sample). Primary outcome measures were MST risk categories (MST 0-1: not at risk, MST ≥2: at risk) as determined by screening completed by patients and a dietitian-researcher, patient test-retest screening, and patient acceptability. Percent and chance-corrected agreement (Cohen's kappa coefficient, κ) were used to determine agreement between patient-MST and dietitian-MST (inter-rater reliability) and MST completed by patient on admission to unit (patient-MSTA) and MST completed by patient 1 to 3 hours after completion of initial MST (patient-MSTB) (intra-rater reliability). High inter-rater reliability and intra-rater reliability were observed. Agreement between patient-MST and dietitian-MST was 96%, with "almost perfect" chance-adjusted agreement (κ=0.92, 95% CI 0.84 to 0.97). Agreement between repeated patient-MSTA and patient-MSTB was 94%, with "almost perfect" chance-adjusted agreement (κ=0.88, 95% CI 0.71 to 1.00). Based on dietitian-MST, 33% (n=53) of patients were identified as being at risk for malnutrition, and 40% of these reported not seeing a dietitian. Of 156 patients who provided

Validating the Danish adaptation of the World Health Organization's International Classification for Patient Safety classification of patient safety incident types

DEFF Research Database (Denmark)

Mikkelsen, Kim Lyngby; Thommesen, Jacob; Andersen, Henning Boje

2013-01-01

Objectives Validation of a Danish patient safety incident classification adapted from the World Health Organizaton's International Classification for Patient Safety (ICPS-WHO). Design Thirty-three hospital safety management experts classified 58 safety incident cases selected to represent all types.......513 (range: 0.193–0.804). Kappa and ICC showed high correlation (r = 0.99). An inverse correlation was found between the prevalence of type and inter-rater reliability. Results are discussed according to four factors known to determine the inter-rater agreement: skill and motivation of raters; clarity...

Reliability of Smartphone-Based Instant Messaging Application for Diagnosis, Classification, and Decision-making in Pediatric Orthopedic Trauma.

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Stahl, Ido; Katsman, Alexander; Zaidman, Michael; Keshet, Doron; Sigal, Amit; Eidelman, Mark

2017-07-11

Smartphones have the ability to capture and send images, and their use has become common in the emergency setting for transmitting radiographic images with the intent to consult an off-site specialist. Our objective was to evaluate the reliability of smartphone-based instant messaging applications for the evaluation of various pediatric limb traumas, as compared with the standard method of viewing images of a workstation-based picture archiving and communication system (PACS). X-ray images of 73 representative cases of pediatric limb trauma were captured and transmitted to 5 pediatric orthopedic surgeons by the Whatsapp instant messaging application on an iPhone 6 smartphone. Evaluators were asked to diagnose, classify, and determine the course of treatment for each case over their personal smartphones. Following a 4-week interval, revaluation was conducted using the PACS. Intraobserver agreement was calculated for overall agreement and per fracture site. The overall results indicate "near perfect agreement" between interpretations of the radiographs on smartphones compared with computer-based PACS, with κ of 0.84, 0.82, and 0.89 for diagnosis, classification, and treatment planning, respectively. Looking at the results per fracture site, we also found substantial to near perfect agreement. Smartphone-based instant messaging applications are reliable for evaluation of a wide range of pediatric limb fractures. This method of obtaining an expert opinion from the off-site specialist is immediately accessible and inexpensive, making smartphones a powerful tool for doctors in the emergency department, primary care clinics, or remote medical centers, enabling timely and appropriate treatment for the injured child. This method is not a substitution for evaluation of the images in the standard method over computer-based PACS, which should be performed before final decision-making.

Classification-based comparison of pre-processing methods for interpretation of mass spectrometry generated clinical datasets

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Hoefsloot Huub CJ

2009-05-01

Full Text Available Abstract Background Mass spectrometry is increasingly being used to discover proteins or protein profiles associated with disease. Experimental design of mass-spectrometry studies has come under close scrutiny and the importance of strict protocols for sample collection is now understood. However, the question of how best to process the large quantities of data generated is still unanswered. Main challenges for the analysis are the choice of proper pre-processing and classification methods. While these two issues have been investigated in isolation, we propose to use the classification of patient samples as a clinically relevant benchmark for the evaluation of pre-processing methods. Results Two in-house generated clinical SELDI-TOF MS datasets are used in this study as an example of high throughput mass-spectrometry data. We perform a systematic comparison of two commonly used pre-processing methods as implemented in Ciphergen ProteinChip Software and in the Cromwell package. With respect to reproducibility, Ciphergen and Cromwell pre-processing are largely comparable. We find that the overlap between peaks detected by either Ciphergen ProteinChip Software or Cromwell is large. This is especially the case for the more stringent peak detection settings. Moreover, similarity of the estimated intensities between matched peaks is high. We evaluate the pre-processing methods using five different classification methods. Classification is done in a double cross-validation protocol using repeated random sampling to obtain an unbiased estimate of classification accuracy. No pre-processing method significantly outperforms the other for all peak detection settings evaluated. Conclusion We use classification of patient samples as a clinically relevant benchmark for the evaluation of pre-processing methods. Both pre-processing methods lead to similar classification results on an ovarian cancer and a Gaucher disease dataset. However, the settings for pre

Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data.

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Enrico Glaab

Full Text Available Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL's classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes.

AAPT Diagnostic Criteria for Chronic Cancer Pain Conditions

OpenAIRE

Paice, Judith A.; Mulvey, Matt; Bennett, Michael; Dougherty, Patrick M.; Farrar, John T.; Mantyh, Patrick W.; Miaskowski, Christine; Schmidt, Brian; Smith, Thomas J.

2016-01-01

Chronic cancer pain is a serious complication of malignancy or its treatment. Currently, no comprehensive, universally accepted cancer pain classification system exists. Clarity in classification of common cancer pain syndromes would improve clinical assessment and management. Moreover, an evidence-based taxonomy would enhance cancer pain research efforts by providing consistent diagnostic criteria, ensuring comparability across clinical trials. As part of a collaborative effort between the A...

Support vector machine classification and validation of cancer tissue samples using microarray expression data.

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Furey, T S; Cristianini, N; Duffy, N; Bednarski, D W; Schummer, M; Haussler, D

2000-10-01

DNA microarray experiments generating thousands of gene expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. We have developed a new method to analyse this kind of data using support vector machines (SVMs). This analysis consists of both classification of the tissue samples, and an exploration of the data for mis-labeled or questionable tissue results. We demonstrate the method in detail on samples consisting of ovarian cancer tissues, normal ovarian tissues, and other normal tissues. The dataset consists of expression experiment results for 97,802 cDNAs for each tissue. As a result of computational analysis, a tissue sample is discovered and confirmed to be wrongly labeled. Upon correction of this mistake and the removal of an outlier, perfect classification of tissues is achieved, but not with high confidence. We identify and analyse a subset of genes from the ovarian dataset whose expression is highly differentiated between the types of tissues. To show robustness of the SVM method, two previously published datasets from other types of tissues or cells are analysed. The results are comparable to those previously obtained. We show that other machine learning methods also perform comparably to the SVM on many of those datasets. The SVM software is available at http://www.cs. columbia.edu/ approximately bgrundy/svm.

Improved RMR Rock Mass Classification Using Artificial Intelligence Algorithms

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Gholami, Raoof; Rasouli, Vamegh; Alimoradi, Andisheh

2013-09-01

Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable regions. Many correlations have been proposed to relate measured quantities such as wave velocity to rock mass classification systems to limit the associated time and cost of conducting the sampling and mechanical tests conventionally used to calculate RMR values. However, these empirical correlations have been found to be unreliable, as they usually overestimate or underestimate the RMR value. The aim of this paper is to compare the results of RMR classification obtained from the use of empirical correlations versus machine-learning methodologies based on artificial intelligence algorithms. The proposed methods were verified based on two case studies located in northern Iran. Relevance vector regression (RVR) and support vector regression (SVR), as two robust machine-learning methodologies, were used to predict the RMR for tunnel host rocks. RMR values already obtained by sampling and site investigation at one tunnel were taken into account as the output of the artificial networks during training and testing phases. The results reveal that use of empirical correlations overestimates the predicted RMR values. RVR and SVR, however, showed more reliable results, and are therefore suggested for use in RMR classification for design purposes of rock structures.

A guide to reliability data collection, validation and storage

International Nuclear Information System (INIS)

Stevens, B.

1986-01-01

The EuReDatA Working Group produced a basic document that addressed many of the problems associated with the design of a suitable data collection scheme to achieve pre-defined objectives. The book that resulted from this work describes the need for reliability data, data sources and collection procedures, component description and classification, form design, data management, updating and checking procedures, the estimation of failure rates, availability and utilisation factors, and uncertainties in reliability parameters. (DG)

Reliable prediction and determination of Norwegian lamb carcass composition and value

International Nuclear Information System (INIS)

Kongsro, Jørgen

2008-01-01

The main objective of this work was to study prediction and determination of Norwegian lamb carcass composition with different techniques spanning from subjective appraisal to computer-intensive methods. There is an increasing demand, both from farmers and processors of meats, for a more objective and reliable system for prediction of muscle (lean meat), fat, bone and value of a lamb carcass. When introducing new technologies for determination of lamb carcass composition, the reference method used for calibration must be precise and reliable. The precision and reliability of the current dissection reference for lamb carcass classification and grading has never been quantified. A poor reference method will not benefit even the most optimal system for prediction and determination of lamb carcasses. To help achieve reliable systems, the uncertainty or errors in the reference method and measuring systems needs to be quantified. Using proper calibration methods for the measuring systems, the uncertainty and modeling power can be determined for lamb carcasses. The results of the work presented in this thesis show that the current classification system using subjective appraisal (EUROP) is reliable; however the accuracy with respect to carcass composition, especially for lean meat or muscle and carcass value, is poor. The reference method used for determining lamb carcass composition with respect to lamb carcass classification and grading is precise and reliable for carcass composition. For the composition and yield of sub-primal cuts, the reliability varied, and was especially poor for the breast cut. Further attention is needed for jointing and cutting of sub-primals to achieve even higher precision and reliability of the reference method. As an alternative to butcher or manual dissection, Computer Tomography (CT) showed promising results with respect to prediction of lamb carcass composition. This method is nicknamed “virtual dissection”. By utilizing the

Comparison of Reliability and Validity of the Breast Cancer depression anxiety stress scales (DASS- 21) with the Beck Depression Inventory-(BDI-II) and Hospital Anxiety and Depression Scale (HADS)

OpenAIRE

Bener A; Alsulaiman R; Doodson LG; El Ayoubi HR

2016-01-01

Background: No study has been conducted to determine the reliability and validity of the Depression, Anxiety and Stress Scale (DASS-21), Hospital Anxiety and Depression [HADS] and Beck Depression Inventory (BDI-II) among the Arab Breast Cancer population. Aim: The aim of this study was to compare the reliability and validity of the Depression, Anxiety, and Stress scale (DASS-21), the Beck Depression Inventory-(BDI-II) and Hospital Anxiety and Depression Scale (HADS) among Breast Cancer women ...

