Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.

Science.gov (United States)

Sieberts, Solveig K; Zhu, Fan; García-García, Javier; Stahl, Eli; Pratap, Abhishek; Pandey, Gaurav; Pappas, Dimitrios; Aguilar, Daniel; Anton, Bernat; Bonet, Jaume; Eksi, Ridvan; Fornés, Oriol; Guney, Emre; Li, Hongdong; Marín, Manuel Alejandro; Panwar, Bharat; Planas-Iglesias, Joan; Poglayen, Daniel; Cui, Jing; Falcao, Andre O; Suver, Christine; Hoff, Bruce; Balagurusamy, Venkat S K; Dillenberger, Donna; Neto, Elias Chaibub; Norman, Thea; Aittokallio, Tero; Ammad-Ud-Din, Muhammad; Azencott, Chloe-Agathe; Bellón, Víctor; Boeva, Valentina; Bunte, Kerstin; Chheda, Himanshu; Cheng, Lu; Corander, Jukka; Dumontier, Michel; Goldenberg, Anna; Gopalacharyulu, Peddinti; Hajiloo, Mohsen; Hidru, Daniel; Jaiswal, Alok; Kaski, Samuel; Khalfaoui, Beyrem; Khan, Suleiman Ali; Kramer, Eric R; Marttinen, Pekka; Mezlini, Aziz M; Molparia, Bhuvan; Pirinen, Matti; Saarela, Janna; Samwald, Matthias; Stoven, Véronique; Tang, Hao; Tang, Jing; Torkamani, Ali; Vert, Jean-Phillipe; Wang, Bo; Wang, Tao; Wennerberg, Krister; Wineinger, Nathan E; Xiao, Guanghua; Xie, Yang; Yeung, Rae; Zhan, Xiaowei; Zhao, Cheng; Greenberg, Jeff; Kremer, Joel; Michaud, Kaleb; Barton, Anne; Coenen, Marieke; Mariette, Xavier; Miceli, Corinne; Shadick, Nancy; Weinblatt, Michael; de Vries, Niek; Tak, Paul P; Gerlag, Danielle; Huizinga, Tom W J; Kurreeman, Fina; Allaart, Cornelia F; Louis Bridges, S; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M; Bridges, S Louis; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M

2016-08-23

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

Serum Adiponectin, Vitamin D, and Alpha-Fetoprotein in Children with Chronic Hepatitis C: Can They Predict Treatment Response?

Directory of Open Access Journals (Sweden)

Mohamed Ahmed Khedr

2015-01-01

Full Text Available Background & Aims. The currently available treatment for chronic hepatitis C (CHC in children is costly and with much toxicity. So, predicting the likelihood of response before starting therapy is important. Methods. Serum adiponectin, vitamin D, and alpha-fetoprotein (AFP were measured before starting pegylated-interferon/ribavirin therapy for 50 children with CHC. Another 21 healthy children were recruited as controls. Results. Serum adiponectin, vitamin D, and AFP were higher in the CHC group than healthy controls (p<0.0001, p=0.071, and p=0.87, resp.. In univariate analysis, serum adiponectin was significantly higher in responders than nonresponders (p<0.0001 and at a cutoff value ≥8.04 ng/mL it can predict treatment response by 77.8% sensitivity and 92.9% specificity, while both AFP and viremia were significantly lower in responders than nonresponders, p<0.0001 and p=0.0003, respectively, and at cutoff values ≤3.265 ng/mL and ≤235,384 IU/mL, respectively, they can predict treatment response with a sensitivity of 83.3% for both and specificity of 85.7% and 78.6%, respectively. In multivariate analysis, adiponectin was found to be the only independent predictor of treatment response (p=0.044. Conclusions. The pretreatment serum level of adiponectin can predict the likelihood of treatment response, thus avoiding toxicities for those unlikely to respond to therapy.

Stathmin protein level, a potential predictive marker for taxane treatment response in endometrial cancer.

Directory of Open Access Journals (Sweden)

Henrica M J Werner

Full Text Available Stathmin is a prognostic marker in many cancers, including endometrial cancer. Preclinical studies, predominantly in breast cancer, have suggested that stathmin may additionally be a predictive marker for response to paclitaxel. We first evaluated the response to paclitaxel in endometrial cancer cell lines before and after stathmin knock-down. Subsequently we investigated the clinical response to paclitaxel containing chemotherapy in metastatic endometrial cancer in relation to stathmin protein level in tumors. Stathmin level was also determined in metastatic lesions, analyzing changes in biomarker status on disease progression. Knock-down of stathmin improved sensitivity to paclitaxel in endometrial carcinoma cell lines with both naturally higher and lower sensitivity to paclitaxel. In clinical samples, high stathmin level was demonstrated to be associated with poor response to paclitaxel containing chemotherapy and to reduced disease specific survival only in patients treated with such combination. Stathmin level increased significantly from primary to metastatic lesions. This study suggests, supported by both preclinical and clinical data, that stathmin could be a predictive biomarker for response to paclitaxel treatment in endometrial cancer. Re-assessment of stathmin level in metastatic lesions prior to treatment start may be relevant. Also, validation in a randomized clinical trial will be important.