Prediction of lung cancer patient survival via supervised machine learning classification techniques.

Science.gov (United States)

Lynch, Chip M; Abdollahi, Behnaz; Fuqua, Joshua D; de Carlo, Alexandra R; Bartholomai, James A; Balgemann, Rayeanne N; van Berkel, Victor H; Frieboes, Hermann B

2017-12-01

Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble. Key data attributes in applying these methods include tumor grade, tumor size, gender, age, stage, and number of primaries, with the goal to enable comparison of predictive power between the various methods The prediction is treated like a continuous target, rather than a classification into categories, as a first step towards improving survival prediction. The results show that the predicted values agree with actual values for low to moderate survival times, which constitute the majority of the data. The best performing technique was the custom ensemble with a Root Mean Square Error (RMSE) value of 15.05. The most influential model within the custom ensemble was GBM, while Decision Trees may be inapplicable as it had too few discrete outputs. The results further show that among the five individual models generated, the most accurate was GBM with an RMSE value of 15.32. Although SVM underperformed with an RMSE value of 15.82, statistical analysis singles the SVM as the only model that generated a distinctive output. The results of the models are consistent with a classical Cox proportional hazards model used as a reference technique. We conclude that application of these supervised learning techniques to lung cancer data in the SEER database may be of use to estimate patient survival time

An iterated Laplacian based semi-supervised dimensionality reduction for classification of breast cancer on ultrasound images.

Science.gov (United States)

Liu, Xiao; Shi, Jun; Zhou, Shichong; Lu, Minhua

2014-01-01

The dimensionality reduction is an important step in ultrasound image based computer-aided diagnosis (CAD) for breast cancer. A newly proposed l2,1 regularized correntropy algorithm for robust feature selection (CRFS) has achieved good performance for noise corrupted data. Therefore, it has the potential to reduce the dimensions of ultrasound image features. However, in clinical practice, the collection of labeled instances is usually expensive and time costing, while it is relatively easy to acquire the unlabeled or undetermined instances. Therefore, the semi-supervised learning is very suitable for clinical CAD. The iterated Laplacian regularization (Iter-LR) is a new regularization method, which has been proved to outperform the traditional graph Laplacian regularization in semi-supervised classification and ranking. In this study, to augment the classification accuracy of the breast ultrasound CAD based on texture feature, we propose an Iter-LR-based semi-supervised CRFS (Iter-LR-CRFS) algorithm, and then apply it to reduce the feature dimensions of ultrasound images for breast CAD. We compared the Iter-LR-CRFS with LR-CRFS, original supervised CRFS, and principal component analysis. The experimental results indicate that the proposed Iter-LR-CRFS significantly outperforms all other algorithms.

Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model

Science.gov (United States)

Ma, Ling; Lu, Guolan; Wang, Dongsheng; Wang, Xu; Chen, Zhuo Georgia; Muller, Susan; Chen, Amy; Fei, Baowei

2017-03-01

Hyperspectral imaging (HSI) is an emerging imaging modality that can provide a noninvasive tool for cancer detection and image-guided surgery. HSI acquires high-resolution images at hundreds of spectral bands, providing big data to differentiating different types of tissue. We proposed a deep learning based method for the detection of head and neck cancer with hyperspectral images. Since the deep learning algorithm can learn the feature hierarchically, the learned features are more discriminative and concise than the handcrafted features. In this study, we adopt convolutional neural networks (CNN) to learn the deep feature of pixels for classifying each pixel into tumor or normal tissue. We evaluated our proposed classification method on the dataset containing hyperspectral images from 12 tumor-bearing mice. Experimental results show that our method achieved an average accuracy of 91.36%. The preliminary study demonstrated that our deep learning method can be applied to hyperspectral images for detecting head and neck tumors in animal models.

A Quantum Hybrid PSO Combined with Fuzzy k-NN Approach to Feature Selection and Cell Classification in Cervical Cancer Detection

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Abdullah M. Iliyasu

2017-12-01

Full Text Available A quantum hybrid (QH intelligent approach that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO method with the intuitionistic rationality of traditional fuzzy k-nearest neighbours (Fuzzy k-NN algorithm (known simply as the Q-Fuzzy approach is proposed for efficient feature selection and classification of cells in cervical smeared (CS images. From an initial multitude of 17 features describing the geometry, colour, and texture of the CS images, the QPSO stage of our proposed technique is used to select the best subset features (i.e., global best particles that represent a pruned down collection of seven features. Using a dataset of almost 1000 images, performance evaluation of our proposed Q-Fuzzy approach assesses the impact of our feature selection on classification accuracy by way of three experimental scenarios that are compared alongside two other approaches: the All-features (i.e., classification without prior feature selection and another hybrid technique combining the standard PSO algorithm with the Fuzzy k-NN technique (P-Fuzzy approach. In the first and second scenarios, we further divided the assessment criteria in terms of classification accuracy based on the choice of best features and those in terms of the different categories of the cervical cells. In the third scenario, we introduced new QH hybrid techniques, i.e., QPSO combined with other supervised learning methods, and compared the classification accuracy alongside our proposed Q-Fuzzy approach. Furthermore, we employed statistical approaches to establish qualitative agreement with regards to the feature selection in the experimental scenarios 1 and 3. The synergy between the QPSO and Fuzzy k-NN in the proposed Q-Fuzzy approach improves classification accuracy as manifest in the reduction in number cell features, which is crucial for effective cervical cancer detection and diagnosis.

Pathogenesis of Gastric Cancer: Genetics and Molecular Classification.

Science.gov (United States)

Figueiredo, Ceu; Camargo, M C; Leite, Marina; Fuentes-Pananá, Ezequiel M; Rabkin, Charles S; Machado, José C

Gastric cancer is the fifth most incident and the third most common cause of cancer-related death in the world. Infection with Helicobacter pylori is the major risk factor for this disease. Gastric cancer is the final outcome of a cascade of events that takes decades to occur and results from the accumulation of multiple genetic and epigenetic alterations. These changes are crucial for tumor cells to expedite and sustain the array of pathways involved in the cancer development, such as cell cycle, DNA repair, metabolism, cell-to-cell and cell-to-matrix interactions, apoptosis, angiogenesis, and immune surveillance. Comprehensive molecular analyses of gastric cancer have disclosed the complex heterogeneity of this disease. In particular, these analyses have confirmed that Epstein-Barr virus (EBV)-positive gastric cancer is a distinct entity. The identification of gastric cancer subtypes characterized by recognizable molecular profiles may pave the way for a more personalized clinical management and to the identification of novel therapeutic targets and biomarkers for screening, prognosis, prediction of response to treatment, and monitoring of gastric cancer progression.

The eighth TNM classification system for lung cancer: A consideration based on the degree of pleural invasion and involved neighboring structures.

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Sakakura, Noriaki; Mizuno, Tetsuya; Kuroda, Hiroaki; Arimura, Takaaki; Yatabe, Yasushi; Yoshimura, Kenichi; Sakao, Yukinori

2018-04-01

The eighth tumor-node-metastasis (TNM) classification system for lung cancer has been used since January 2017 and must be applied to an individual institution's database. We analyzed pathological stage data of 2756 patients who underwent resection of non-small-cell lung cancer, particularly in terms of the degree of visceral pleural invasion and involved neighboring structures. Few patients had stage IIA disease (103, 4%); stratification between stages IB and IIA was insufficient (p = 0.129). When T2a tumors were divided into PL1 and PL2 subgroups based on the degree of pleural invasion, there was a significant prognostic difference between the subgroups (p consideration. Copyright © 2018 Elsevier B.V. All rights reserved.

KINEMATIC CLASSIFICATIONS OF LOCAL INTERACTING GALAXIES: IMPLICATIONS FOR THE MERGER/DISK CLASSIFICATIONS AT HIGH-z

International Nuclear Information System (INIS)

Hung, Chao-Ling; Larson, Kirsten L.; Sanders, D. B.; Rich, Jeffrey A.; Yuan, Tiantian; Kewley, Lisa J.; Casey, Caitlin M.; Smith, Howard A.; Hayward, Christopher C.

2015-01-01

The classification of galaxy mergers and isolated disks is key for understanding the relative importance of galaxy interactions and secular evolution during the assembly of galaxies. Galaxy kinematics as traced by emission lines have been used to suggest the existence of a significant population of high-z star-forming galaxies consistent with isolated rotating disks. However, recent studies have cautioned that post-coalescence mergers may also display disk-like kinematics. To further investigate the robustness of merger/disk classifications based on kinematic properties, we carry out a systematic classification of 24 local (U)LIRGs spanning a range of morphologies: from isolated spiral galaxies, ongoing interacting systems, to fully merged remnants. We artificially redshift the Wide Field Spectrograph observations of these local (U)LIRGs to z = 1.5 to make a realistic comparison with observations at high-z, and also to ensure that all galaxies have the same spatial sampling of ∼900 pc. Using both kinemetry-based and visual classifications, we find that the reliability of kinematic classification shows a strong trend with the interaction stage of galaxies. Mergers with two nuclei and tidal tails have the most distinct kinematics compared to isolated disks, whereas a significant population of the interacting disks and merger remnants are indistinguishable from isolated disks. The high fraction of mergers displaying disk-like kinematics reflects the complexity of the dynamics during galaxy interactions. Additional merger indicators such as morphological properties traced by stars or molecular gas are required to further constrain the merger/disk classifications at high-z

Biobank classification in an Australian setting.