Failure of 111In-labeled bleomycin tumor scanning to predict response to bleomycin (NSC-125066) treatment

International Nuclear Information System (INIS)

Jones, S.E.; Salmon, S.E.; Durie, B.G.M.

1974-01-01

The question of whether or not 111 In-labeled bleomycin is predictive of the response of tumors to bleomycin treatment is answered in the negative. The real test of the value of labeled bleomycin as a predictor of response will be possible only when a tightly labeled bleomycin with fully preserved biologic activity is synthesized. The negative results of this study do not invalidate further investigations of the predictive values of labeled anticancer drugs

A study on the predictability of acute lymphoblastic leukaemia response to treatment using a hybrid oncosimulator.

Science.gov (United States)

Ouzounoglou, Eleftherios; Kolokotroni, Eleni; Stanulla, Martin; Stamatakos, Georgios S

2018-02-06

Efficient use of Virtual Physiological Human (VPH)-type models for personalized treatment response prediction purposes requires a precise model parameterization. In the case where the available personalized data are not sufficient to fully determine the parameter values, an appropriate prediction task may be followed. This study, a hybrid combination of computational optimization and machine learning methods with an already developed mechanistic model called the acute lymphoblastic leukaemia (ALL) Oncosimulator which simulates ALL progression and treatment response is presented. These methods are used in order for the parameters of the model to be estimated for retrospective cases and to be predicted for prospective ones. The parameter value prediction is based on a regression model trained on retrospective cases. The proposed Hybrid ALL Oncosimulator system has been evaluated when predicting the pre-phase treatment outcome in ALL. This has been correctly achieved for a significant percentage of patient cases tested (approx. 70% of patients). Moreover, the system is capable of denying the classification of cases for which the results are not trustworthy enough. In that case, potentially misleading predictions for a number of patients are avoided, while the classification accuracy for the remaining patient cases further increases. The results obtained are particularly encouraging regarding the soundness of the proposed methodologies and their relevance to the process of achieving clinical applicability of the proposed Hybrid ALL Oncosimulator system and VPH models in general.

Response to intravenous fentanyl infusion predicts subsequent response to transdermal fentanyl.

Science.gov (United States)

Hayashi, Norihito; Kanai, Akifumi; Suzuki, Asaha; Nagahara, Yuki; Okamoto, Hirotsugu

2016-04-01

Prediction of the response to transdermal fentanyl (FENtd) before its use for chronic pain is desirable. We tested the hypothesis that the response to intravenous fentanyl infusion (FENiv) can predict the response to FENtd, including the analgesic and adverse effects. The study subjects were 70 consecutive patients with chronic pain. The response to fentanyl at 0.1 mg diluted in 50 ml of physiological saline and infused over 30 min was tested. This was followed by treatment with FENtd (Durotep MT patch 2.1 mg) at a dose of 12.5 µg/h for 2 weeks. Pain intensity before and after FENiv and 2 weeks after FENtd, and the response to treatment, were assessed by the numerical rating scale (NRS), clinical global impression-improvement scale (CGI-I), satisfaction scale (SS), and adverse effects. The NRS score decreased significantly from 7 (4-9) [median (range)] at baseline to 3 (0-8) after FENiv (p 0.04, each). The analgesic and side effects after intravenous fentanyl infusion can be used to predict the response to short-term transdermal treatment with fentanyl.

Qualitative assessment of awake nasopharyngoscopy for prediction of oral appliance treatment response in obstructive sleep apnoea.

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Sutherland, Kate; Chan, Andrew S L; Ngiam, Joachim; Darendeliler, M Ali; Cistulli, Peter A