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Rush, Amanda; Christiansen, Jeffrey H; Farrell, Jake P; Goode, Susan M; Scott, Rodney J; Spring, Kevin J; Byrne, Jennifer A

2015-06-01

In 2011, Watson and Barnes proposed a schema for classifying biobanks into 3 groups (mono-, oligo-, and poly-user), primarily based upon biospecimen access policies. We used results from a recent comprehensive survey of cancer biobanks in New South Wales, Australia to assess the applicability of this biobank classification schema in an Australian setting. Cancer biobanks were identified using publically available data, and by consulting with research managers. A comprehensive survey was developed and administered through a face-to-face setting. Data were analyzed using Microsoft Excel™ 2010 and IBM SPSS Statistics™ version 21.0. The cancer biobank cohort (n=23) represented 5 mono-user biobanks, 7 oligo-user biobanks, and 11 poly-user biobanks, and was analyzed as two groups (mono-/oligo- versus poly-user biobanks). Poly-user biobanks employed significantly more full-time equivalent staff, and were significantly more likely to have a website, share staff between biobanks, access governance support, utilize quality control measures, be aware of biobanking best practice documents, and offer staff training. Mono-/oligo-user biobanks were significantly more likely to seek advice from other biobanks. Our results further delineate a biobank classification system that is primarily based on access policy, and demonstrate its relevance in an Australian setting.

Carcinoma de mama: novos conceitos na classificação Breast cancer: new concepts in classification

Directory of Open Access Journals (Sweden)

Daniella Serafin Couto Vieira

2008-01-01

and the basis for and improved breast cancer molecular taxonomy. Another important implication is that genetic profiling may lead to the identification of new target for therapy and better predictive markers are needed to guide difficult treatment decisions. Additionally, the current pathology classification system is suboptimal, since patients with identical tumor types and stage of disease present different responses to therapy and different overall outcomes. Basal breast tumor represents one of the most intriguing subtypes and is frequently associated with poor prognosis and absence of putative therapeutic targets. Then, the purpose of this review was to resume the most recent knowledge about the breast carcinoma classification and characterization.

Machine learning models in breast cancer survival prediction.

Science.gov (United States)

Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

2016-01-01

Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of

Intra-rater and inter-rater reliability of a medical record abstraction study on transition of care after childhood cancer.

Directory of Open Access Journals (Sweden)

Micòl E Gianinazzi

Full Text Available The abstraction of data from medical records is a widespread practice in epidemiological research. However, studies using this means of data collection rarely report reliability. Within the Transition after Childhood Cancer Study (TaCC which is based on a medical record abstraction, we conducted a second independent abstraction of data with the aim to assess a intra-rater reliability of one rater at two time points; b the possible learning effects between these two time points compared to a gold-standard; and c inter-rater reliability.Within the TaCC study we conducted a systematic medical record abstraction in the 9 Swiss clinics with pediatric oncology wards. In a second phase we selected a subsample of medical records in 3 clinics to conduct a second independent abstraction. We then assessed intra-rater reliability at two time points, the learning effect over time (comparing each rater at two time-points with a gold-standard and the inter-rater reliability of a selected number of variables. We calculated percentage agreement and Cohen's kappa.For the assessment of the intra-rater reliability we included 154 records (80 for rater 1; 74 for rater 2. For the inter-rater reliability we could include 70 records. Intra-rater reliability was substantial to excellent (Cohen's kappa 0-6-0.8 with an observed percentage agreement of 75%-95%. In all variables learning effects were observed. Inter-rater reliability was substantial to excellent (Cohen's kappa 0.70-0.83 with high agreement ranging from 86% to 100%.Our study showed that data abstracted from medical records are reliable. Investigating intra-rater and inter-rater reliability can give confidence to draw conclusions from the abstracted data and increase data quality by minimizing systematic errors.

Iris Image Classification Based on Hierarchical Visual Codebook.

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Zhenan Sun; Hui Zhang; Tieniu Tan; Jianyu Wang

2014-06-01

Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection.

[The importance of classifications in psychiatry].

Science.gov (United States)

Lempérière, T

1995-12-01

The classifications currently used in psychiatry have different aims: to facilitate communication between researchers and clinicians at national and international levels through the use of a common language, or at least a clearly and precisely defined nomenclature; to provide a nosographical reference system which can be used in practice (diagnosis, prognosis, treatment); to optimize research by ensuring that sample cases are as homogeneous as possible; to facilitate statistical records for public health institutions. A classification is of practical interest only if it is reliable, valid and acceptable to all potential users. In recent decades, there has been a considerable systematic and coordinated effort to improve the methodological approach to classification and categorization in the field of psychiatry, including attempts to create operational definitions, field trials of inter-assessor reliability, attempts to validate the selected nosological categories by analysis of correlation between progression, treatment response, family history and additional examinations. The introduction of glossaries, and particularly of diagnostic criteria, marked a decisive step in this new approach. The key problem remains that of the validity of diagnostic criteria. Ideally, these should be based on demonstrable etiologic or pathogenic data, but such information is rarely available in psychiatry. Current classifications rely on the use of extremely diverse elements in differing degrees: descriptive criteria, evolutive criteria, etiopathogenic criteria, psychopathogenic criteria, etc. Certain syndrome-based classifications such as DSM III and its successors aim to be atheoretical and pragmatic. Others, such as ICD-10, while more eclectic than the different versions of DSM, follow suit by abandoning the terms "disease" and "illness" in favor of the more consensual "disorder". The legitimacy of classifications in the field of psychiatry has been fiercely contested, being

The six-item Clock Drawing Test – reliability and validity in mild Alzheimer’s disease

DEFF Research Database (Denmark)

Jørgensen, Kasper; Kristensen, Maria K; Waldemar, Gunhild

2015-01-01

This study presents a reliable, short and practical version of the Clock Drawing Test (CDT) for clinical use and examines its diagnostic accuracy in mild Alzheimer's disease versus elderly nonpatients. Clock drawings from 231 participants were scored independently by four clinical neuropsychologi......This study presents a reliable, short and practical version of the Clock Drawing Test (CDT) for clinical use and examines its diagnostic accuracy in mild Alzheimer's disease versus elderly nonpatients. Clock drawings from 231 participants were scored independently by four clinical...... neuropsychologists blind to diagnostic classification. The interrater agreement of individual scoring criteria was analyzed and items with poor or moderate reliability were excluded. The classification accuracy of the resulting scoring system - the six-item CDT - was examined. We explored the effect of further...

Screening for Psychosocial Risk in Dutch Families of a Child With Cancer: Reliability, Validity, and Usability of the Psychosocial Assessment Tool

NARCIS (Netherlands)

Sint Nicolaas, Simone M.; Schepers, Sasja A.; Hoogerbrugge, Peter M.; Caron, Huib N.; Kaspers, Gertjan J. L.; van den Heuvel-Eibrink, Marry M.; Grootenhuis, Martha A.; Verhaak, Chris M.

2016-01-01

The Psychosocial Assessment Tool (PAT) was developed to screen for psychosocial risk in families of a child diagnosed with cancer. The current study is the first describing the cross-cultural adaptation, reliability, validity, and usability of the PAT in an European country (Dutch translation).   A

Rating scales for dystonia in cerebral palsy: reliability and validity.

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Monbaliu, E; Ortibus, E; Roelens, F; Desloovere, K; Deklerck, J; Prinzie, P; de Cock, P; Feys, H

2010-06-01

This study investigated the reliability and validity of the Barry-Albright Dystonia Scale (BADS), the Burke-Fahn-Marsden Movement Scale (BFMMS), and the Unified Dystonia Rating Scale (UDRS) in patients with bilateral dystonic cerebral palsy (CP). Three raters independently scored videotapes of 10 patients (five males, five females; mean age 13 y 3 mo, SD 5 y 2 mo, range 5-22 y). One patient each was classified at levels I-IV in the Gross Motor Function Classification System and six patients were classified at level V. Reliability was measured by (1) intraclass correlation coefficient (ICC) for interrater reliability, (2) standard error of measurement (SEM) and smallest detectable difference (SDD), and (3) Cronbach's alpha for internal consistency. Validity was assessed by Pearson's correlations among the three scales used and by content analysis. Moderate to good interrater reliability was found for total scores of the three scales (ICC: BADS=0.87; BFMMS=0.86; UDRS=0.79). However, many subitems showed low reliability, in particular for the UDRS. SEM and SDD were respectively 6.36% and 17.72% for the BADS, 9.88% and 27.39% for the BFMMS, and 8.89% and 24.63% for the UDRS. High internal consistency was found. Pearson's correlations were high. Content validity showed insufficient accordance with the new CP definition and classification. Our results support the internal consistency and concurrent validity of the scales; however, taking into consideration the limitations in reliability, including the large SDD values and the content validity, further research on methods of assessment of dystonia is warranted.

Low Dimensional Representation of Fisher Vectors for Microscopy Image Classification.

Science.gov (United States)

Song, Yang; Li, Qing; Huang, Heng; Feng, Dagan; Chen, Mei; Cai, Weidong

2017-08-01

Microscopy image classification is important in various biomedical applications, such as cancer subtype identification, and protein localization for high content screening. To achieve automated and effective microscopy image classification, the representative and discriminative capability of image feature descriptors is essential. To this end, in this paper, we propose a new feature representation algorithm to facilitate automated microscopy image classification. In particular, we incorporate Fisher vector (FV) encoding with multiple types of local features that are handcrafted or learned, and we design a separation-guided dimension reduction method to reduce the descriptor dimension while increasing its discriminative capability. Our method is evaluated on four publicly available microscopy image data sets of different imaging types and applications, including the UCSB breast cancer data set, MICCAI 2015 CBTC challenge data set, and IICBU malignant lymphoma, and RNAi data sets. Our experimental results demonstrate the advantage of the proposed low-dimensional FV representation, showing consistent performance improvement over the existing state of the art and the commonly used dimension reduction techniques.