2018-01-23

Clinical methods to identify responders to oral appliance (OA) therapy for obstructive sleep apnoea (OSA) are needed. Awake nasopharyngoscopy during mandibular advancement, with image capture and subsequent processing and analysis, may predict treatment response. A qualitative assessment of awake nasopharyngoscopy would be simpler for clinical practice. We aimed to determine if a qualitative classification system of nasopharyngoscopic observations reflects treatment response. OSA patients were recruited for treatment with a customised two-piece OA. A custom scoring sheet was used to record observations of the pharyngeal airway (velopharynx, oropharynx, hypopharynx) during supine nasopharyngoscopy in response to mandibular advancement and performance of the Müller manoeuvre. Qualitative scores for degree ( 75%), collapse pattern (concentric, anteroposterior, lateral) and diameter change (uniform, anteroposterior, lateral) were recorded. Treatment outcome was confirmed by polysomnography after a titration period of 14.6 ± 9.8 weeks. Treatment response was defined as (1) Treatment AHI  50% AHI reduction and (3) > 50% AHI reduction. Eighty OSA patients (53.8% male) underwent nasopharyngoscopy. The most common naspharyngoscopic observation with mandibular advancement was a small ( 75% velopharyngeal collapse on performance of the Müller manoeuvre. Mandibular advancement reduced the observed level of pharyngeal collapse at all three pharyngeal regions (p < 0.001). None of the nasopharyngoscopic qualitative scores differed between responder and non-responder groups. Qualitative assessment of awake nasopharyngoscopy appears useful for assessing the effect of mandibular advancement on upper airway collapsibility. However, it is not sensitive enough to predict oral appliance treatment outcome.

Rapid response predicts 12-month post-treatment outcomes in binge-eating disorder: theoretical and clinical implications

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Grilo, C. M.; White, M. A.; Wilson, G. T.; Gueorguieva, R.; Masheb, R. M.

2011-01-01

Background We examined rapid response in obese patients with binge-eating disorder (BED) in a clinical trial testing cognitive behavioral therapy (CBT) and behavioral weight loss (BWL). Method Altogether, 90 participants were randomly assigned to CBT or BWL. Assessments were performed at baseline, throughout and post-treatment and at 6- and 12-month follow-ups. Rapid response, defined as ≥70% reduction in binge eating by week four, was determined by receiver operating characteristic curves and used to predict outcomes. Results Rapid response characterized 57% of participants (67% of CBT, 47% of BWL) and was unrelated to most baseline variables. Rapid response predicted greater improvements across outcomes but had different prognostic significance and distinct time courses for CBT versus BWL. Patients receiving CBT did comparably well regardless of rapid response in terms of reduced binge eating and eating disorder psychopathology but did not achieve weight loss. Among patients receiving BWL, those without rapid response failed to improve further. However, those with rapid response were significantly more likely to achieve binge-eating remission (62% v. 13%) and greater reductions in binge-eating frequency, eating disorder psychopathology and weight loss. Conclusions Rapid response to treatment in BED has prognostic significance through 12-month follow-up, provides evidence for treatment specificity and has clinical implications for stepped-care treatment models for BED. Rapid responders who receive BWL benefit in terms of both binge eating and short-term weight loss. Collectively, these findings suggest that BWL might be a candidate for initial intervention in stepped-care models with an evaluation of progress after 1 month to identify non-rapid responders who could be advised to consider a switch to a specialized treatment. PMID:21923964

Global DNA methylation is altered by neoadjuvant chemoradiotherapy in rectal cancer and may predict response to treatment - A pilot study.

LENUS (Irish Health Repository)

Tsang, J S

2014-07-28

In rectal cancer, not all tumours display a response to neoadjuvant treatment. An accurate predictor of response does not exist to guide patient-specific treatment. DNA methylation is a distinctive molecular pathway in colorectal carcinogenesis. Whether DNA methylation is altered by neoadjuvant treatment and a potential response predictor is unknown. We aimed to determine whether DNA methylation is altered by neoadjuvant chemoradiotherapy (CRT) and to determine its role in predicting response to treatment.

Citric Acid Metabolism in Resistant Hypertension: Underlying Mechanisms and Metabolic Prediction of Treatment Response.

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Martin-Lorenzo, Marta; Martinez, Paula J; Baldan-Martin, Montserrat; Ruiz-Hurtado, Gema; Prado, Jose Carlos; Segura, Julian; de la Cuesta, Fernando; Barderas, Maria G; Vivanco, Fernando; Ruilope, Luis Miguel; Alvarez-Llamas, Gloria

2017-11-01

Resistant hypertension (RH) affects 9% to 12% of hypertensive adults. Prolonged exposure to suboptimal blood pressure control results in end-organ damage and cardiovascular risk. Spironolactone is the most effective drug for treatment, but not all patients respond and side effects are not negligible. Little is known on the mechanisms responsible for RH. We aimed to identify metabolic alterations in urine. In addition, a potential capacity of metabolites to predict response to spironolactone was investigated. Urine was collected from 29 patients with RH and from a group of 13 subjects with pseudo-RH. For patients, samples were collected before and after spironolactone administration and were classified in responders (n=19) and nonresponders (n=10). Nuclear magnetic resonance was applied to identify altered metabolites and pathways. Metabolites were confirmed by liquid chromatography-mass spectrometry. Citric acid cycle was the pathway most significantly altered ( P citric acid cycle and deregulation of reactive oxygen species homeostasis control continue its activation after hypertension was developed. A metabolic panel showing alteration before spironolactone treatment and predicting future response of patients is shown. These molecular indicators will contribute optimizing the rate of control of RH patients with spironolactone. © 2017 American Heart Association, Inc.

Predictive value of dorso-lateral prefrontal connectivity for rTMS response in treatment-resistant depression: A brain perfusion SPECT study.

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Richieri, Raphaëlle; Verger, Antoine; Boyer, Laurent; Boucekine, Mohamed; David, Anthony; Lançon, Christophe; Cermolacce, Michel; Guedj, Eric

2018-05-18

Previous clinical trials have suggested that repetitive transcranial magnetic stimulation (rTMS) has a significant antidepressant effect in patients with treatment resistant depression (TRD). However, results remain heterogeneous with many patients without effective response. The aim of this SPECT study was to determine before treatment the predictive value of the connectivity of the stimulated area on further rTMS response in patients with TRD. Fifty-eight TRD patients performed a brain perfusion SPECT before high frequency rTMS of the left dorsolateral prefrontal cortex (DLPFC). A voxel based-analysis was achieved to compare connectivity of the left DLPFC in responders and non-responders using inter-regional correlations (p left DLPFC and the right cerebellum in comparison to non-responders, independently of age, gender, severity of depression, and severity of treatment resistance. The area under the curve for the combination of these two SPECT clusters to predict rTMS response was 0.756 (p left DLPFC predicts rTMS response before treatment. Crown Copyright © 2018. Published by Elsevier Inc. All rights reserved.

A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder.

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Khodayari-Rostamabad, Ahmad; Reilly, James P; Hasey, Gary M; de Bruin, Hubert; Maccrimmon, Duncan J

2013-10-01

The problem of identifying, in advance, the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD). A relatively small number of most discriminating features are selected from a large group of candidate features extracted from the subject's pre-treatment EEG, using a machine learning procedure for feature selection. The selected features are fed into a classifier, which was realized as a mixture of factor analysis (MFA) model, whose output is the predicted response in the form of a likelihood value. This likelihood indicates the extent to which the subject belongs to the responder vs. non-responder classes. The overall method was evaluated using a "leave-n-out" randomized permutation cross-validation procedure. A list of discriminating EEG biomarkers (features) was found. The specificity of the proposed method is 80.9% while sensitivity is 94.9%, for an overall prediction accuracy of 87.9%. There is a 98.76% confidence that the estimated prediction rate is within the interval [75%, 100%]. These results indicate that the proposed ML method holds considerable promise in predicting the efficacy of SSRI antidepressant therapy for MDD, based on a simple and cost-effective pre-treatment EEG. The proposed approach offers the potential to improve the treatment of major depression and to reduce health care costs. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Prediction of response to PPI therapy and factors influencing treatment outcome in patients with GORD: a prospective pragmatic trial using pantoprazole

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Tholen Anne

2011-05-01

Full Text Available Abstract Background Management of patients with gastro-oesophageal reflux disease (GORD can be assisted by information predicting the likely response to proton pump inhibitor (PPI treatment. The aim was to undertake a study of GORD patients designed to approximate ordinary clinical practice that would identify patient characteristics predicting symptomatic response to pantoprazole treatment. Methods 1888 patients with symptoms of GORD were enrolled in a multicentre, multinational, prospective, open study of 8 weeks pantoprazole treatment, 40 mg daily. Response was assessed by using the ReQuest™ questionnaire, by the investigator making conventional clinical enquiry and by asking patients about their satisfaction with symptom control. Factors including pre-treatment oesophagitis, gender, age, body mass index (BMI, Helicobacter pylori status, anxiety and depression, and concurrent IBS symptoms were examined using logistic regression to determine if they were related to response, judged from the ReQuest™-GI score. Results Poorer treatment responses were associated with non-erosive reflux disease, female gender, lower BMI, anxiety and concurrent irritable bowel syndrome symptoms before treatment. No association was found with age, Helicobacter pylori status or oesophagitis grade. Some reflux-related symptoms were still present in 14% of patients who declared themselves 'well-satisfied' with their symptom control. Conclusions Some readily identifiable features help to predict symptomatic responses to a PPI and consequently may help in managing patient expectation. ClinicalTrial.gov identifier: NCT00312806.

Tissue Biomarkers in Predicting Response to Sunitinib Treatment of Metastatic Renal Cell Carcinoma.

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Trávníček, Ivan; Branžovský, Jindřich; Kalusová, Kristýna; Hes, Ondřej; Holubec, Luboš; Pele, Kevin Bauleth; Ürge, Tomáš; Hora, Milan

2015-10-01

To identify tissue biomarkers that are predictive of the therapeutic effect of sunitinib in treatment of metastatic clear cell renal cell carcinoma (mCRCC). Our study included 39 patients with mCRCC treated with sunitinib. Patients were stratified into