Interobserver reliability of coronoid fracture classification: two-dimensional versus three-dimensional computed tomography

NARCIS (Netherlands)

Lindenhovius, Anneluuk; Karanicolas, Paul Jack; Bhandari, Mohit; van Dijk, Niek; Ring, David; Allan, Christopher; Anglen, Jeffrey; Axelrod, Terry; Baratz, Mark; Beingessner, Daphne; Brink, Peter; Cassidy, Charles; Coles, Chad; Conflitti, Joe; Crist, Brett; Della Rocca, Gregory; Dijkstra, Sander; Elmans, L. H. G. J.; Feibel, Roger; Flores, Luis; Frihagen, Frede; Gosens, Taco; Goslings, J. C.; Greenberg, Jeffrey; Grosso, Elena; Harness, Neil; van der Heide, Huub; Jeray, Kyle; Kalainov, David; van Kampen, Albert; Kawamura, Sumito; Kloen, Peter; McKee, Michael; Nork, Sean; Page, Richard; Pesantez, Rodrigo; Peters, Anil; Poolman, Rudolf; Prayson, Michael; Richardson, Martin; Seiler, John; Swiontkowski, Marc; Thomas, George; Trumble, Tom; van Vugt, Arie; Wright, Thomas; Zalavras, Charalampos; Zura, Robert

2009-01-01

This study tests the hypothesis that 3-dimensional computed tomography (CT) reconstructions improve interobserver agreement on classification and treatment of coronoid fractures compared with 2-dimensional CT. A total of 29 orthopedic surgeons evaluated 10 coronoid fractures on 2 occasions (first

Development and content validity testing of a comprehensive classification of diagnoses for pediatric nurse practitioners.

Science.gov (United States)

Burns, C

1991-01-01

Pediatric nurse practitioners (PNPs) need an integrated, comprehensive classification that includes nursing, disease, and developmental diagnoses to effectively describe their practice. No such classification exists. Further, methodologic studies to help evaluate the content validity of any nursing taxonomy are unavailable. A conceptual framework was derived. Then 178 diagnoses from the North American Nursing Diagnosis Association (NANDA) 1986 list, selected diagnoses from the International Classification of Diseases, the Diagnostic and Statistical Manual, Third Revision, and others were selected. This framework identified and listed, with definitions, three domains of diagnoses: Developmental Problems, Diseases, and Daily Living Problems. The diagnoses were ranked using a 4-point scale (4 = highly related to 1 = not related) and were placed into the three domains. The rating scale was assigned by a panel of eight expert pediatric nurses. Diagnoses that were assigned to the Daily Living Problems domain were then sorted into the 11 Functional Health patterns described by Gordon (1987). Reliability was measured using proportions of agreement and Kappas. Content validity of the groups created was measured using indices of content validity and average congruency percentages. The experts used a new method to sort the diagnoses in a new way that decreased overlaps among the domains. The Developmental and Disease domains were judged reliable and valid. The Daily Living domain of nursing diagnoses showed marginally acceptable validity with acceptable reliability. Six Functional Health Patterns were judged reliable and valid, mixed results were determined for four categories, and the Coping/Stress Tolerance category was judged reliable but not valid using either test. There were considerable differences between the panel's, Gordon's (1987), and NANDA's clustering of NANDA diagnoses. This study defines the diagnostic practice of nurses from a holistic, patient

Classification of Breast Cancer Resistant Protein (BCRP) Inhibitors and Non-Inhibitors Using Machine Learning Approaches.

Science.gov (United States)

Belekar, Vilas; Lingineni, Karthik; Garg, Prabha

2015-01-01

The breast cancer resistant protein (BCRP) is an important transporter and its inhibitors play an important role in cancer treatment by improving the oral bioavailability as well as blood brain barrier (BBB) permeability of anticancer drugs. In this work, a computational model was developed to predict the compounds as BCRP inhibitors or non-inhibitors. Various machine learning approaches like, support vector machine (SVM), k-nearest neighbor (k-NN) and artificial neural network (ANN) were used to develop the models. The Matthews correlation coefficients (MCC) of developed models using ANN, k-NN and SVM are 0.67, 0.71 and 0.77, and prediction accuracies are 85.2%, 88.3% and 90.8% respectively. The developed models were tested with a test set of 99 compounds and further validated with external set of 98 compounds. Distribution plot analysis and various machine learning models were also developed based on druglikeness descriptors. Applicability domain is used to check the prediction reliability of the new molecules.

Is sequential cranial ultrasound reliable for detection of white matter injury in very preterm infants?

International Nuclear Information System (INIS)

Leijser, Lara M.; Steggerda, Sylke J.; Walther, Frans J.; Wezel-Meijler, Gerda van; Bruine, Francisca T. de; Grond, Jeroen van der

2010-01-01

Cranial ultrasound (cUS) may not be reliable for detection of diffuse white matter (WM) injury. Our aim was to assess in very preterm infants the reliability of a classification system for WM injury on sequential cUS throughout the neonatal period, using magnetic resonance imaging (MRI) as reference standard. In 110 very preterm infants (gestational age <32 weeks), serial cUS during admission (median 8, range 4-22) and again around term equivalent age (TEA) and a single MRI around TEA were performed. cUS during admission were assessed for presence of WM changes, and contemporaneous cUS and MRI around TEA additionally for abnormality of lateral ventricles. Sequential cUS (from birth up to TEA) and MRI were classified as normal/mildly abnormal, moderately abnormal, or severely abnormal, based on a combination of findings of the WM and lateral ventricles. Predictive values of the cUS classification were calculated. Sequential cUS were classified as normal/mildly abnormal, moderately abnormal, and severely abnormal in, respectively, 22%, 65%, and 13% of infants and MRI in, respectively, 30%, 52%, and 18%. The positive predictive value of the cUS classification for the MRI classification was high for severely abnormal WM (0.79) but lower for normal/mildly abnormal (0.67) and moderately abnormal (0.64) WM. Sequential cUS during the neonatal period detects severely abnormal WM in very preterm infants but is less reliable for mildly and moderately abnormal WM. MRI around TEA seems needed to reliably detect WM injury in very preterm infants. (orig.)

Validation of Land Cover Products Using Reliability Evaluation Methods

Directory of Open Access Journals (Sweden)

Wenzhong Shi

2015-06-01

Full Text Available Validation of land cover products is a fundamental task prior to data applications. Current validation schemes and methods are, however, suited only for assessing classification accuracy and disregard the reliability of land cover products. The reliability evaluation of land cover products should be undertaken to provide reliable land cover information. In addition, the lack of high-quality reference data often constrains validation and affects the reliability results of land cover products. This study proposes a validation schema to evaluate the reliability of land cover products, including two methods, namely, result reliability evaluation and process reliability evaluation. Result reliability evaluation computes the reliability of land cover products using seven reliability indicators. Process reliability evaluation analyzes the reliability propagation in the data production process to obtain the reliability of land cover products. Fuzzy fault tree analysis is introduced and improved in the reliability analysis of a data production process. Research results show that the proposed reliability evaluation scheme is reasonable and can be applied to validate land cover products. Through the analysis of the seven indicators of result reliability evaluation, more information on land cover can be obtained for strategic decision-making and planning, compared with traditional accuracy assessment methods. Process reliability evaluation without the need for reference data can facilitate the validation and reflect the change trends of reliabilities to some extent.

Automated cell type discovery and classification through knowledge transfer

Science.gov (United States)

Lee, Hao-Chih; Kosoy, Roman; Becker, Christine E.

2017-01-01

Abstract Motivation: Recent advances in mass cytometry allow simultaneous measurements of up to 50 markers at single-cell resolution. However, the high dimensionality of mass cytometry data introduces computational challenges for automated data analysis and hinders translation of new biological understanding into clinical applications. Previous studies have applied machine learning to facilitate processing of mass cytometry data. However, manual inspection is still inevitable and becoming the barrier to reliable large-scale analysis. Results: We present a new algorithm called Automated Cell-type Discovery and Classification (ACDC) that fully automates the classification of canonical cell populations and highlights novel cell types in mass cytometry data. Evaluations on real-world data show ACDC provides accurate and reliable estimations compared to manual gating results. Additionally, ACDC automatically classifies previously ambiguous cell types to facilitate discovery. Our findings suggest that ACDC substantially improves both reliability and interpretability of results obtained from high-dimensional mass cytometry profiling data. Availability and Implementation: A Python package (Python 3) and analysis scripts for reproducing the results are availability on https://bitbucket.org/dudleylab/acdc. Contact: brian.kidd@mssm.edu or joel.dudley@mssm.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28158442

Differential Classification of Dementia

Directory of Open Access Journals (Sweden)

E. Mohr

1995-01-01

Full Text Available In the absence of biological markers, dementia classification remains complex both in terms of characterization as well as early detection of the presence or absence of dementing symptoms, particularly in diseases with possible secondary dementia. An empirical, statistical approach using neuropsychological measures was therefore developed to distinguish demented from non-demented patients and to identify differential patterns of cognitive dysfunction in neurodegenerative disease. Age-scaled neurobehavioral test results (Wechsler Adult Intelligence Scale—Revised and Wechsler Memory Scale from Alzheimer's (AD and Huntington's (HD patients, matched for intellectual disability, as well as normal controls were used to derive a classification formula. Stepwise discriminant analysis accurately (99% correct distinguished controls from demented patients, and separated the two patient groups (79% correct. Variables discriminating between HD and AD patient groups consisted of complex psychomotor tasks, visuospatial function, attention and memory. The reliability of the classification formula was demonstrated with a new, independent sample of AD and HD patients which yielded virtually identical results (classification accuracy for dementia: 96%; AD versus HD: 78%. To validate the formula, the discriminant function was applied to Parkinson's (PD patients, 38% of whom were classified as demented. The validity of the classification was demonstrated by significant PD subgroup differences on measures of dementia not included in the discriminant function. Moreover, a majority of demented PD patients (65% were classified as having an HD-like pattern of cognitive deficits, in line with previous reports of the subcortical nature of PD dementia. This approach may thus be useful in classifying presence or absence of dementia and in discriminating between dementia subtypes in cases of secondary or coincidental dementia.

Supervised machine learning and active learning in classification of radiology reports.

Science.gov (United States)

Nguyen, Dung H M; Patrick, Jon D

2014-01-01

This paper presents an automated system for classifying the results of imaging examinations (CT, MRI, positron emission tomography) into reportable and non-reportable cancer cases. This system is part of an industrial-strength processing pipeline built to extract content from radiology reports for use in the Victorian Cancer Registry. In addition to traditional supervised learning methods such as conditional random fields and support vector machines, active learning (AL) approaches were investigated to optimize training production and further improve classification performance. The project involved two pilot sites in Victoria, Australia (Lake Imaging (Ballarat) and Peter MacCallum Cancer Centre (Melbourne)) and, in collaboration with the NSW Central Registry, one pilot site at Westmead Hospital (Sydney). The reportability classifier performance achieved 98.25% sensitivity and 96.14% specificity on the cancer registry's held-out test set. Up to 92% of training data needed for supervised machine learning can be saved by AL. AL is a promising method for optimizing the supervised training production used in classification of radiology reports. When an AL strategy is applied during the data selection process, the cost of manual classification can be reduced significantly. The most important practical application of the reportability classifier is that it can dramatically reduce human effort in identifying relevant reports from the large imaging pool for further investigation of cancer. The classifier is built on a large real-world dataset and can achieve high performance in filtering relevant reports to support cancer registries. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Reliability and validity of a survey to measure bowel function and quality of life in long-term rectal cancer survivors.

Science.gov (United States)

Wendel, Christopher S; Grant, Marcia; Herrinton, Lisa; Temple, Larissa K F; Hornbrook, Mark C; McMullen, Carmit K; Bulkley, Joanna E; Altschuler, Andrea; Krouse, Robert S

2014-12-01

Examination of reliability and validity of a specialized health-related quality of life questionnaire for rectal cancer (RC) survivors (≥5 years post-diagnosis). We mailed 1,063 Kaiser Permanente (KP) RC survivors (313 ostomy and 750 anastomosis) a questionnaire containing the Modified City of Hope Quality of Life-Ostomy (mCOH-QOL-O), SF-12v2, Duke-UNC Functional Social Support Questionnaire (FSSQ), and Memorial Sloan-Kettering Cancer Center Bowel Function Index (BFI). We adapted certain BFI items for use by subjects with intestinal ostomies. We evaluated reliability for all instruments with inter-item correlations and Cronbach's alpha. We assessed construct validity only for the BFI in the ostomy group, because such use has not been reported. The overall response rate was 60.5 % (577 respondents/953 eligible). Compared with non-responders, participants were on average 2 years younger and more likely non-Hispanic white, resided in educationally non-deprived areas, and had KP membership through a group. The mCOH-QOL-O, SF-12, and FSSQ were found to be highly reliable for RC survivors. In the ostomy group, BFI Urgency/Soilage and Dietary subscales were found to be reliable, but Frequency was not. Factor analysis supported the construct of Urgency/Soilage and Dietary subscales in the ostomy group, although one item had a moderate correlation with all three factors. The BFI also demonstrated good concurrent validity with other instruments in the ostomy group. With possible exception of the BFI Frequency subscale in populations with ostomies, components of our survey can be used for the entire population of RC survivors, no matter whether they received anastomosis or ostomy.

Latent structure and reliability analysis of the measure of body apperception: cross-validation for head and neck cancer patients.

Science.gov (United States)

Jean-Pierre, Pascal; Fundakowski, Christopher; Perez, Enrique; Jean-Pierre, Shadae E; Jean-Pierre, Ashley R; Melillo, Angelica B; Libby, Rachel; Sargi, Zoukaa

2013-02-01

Cancer and its treatments are associated with psychological distress that can negatively impact self-perception, psychosocial functioning, and quality of life. Patients with head and neck cancers (HNC) are particularly susceptible to psychological distress. This study involved a cross-validation of the Measure of Body Apperception (MBA) for HNC patients. One hundred and twenty-two English-fluent HNC patients between 20 and 88 years of age completed the MBA on a Likert scale ranging from "1 = disagree" to "4 = agree." We assessed the latent structure and internal consistency reliability of the MBA using Principal Components Analysis (PCA) and Cronbach's coefficient alpha (α), respectively. We determined convergent and divergent validities of the MBA using correlations with the Hospital Anxiety and Depression Scale (HADS), observer disfigurement rating, and patients' clinical and demographic variables. The PCA revealed a coherent set of items that explained 38 % of the variance. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.73 and the Bartlett's test of sphericity was statistically significant (χ (2) (28) = 253.64; p 0.05). The MBA is a valid and reliable screening measure of body apperception for HNC patients.

Structural and reliability analysis of a patient satisfaction with cancer-related care measure: a multisite patient navigation research program study.

Science.gov (United States)

Jean-Pierre, Pascal; Fiscella, Kevin; Freund, Karen M; Clark, Jack; Darnell, Julie; Holden, Alan; Post, Douglas; Patierno, Steven R; Winters, Paul C

2011-02-15

Patient satisfaction is an important outcome measure of quality of cancer care and 1 of the 4 core study outcomes of the National Cancer Institute (NCI)-sponsored Patient Navigation Research Program to reduce race/ethnicity-based disparities in cancer care. There is no existing patient satisfaction measure that spans the spectrum of cancer-related care. The objective of this study was to develop a Patient Satisfaction With Cancer Care measure that is relevant to patients receiving diagnostic/therapeutic cancer-related care. The authors developed a conceptual framework, an operational definition of Patient Satisfaction With Cancer Care, and an item pool based on literature review, expert feedback, group discussion, and consensus. The 35-item Patient Satisfaction With Cancer Care measure was administered to 891 participants from the multisite NCI-sponsored Patient Navigation Research Program. Principal components analysis (PCA) was conducted for latent structure analysis. Internal consistency was assessed using Cronbach coefficient alpha (α). Divergent analysis was performed using correlation analyses between the Patient Satisfaction With Cancer Care, the Communication and Attitudinal Self-Efficacy-Cancer, and demographic variables. The PCA revealed a 1-dimensional measure with items forming a coherent set explaining 62% of the variance in patient satisfaction. Reliability assessment revealed high internal consistency (α ranging from 0.95 to 0.96). The Patient Satisfaction With Cancer Care demonstrated good face validity, convergent validity, and divergent validity, as indicated by moderate correlations with subscales of the Communication and Attitudinal Self-Efficacy-Cancer (all P .05). The Patient Satisfaction With Cancer Care is a valid tool for assessing satisfaction with cancer-related care for this sample. Copyright © 2010 American Cancer Society.

Changes in classification of genetic variants in BRCA1 and BRCA2.

Science.gov (United States)

Kast, Karin; Wimberger, Pauline; Arnold, Norbert

2018-02-01

Classification of variants of unknown significance (VUS) in the breast cancer genes BRCA1 and BRCA2 changes with accumulating evidence for clinical relevance. In most cases down-staging towards neutral variants without clinical significance is possible. We searched the database of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC) for changes in classification of genetic variants as an update to our earlier publication on genetic variants in the Centre of Dresden. Changes between 2015 and 2017 were recorded. In the group of variants of unclassified significance (VUS, Class 3, uncertain), only changes of classification towards neutral genetic variants were noted. In BRCA1, 25% of the Class 3 variants (n = 2/8) changed to Class 2 (likely benign) and Class 1 (benign). In BRCA2, in 50% of the Class 3 variants (n = 16/32), a change to Class 2 (n = 10/16) or Class 1 (n = 6/16) was observed. No change in classification was noted in Class 4 (likely pathogenic) and Class 5 (pathogenic) genetic variants in both genes. No up-staging from Class 1, Class 2 or Class 3 to more clinical significance was observed. All variants with a change in classification in our cohort were down-staged towards no clinical significance by a panel of experts of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC). Prevention in families with Class 3 variants should be based on pedigree based risks and should not be guided by the presence of a VUS.

A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

Directory of Open Access Journals (Sweden)

Wang Lily

2008-07-01

Full Text Available Abstract Background Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. Results In the present paper we identify methodological biases of prior work comparing random forests and support vector machines and conduct a new rigorous evaluation of the two algorithms that corrects these limitations. Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random forests, often by a large margin. Our data also underlines the importance of sound research design in benchmarking and comparison of bioinformatics algorithms. Conclusion We found that both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.

Cancer of the esophagus

International Nuclear Information System (INIS)

Pereslegin, I.A.

1985-01-01

Classification, clinical characters, diagnosis of the esophagus cancer are given. Radiotherapy for radical and palliative treatment of the esophagus cancer is described. Dose distributions in gamma therapy of different forms of the esophagus cancer are given. Combined treatment (preoperative radiotherapy and operation) is briefly described

Classification and Compression of Multi-Resolution Vectors: A Tree Structured Vector Quantizer Approach

Science.gov (United States)

2002-01-01

their expression profile and for classification of cells into tumerous and non- tumerous classes. Then we will present a parallel tree method for... cancerous cells. We will use the same dataset and use tree structured classifiers with multi-resolution analysis for classifying cancerous from non- cancerous ...cells. We have the expressions of 4096 genes from 98 different cell types. Of these 98, 72 are cancerous while 26 are non- cancerous . We are interested

Classification deficits in Alzheimer's disease with special reference to living and nonliving things.

Science.gov (United States)

Montanes, P; Goldblum, M C; Boller, F

1996-08-01

The present study was conducted to assess the hypothesis that visual similarity between exemplars within a semantic category may affect differentially the recognition process of living and nonliving things, according to task demands, in patients with semantic memory disorders. Thirty-nine Alzheimer's patients and 39 normal elderly subjects were presented with a task in which they had to classify pictures and words, depicting either living or nonliving things, at two levels of classification: subordinate (e.g., mammals versus birds or tools versus vehicles) and attribute (e.g., wild versus domestic animals or fast versus slow vehicles). Contrary to previous results (Montañes, Goldblum, & Boller, 1995) in a naming task, but as expected, living things were better classified than nonliving ones by both controls and patients. As expected, classifications at the subordinate level also gave rise to better performance than classifications at the attribute level. Although (and somewhat unexpectedly) no advantage of picture over word classification emerged, some effects consistent with the hypothesis that visual similarity affects picture classification emerged, in particular within a subgroup of patients with predominant verbal deficits and the most severe semantic memory disorders. This subgroup obtained a better score on classification of pictures than of words depicting living items (that share many visual features) when classification is at the subordinate level (for which visual similarity is a reliable clue to classification), but met with major difficulties when classifying those pictures at the attribute level (for which shared visual features are not reliable clues to classification). These results emphasize the fact that some "normal" effects specific to items in living and nonliving categories have to be considered among the factors causing selective category-specific deficits in patients, as well as their relevance in achieving tasks which require either

Cross-Cultural Translation, Adaptation and Reliability of the Danish M. D. Andeson Dysphagia Inventory (MDADI) in Patients with Head and Neck Cancer.

Science.gov (United States)

Hajdú, Sara Fredslund; Plaschke, Christina Caroline; Johansen, Christoffer; Dalton, Susanne Oksbjerg; Wessel, Irene

2017-08-01

The objectives were to translate and culturally adapt the M.D. Anderson Dysphagia Inventory (MDADI) into Danish and subsequently test the reliability of the Danish version. The MDADI was translated into Danish and cross culturally adapted through cognitive interviews. The final version was test-retest evaluated in a group of head and neck cancer (HNC) patients who responded to the questionnaire twice with a mean of eight days apart. Interclass correlation coefficient, Cronbach's alpha, floor and ceiling effects, standard error of measurement and minimal detectable change were investigated. Fourteen patients were interviewed on the comprehensibility of the Danish MDADI, and all found the questionnaire meaningful, easy to understand, non-offensive and to include relevant aspects of dysphagia related to HNC. Sixty-four patients were included in the test-retest study. Especially, one item in the emotional scale (E7) appeared to be often misinterpreted, and ceiling effects were found in all four subdomains (global, emotional, functional and physical). The four subdomains and the composite score showed acceptable test-retest reliability and internal consistency in a Danish population of HNC patients. The Danish MDADI is reliable in terms of internal consistency and test-retest reproducibility and can be used in assessing the health-related quality of life in head and neck cancer patients with dysphagia.

Modeling and evaluating repeatability and reproducibility of ordinal classifications

NARCIS (Netherlands)

de Mast, J.; van Wieringen, W.N.

2010-01-01

This paper argues that currently available methods for the assessment of the repeatability and reproducibility of ordinal classifications are not satisfactory. The paper aims to study whether we can modify a class of models from Item Response Theory, well established for the study of the reliability

Color classification of veal carcasses: Past, present and future

NARCIS (Netherlands)

Lucassen, M.P.; Alferdinck, J.W.A.M.; Megen, R. van

2010-01-01

In The Netherlands, veal carcasses are classified on color, conformation and fatness. In the past 20 years, major efforts have been put into the development of a reliable color classification system. Initially, the color of the musculus rectus abdominis was visually matched to a 10-point scale.

Classification of masses on mammograms using support vector machine

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Chu, Yong; Li, Lihua; Goldgof, Dmitry B.; Qui, Yan; Clark, Robert A.

2003-05-01

Mammography is the most effective method for early detection of breast cancer. However, the positive predictive value for classification of malignant and benign lesion from mammographic images is not very high. Clinical studies have shown that most biopsies for cancer are very low, between 15% and 30%. It is important to increase the diagnostic accuracy by improving the positive predictive value to reduce the number of unnecessary biopsies. In this paper, a new classification method was proposed to distinguish malignant from benign masses in mammography by Support Vector Machine (SVM) method. Thirteen features were selected based on receiver operating characteristic (ROC) analysis of classification using individual feature. These features include four shape features, two gradient features and seven Laws features. With these features, SVM was used to classify the masses into two categories, benign and malignant, in which a Gaussian kernel and sequential minimal optimization learning technique are performed. The data set used in this study consists of 193 cases, in which there are 96 benign cases and 97 malignant cases. The leave-one-out evaluation of SVM classifier was taken. The results show that the positive predict value of the presented method is 81.6% with the sensitivity of 83.7% and the false-positive rate of 30.2%. It demonstrated that the SVM-based classifier is effective in mass classification.

Therapy of pancreatic cancer

International Nuclear Information System (INIS)

Takeda, Yutaka; Kitagawa, Toru; Nakamori, Shoji

2009-01-01

Pancreatic cancer remains one of the most difficult diseases to cure. Japan pancreas society guidelines for management of pancreatic cancer indicate therapeutic algorithm according to the clinical stage. For locally limited pancreatic cancer (cStage I, II, III in Japanese classification system), surgical resection is recommended, however prognosis is still poor. Major randomized controlled trials of resected pancreatic cancer indicates that adjuvant chemotherapy is superior to observation and gemcitabine is superior to 5-fluorouracil (FU). For locally advanced resectable pancreatic cancer (cStage IVa in Japanese classification system (JCS)), we perform neoadjuvant chemoradiotherapy. Phase I study established a recommended dose of 800 mg gemcitabine and radiation dose of 36 Gy. For locally advanced nonresectable pancreatic cancer (cStage IVa in JCS), chemoradiotherapy followed by chemotherapy is recommended. Although pancreatic cancer is chemotherapy resistant tumor, systemic chemotherapy is recommended for metastatic pancreatic cancer (cStage IVb in JCS). Single-agent gemcitabine is the standard first line agent for the treatment of advanced pancreatic cancer. Meta-analysis of chemotherapy showed possibility of survival benefit of gemcitabine combination chemotherapy over gemcitabine alone. We hope gemcitabine combination chemotherapy or molecular targeted therapy will improve prognosis of pancreatic cancer in the future. (author)

Data Applicability of Heritage and New Hardware for Launch Vehicle System Reliability Models

Science.gov (United States)

Al Hassan Mohammad; Novack, Steven

2015-01-01

Many launch vehicle systems are designed and developed using heritage and new hardware. In most cases, the heritage hardware undergoes modifications to fit new functional system requirements, impacting the failure rates and, ultimately, the reliability data. New hardware, which lacks historical data, is often compared to like systems when estimating failure rates. Some qualification of applicability for the data source to the current system should be made. Accurately characterizing the reliability data applicability and quality under these circumstances is crucial to developing model estimations that support confident decisions on design changes and trade studies. This presentation will demonstrate a data-source classification method that ranks reliability data according to applicability and quality criteria to a new launch vehicle. This method accounts for similarities/dissimilarities in source and applicability, as well as operating environments like vibrations, acoustic regime, and shock. This classification approach will be followed by uncertainty-importance routines to assess the need for additional data to reduce uncertainty.

Cancer survival classification using integrated data sets and intermediate information.

Science.gov (United States)

Kim, Shinuk; Park, Taesung; Kon, Mark

2014-09-01

Although numerous studies related to cancer survival have been published, increasing the prediction accuracy of survival classes still remains a challenge. Integration of different data sets, such as microRNA (miRNA) and mRNA, might increase the accuracy of survival class prediction. Therefore, we suggested a machine learning (ML) approach to integrate different data sets, and developed a novel method based on feature selection with Cox proportional hazard regression model (FSCOX) to improve the prediction of cancer survival time. FSCOX provides us with intermediate survival information, which is usually discarded when separating survival into 2 groups (short- and long-term), and allows us to perform survival analysis. We used an ML-based protocol for feature selection, integrating information from miRNA and mRNA expression profiles at the feature level. To predict survival phenotypes, we used the following classifiers, first, existing ML methods, support vector machine (SVM) and random forest (RF), second, a new median-based classifier using FSCOX (FSCOX_median), and third, an SVM classifier using FSCOX (FSCOX_SVM). We compared these methods using 3 types of cancer tissue data sets: (i) miRNA expression, (ii) mRNA expression, and (iii) combined miRNA and mRNA expression. The latter data set included features selected either from the combined miRNA/mRNA profile or independently from miRNAs and mRNAs profiles (IFS). In the ovarian data set, the accuracy of survival classification using the combined miRNA/mRNA profiles with IFS was 75% using RF, 86.36% using SVM, 84.09% using FSCOX_median, and 88.64% using FSCOX_SVM with a balanced 22 short-term and 22 long-term survivor data set. These accuracies are higher than those using miRNA alone (70.45%, RF; 75%, SVM; 75%, FSCOX_median; and 75%, FSCOX_SVM) or mRNA alone (65.91%, RF; 63.64%, SVM; 72.73%, FSCOX_median; and 70.45%, FSCOX_SVM). Similarly in the glioblastoma multiforme data, the accuracy of miRNA/mRNA using IFS

The differentiation of malignant and benign human breast tissue at surgical margins and biopsy using x-ray interaction data and Bayesian classification

International Nuclear Information System (INIS)

Mersov, A.; Mersov, G.; Al-Ebraheem, A.; Cornacchi, S.; Gohla, G.; Lovrics, P.; Farquharson, M.J.

2014-01-01

Worldwide, about 1.3 million women are diagnosed with breast cancer annually with an estimated 465,000 deaths. Accordingly, there is a need for high accuracy and speed in diagnosis of lesions suspected of being cancerous. This study assesses the interaction data collected from low energy x-rays within breast tissue samples. Trace element concentrations are assessed using x-ray fluorescence, as well as electron density, and molecular structure which are examined using incoherent and coherent scatter, respectively. Our work to date has shown that such data can provide a quantitative measure of certain tissue characterising parameters and hence, through appropriate modelling, could be used to classify samples for uses such as surgical margin detection and biopsy examination. The parameters used in this study for comparing the normal and tumour tissue sample populations are: levels of elements Ca, Cu, Fe, Br, Zn, Rb, K; the area, FWHM and amplitude from peaks fitted to the coherent scatter profile that are associated with fat, fibre and water content; the ratio of the Compton and coherent scatter peak area, FWHM and amplitude from the incoherent scatter profile. The novelty of the approach to this work lies in the fact that the classification process does not rely on one source of data but combines several measurements, the data from which in this application are modelled using a method based on Bayesian classification. The reliability of the classifications was assessed by its application to diagnostically known data that was not itself included in the thresholds determination. The results of the classification of over 70 breast tissue samples will be presented in this study. Bayesian modelling was carried out using selected significant parameters for classification resulting in 71% of normal tissue samples (n=35) and 66% of tumour tissue samples (n=35) being correctly classified when using all the samples. Bayesian classification using the same variables on all

Developing and Validating the Communication Function Classification System for Individuals with Cerebral Palsy

Science.gov (United States)

Hidecker, Mary Jo Cooley; Paneth, Nigel; Rosenbaum, Peter L.; Kent, Raymond D.; Lillie, Janet; Eulenberg, John B.; Chester, Ken, Jr.; Johnson, Brenda; Michalsen, Lauren; Evatt, Morgan; Taylor, Kara

2011-01-01

Aim: The purpose of this study was to create and validate the Communication Function Classification System (CFCS) for children with cerebral palsy (CP), for use by a wide variety of individuals who are interested in CP. This paper reports the content validity, interrater reliability, and test-retest reliability of the CFCS for children with CP.…

The potential prognostic value of connexin 26 and 46 expression in neoadjuvant-treated breast cancer

International Nuclear Information System (INIS)

Teleki, Ivett; Varga, Zsuzsanna; Krenacs, Tibor; Szasz, Marcell A; Kulka, Janina; Wichmann, Barna; Leo, Cornelia; Papassotiropoulos, Barbel; Riemenschnitter, Cosima; Moch, Holger

2013-01-01

Several classification systems are available to assess pathological response to neoadjuvant chemotherapy in breast cancer, but reliable biomarkers to predict the efficiency of primary systemic therapy (PST) are still missing. Deregulation of gap junction channel forming connexins (Cx) has been implicated in carcinogenesis and tumour progression through loss of cell cycle control. In this study we correlated Cx expression and cell proliferation with disease survival and pathological response to neoadjuvant chemotherapy in breast cancers using existing classification systems. The expression of Cx26, Cx32, Cx43, Cx46 and Ki67 was evaluated in 96 breast cancer patients prior to and after neoadjuvant chemotherapy using duplicate cores in tissue microarrays (TMA). Cx plaques of <1μm were detected with multilayer, multichannel fluorescence digital microscopy. Current classifications to assess residual tumour burden after primary systemic therapy included the EWGBSP, CPS-EG, Miller-Payne, Sataloff and NSABP systems. In our cohort dominated by hormone receptor (ER/PR) positive and HER2 negative cases, only the CPS-EG classification showed prognostic relevance: cases with scores 1–2 had significantly better overall survival (p=0.015) than cases with scores 3–5. Pre-chemotherapy Cx43 expression correlated positively with hormone receptor status both before and after chemotherapy and had a negative correlation with HER2 expression pre-chemotherapy. There was a positive correlation between Cx32 and HER2 expression pre-chemotherapy and between Cx32 and Ki67 expression post-chemotherapy. A negative correlation was found between post-chemotherapy Cx46 and Ki67 expression. Decreased post-chemotherapy Cx26 expression (<5%) statistically correlated with better overall survival (p=0.011). Moderate or higher Cx46 expression (>20%) pre- and post-chemotherapy correlated with significantly better survival in the intermediate prognostic subgroups of EWGBSP TR2b (p pre-chemo =0

A simplified immunohistochemical classification of skeletal muscle fibres in mouse

Directory of Open Access Journals (Sweden)

M. Kammoun

2014-06-01

Full Text Available The classification of muscle fibres is of particular interest for the study of the skeletal muscle properties in a wide range of scientific fields, especially animal phenotyping. It is therefore important to define a reliable method for classifying fibre types. The aim of this study was to establish a simplified method for the immunohistochemical classification of fibres in mouse. To carry it out, we first tested a combination of several anti myosin heavy chain (MyHC antibodies in order to choose a minimum number of antibodies to implement a semi-automatic classification. Then, we compared the classification of fibres to the MyHC electrophoretic pattern on the same samples. Only two anti MyHC antibodies on serial sections with the fluorescent labeling of the Laminin were necessary to classify properly fibre types in Tibialis Anterior and Soleus mouse muscles in normal physiological conditions. This classification was virtually identical to the classification realized by the electrophoretic separation of MyHC. This immunohistochemical classification can be applied to the total area of Tibialis Anterior and Soleus mouse muscles. Thus, we provide here a useful, simple and time-efficient method for immunohistochemical classification of fibres, applicable for research in mouse

Analysis of framelets for breast cancer diagnosis.

Science.gov (United States)

Thivya, K S; Sakthivel, P; Venkata Sai, P M

2016-01-01

Breast cancer is the second threatening tumor among the women. The effective way of reducing breast cancer is its early detection which helps to improve the diagnosing process. Digital mammography plays a significant role in mammogram screening at earlier stage of breast carcinoma. Even though, it is very difficult to find accurate abnormality in prevalent screening by radiologists. But the possibility of precise breast cancer screening is encouraged by predicting the accurate type of abnormality through Computer Aided Diagnosis (CAD) systems. The two most important indicators of breast malignancy are microcalcifications and masses. In this study, framelet transform, a multiresolutional analysis is investigated for the classification of the above mentioned two indicators. The statistical and co-occurrence features are extracted from the framelet decomposed mammograms with different resolution levels and support vector machine is employed for classification with k-fold cross validation. This system achieves 94.82% and 100% accuracy in normal/abnormal classification (stage I) and benign/malignant classification (stage II) of mass classification system and 98.57% and 100% for microcalcification system when using the MIAS database.

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

Science.gov (United States)

Huang, Shujun; Cai, Nianguang; Pacheco, Pedro Penzuti; Narrandes, Shavira; Wang, Yang; Xu, Wayne

2018-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Reliable categorisation of visual scoring of coronary artery calcification on low-dose CT for lung cancer screening: validation with the standard Agatston score

Energy Technology Data Exchange (ETDEWEB)

Huang, Yi-Luan; Wu, Fu-Zong; Wang, Yen-Chi [Kaohsiung Veterans General Hospital, Department of Radiology, Kaohsiung 813 (China); National Yang Ming University, Faculty of Medicine, School of Medicine, Taipei (China); Ju, Yu-Jeng [National Taiwan University, Department of Psychology, Taipei (China); Mar, Guang-Yuan [Kaohsiung Veterans General Hospital, Division of Cardiology, Department of Medicine, Kaohsiung 813 (China); Chuo, Chiung-Chen [Kaohsiung Veterans General Hospital, Department of Radiology, Kaohsiung 813 (China); Lin, Huey-Shyan [Fooyin University, School of Nursing, Kaohsiung (China); Wu, Ming-Ting [Kaohsiung Veterans General Hospital, Department of Radiology, Kaohsiung 813 (China); National Yang Ming University, Faculty of Medicine, School of Medicine, Taipei (China); National Yang Ming University, Institute of Clinical Medicine, Taipei (China)

2013-05-15

To validate the reliability of the visual coronary artery calcification score (VCACS) on low-dose CT (LDCT) for concurrent screening of CAC and lung cancer. We enrolled 401 subjects receiving LDCT for lung cancer screening and ECG-gated CT for the Agatston score (AS). LDCT was reconstructed with 3- and 5-mm slice thickness (LDCT-3mm and LDCT-5mm respectively) for VCACS to obtain VCACS-3mm and VCACS-5mm respectively. After a training session comprising 32 cases, two observers performed four-scale VCACS (absent, mild, moderate, severe) of 369 data sets independently, the results were compared with four-scale AS (0, 1-100, 101-400, >400). CACs were present in 39.6 % (146/369) of subjects. The sensitivity of VCACS-3mm was higher than for VCACS-5mm (83.6 % versus 74.0 %). The median of AS of the 24 false-negative cases in VCACS-3mm was 2.3 (range 1.1-21.1). The false-negative rate for detecting AS {>=} 10 on LDCT-3mm was 1.9 %. VCACS-3mm had higher concordance with AS than VCACS-5mm (k = 0.813 versus k = 0.685). An extended test of VCACS-3mm for four junior observers showed high inter-observer reliability (intra-class correlation = 0.90) and good concordance with AS (k = 0.662-0.747). This study validated the reliability of VCACS on LDCT for lung cancer screening and showed that LDCT-3mm was more feasible than LDCT-5mm for CAD risk stratification. (orig.)

Evaluating and categorizing the reliability of distribution coefficient values in the sorption database

International Nuclear Information System (INIS)

Ochs, Michael; Saito, Yoshihiko; Kitamura, Akira; Shibata, Masahiro; Sasamoto, Hiroshi; Yui, Mikazu

2007-03-01

Japan Atomic Energy Agency (JAEA) has developed the sorption database (JNC-SDB) for bentonite and rocks in order to assess the retardation property of important radioactive elements in natural and engineered barriers in the H12 report. The database includes distribution coefficient (K d ) of important radionuclides. The K d values in the SDB are about 20,000 data. The SDB includes a great variety of K d and additional key information from many different literatures. Accordingly, the classification guideline and classification system were developed in order to evaluate the reliability of each K d value (Th, Pa, U, Np, Pu, Am, Cm, Cs, Ra, Se, Tc on bentonite). The reliability of 3740 K d values are evaluated and categorized. (author)

Reliability-Based Decision Fusion in Multimodal Biometric Verification Systems

Directory of Open Access Journals (Sweden)

Kryszczuk Krzysztof

2007-01-01

Full Text Available We present a methodology of reliability estimation in the multimodal biometric verification scenario. Reliability estimation has shown to be an efficient and accurate way of predicting and correcting erroneous classification decisions in both unimodal (speech, face, online signature and multimodal (speech and face systems. While the initial research results indicate the high potential of the proposed methodology, the performance of the reliability estimation in a multimodal setting has not been sufficiently studied or evaluated. In this paper, we demonstrate the advantages of using the unimodal reliability information in order to perform an efficient biometric fusion of two modalities. We further show the presented method to be superior to state-of-the-art multimodal decision-level fusion schemes. The experimental evaluation presented in this paper is based on the popular benchmarking bimodal BANCA database.

Visual classification of feral cat Felis silvestris catus vocalizations.

Science.gov (United States)

Owens, Jessica L; Olsen, Mariana; Fontaine, Amy; Kloth, Christopher; Kershenbaum, Arik; Waller, Sara

2017-06-01

Cat vocal behavior, in particular, the vocal and social behavior of feral cats, is poorly understood, as are the differences between feral and fully domestic cats. The relationship between feral cat social and vocal behavior is important because of the markedly different ecology of feral and domestic cats, and enhanced comprehension of the repertoire and potential information content of feral cat calls can provide both better understanding of the domestication and socialization process, and improved welfare for feral cats undergoing adoption. Previous studies have used conflicting classification schemes for cat vocalizations, often relying on onomatopoeic or popular descriptions of call types (e.g., "miow"). We studied the vocalizations of 13 unaltered domestic cats that complied with our behavioral definition used to distinguish feral cats from domestic. A total of 71 acoustic units were extracted and visually analyzed for the construction of a hierarchical classification of vocal sounds, based on acoustic properties. We identified 3 major categories (tonal, pulse, and broadband) that further breakdown into 8 subcategories, and show a high degree of reliability when sounds are classified blindly by independent observers (Fleiss' Kappa K  = 0.863). Due to the limited behavioral contexts in this study, additional subcategories of cat vocalizations may be identified in the future, but our hierarchical classification system allows for the addition of new categories and new subcategories as they are described. This study shows that cat vocalizations are diverse and complex, and provides an objective and reliable classification system that can be used in future studies.

Classifications of Acute Scaphoid Fractures: A Systematic Literature Review.

Science.gov (United States)

Ten Berg, Paul W; Drijkoningen, Tessa; Strackee, Simon D; Buijze, Geert A

2016-05-01

Background In the lack of consensus, surgeon-based preference determines how acute scaphoid fractures are classified. There is a great variety of classification systems with considerable controversies. Purposes The purpose of this study was to provide an overview of the different classification systems, clarifying their subgroups and analyzing their popularity by comparing citation indexes. The intention was to improve data comparison between studies using heterogeneous fracture descriptions. Methods We performed a systematic review of the literature based on a search of medical literature from 1950 to 2015, and a manual search using the reference lists in relevant book chapters. Only original descriptions of classifications of acute scaphoid fractures in adults were included. Popularity was based on citation index as reported in the databases of Web of Science (WoS) and Google Scholar. Articles that were cited <10 times in WoS were excluded. Results Our literature search resulted in 308 potentially eligible descriptive reports of which 12 reports met the inclusion criteria. We distinguished 13 different (sub) classification systems based on (1) fracture location, (2) fracture plane orientation, and (3) fracture stability/displacement. Based on citations numbers, the Herbert classification was most popular, followed by the Russe and Mayo classifications. All classification systems were based on plain radiography. Conclusions Most classification systems were based on fracture location, displacement, or stability. Based on the controversy and limited reliability of current classification systems, suggested research areas for an updated classification include three-dimensional fracture pattern etiology and fracture fragment mobility assessed by dynamic imaging.

Multi-agent Negotiation Mechanisms for Statistical Target Classification in Wireless Multimedia Sensor Networks

Science.gov (United States)

Wang, Xue; Bi, Dao-wei; Ding, Liang; Wang, Sheng

2007-01-01

The recent availability of low cost and miniaturized hardware has allowed wireless sensor networks (WSNs) to retrieve audio and video data in real world applications, which has fostered the development of wireless multimedia sensor networks (WMSNs). Resource constraints and challenging multimedia data volume make development of efficient algorithms to perform in-network processing of multimedia contents imperative. This paper proposes solving problems in the domain of WMSNs from the perspective of multi-agent systems. The multi-agent framework enables flexible network configuration and efficient collaborative in-network processing. The focus is placed on target classification in WMSNs where audio information is retrieved by microphones. To deal with the uncertainties related to audio information retrieval, the statistical approaches of power spectral density estimates, principal component analysis and Gaussian process classification are employed. A multi-agent negotiation mechanism is specially developed to efficiently utilize limited resources and simultaneously enhance classification accuracy and reliability. The negotiation is composed of two phases, where an auction based approach is first exploited to allocate the classification task among the agents and then individual agent decisions are combined by the committee decision mechanism. Simulation experiments with real world data are conducted and the results show that the proposed statistical approaches and negotiation mechanism not only reduce memory and computation requirements in WMSNs but also significantly enhance classification accuracy and reliability. PMID:28903223

Classification and risk assessment of individuals with familial polyposis, Gardner's syndrome, and familial non-polyposis colon cancer from [3H]thymidine labeling patterns in colonic epithelial cells

International Nuclear Information System (INIS)

Lipkin, M.; Blattner, W.A.; Gardner, E.J.; Burt, R.W.; Lynch, H.; Deschner, E.; Winawer, S.; Fraumeni, J.F. Jr.

1984-01-01

A probabilistic analysis has been developed to assist the binary classification and risk assessment of members of familial colon cancer kindreds. The analysis is based on the microautoradiographic observation of [ 3 H]thymidine-labeled epithelial cells in colonic mucosa of the kindred members. From biopsies of colonic mucosa which are labeled with [ 3 H]thymidine in vitro, the degree of similarity of each subject's cell-labeling pattern measured over entire crypts was automatically compared to the labeling patterns of high-risk and low-risk reference populations. Each individual was then presumptively classified and assigned to one of the reference populations, and a degree of risk for the classification was provided. In carrying out the analysis, a linear score was calculated for each individual relative to each of the reference populations, and the classification was based on the polarity of the score difference; the degree of risk was then quantitated from the magnitude of the score difference. When the method was applied to kindreds having either familial polyposis or familial non-polyposis colon cancer, it effectively segregated individuals affected with disease from others at low risk, with sensitivity and specificity ranging from 71 to 92%. Further application of the method to asymptomatic family members believed to be at 50% risk on the basis of pedigree evaluation revealed a biomodal distribution to nearly zero or full risk. The accuracy and simplicity of this approach and its capability of revealing early stages of abnormal colonic epithelial cell development indicate potential for preclinical screening of subjects at risk in cancer-prone kindreds and for assisting the analysis of modes of inheritance

Numeric pathologic lymph node classification shows prognostic superiority to topographic pN classification in esophageal squamous cell carcinoma.

Science.gov (United States)

Sugawara, Kotaro; Yamashita, Hiroharu; Uemura, Yukari; Mitsui, Takashi; Yagi, Koichi; Nishida, Masato; Aikou, Susumu; Mori, Kazuhiko; Nomura, Sachiyo; Seto, Yasuyuki

2017-10-01

The current eighth tumor node metastasis lymph node category pathologic lymph node staging system for esophageal squamous cell carcinoma is based solely on the number of metastatic nodes and does not consider anatomic distribution. We aimed to assess the prognostic capability of the eighth tumor node metastasis pathologic lymph node staging system (numeric-based) compared with the 11th Japan Esophageal Society (topography-based) pathologic lymph node staging system in patients with esophageal squamous cell carcinoma. We retrospectively reviewed the clinical records of 289 patients with esophageal squamous cell carcinoma who underwent esophagectomy with extended lymph node dissection during the period from January 2006 through June 2016. We compared discrimination abilities for overall survival, recurrence-free survival, and cancer-specific survival between these 2 staging systems using C-statistics. The median number of dissected and metastatic nodes was 61 (25% to 75% quartile range, 45 to 79) and 1 (25% to 75% quartile range, 0 to 3), respectively. The eighth tumor node metastasis pathologic lymph node staging system had a greater ability to accurately determine overall survival (C-statistics: tumor node metastasis classification, 0.69, 95% confidence interval, 0.62-0.76; Japan Esophageal Society classification; 0.65, 95% confidence interval, 0.58-0.71; P = .014) and cancer-specific survival (C-statistics: tumor node metastasis classification, 0.78, 95% confidence interval, 0.70-0.87; Japan Esophageal Society classification; 0.72, 95% confidence interval, 0.64-0.80; P = .018). Rates of total recurrence rose as the eighth tumor node metastasis pathologic lymph node stage increased, while stratification of patients according to the topography-based node classification system was not feasible. Numeric nodal staging is an essential tool for stratifying the oncologic outcomes of patients with esophageal squamous cell carcinoma even in the cohort in which adequate

Analysis of information security reliability: A tutorial

International Nuclear Information System (INIS)

Kondakci, Suleyman

2015-01-01

This article presents a concise reliability analysis of network security abstracted from stochastic modeling, reliability, and queuing theories. Network security analysis is composed of threats, their impacts, and recovery of the failed systems. A unique framework with a collection of the key reliability models is presented here to guide the determination of the system reliability based on the strength of malicious acts and performance of the recovery processes. A unique model, called Attack-obstacle model, is also proposed here for analyzing systems with immunity growth features. Most computer science curricula do not contain courses in reliability modeling applicable to different areas of computer engineering. Hence, the topic of reliability analysis is often too diffuse to most computer engineers and researchers dealing with network security. This work is thus aimed at shedding some light on this issue, which can be useful in identifying models, their assumptions and practical parameters for estimating the reliability of threatened systems and for assessing the performance of recovery facilities. It can also be useful for the classification of processes and states regarding the reliability of information systems. Systems with stochastic behaviors undergoing queue operations and random state transitions can also benefit from the approaches presented here. - Highlights: • A concise survey and tutorial in model-based reliability analysis applicable to information security. • A framework of key modeling approaches for assessing reliability of networked systems. • The framework facilitates quantitative risk assessment tasks guided by stochastic modeling and queuing theory. • Evaluation of approaches and models for modeling threats, failures, impacts, and recovery analysis of information systems

Semi-supervised Probabilistic Distance Clustering and the Uncertainty of Classification

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Iyigun, Cem; Ben-Israel, Adi

Semi-supervised clustering is an attempt to reconcile clustering (unsupervised learning) and classification (supervised learning, using prior information on the data). These two modes of data analysis are combined in a parameterized model, the parameter θ ∈ [0, 1] is the weight attributed to the prior information, θ = 0 corresponding to clustering, and θ = 1 to classification. The results (cluster centers, classification rule) depend on the parameter θ, an insensitivity to θ indicates that the prior information is in agreement with the intrinsic cluster structure, and is otherwise redundant. This explains why some data sets (such as the Wisconsin breast cancer data, Merz and Murphy, UCI repository of machine learning databases, University of California, Irvine, CA) give good results for all reasonable classification methods. The uncertainty of classification is represented here by the geometric mean of the membership probabilities, shown to be an entropic distance related to the Kullback-Leibler divergence.

Validity and Reliability of the U.S. National Cancer Institute's Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE)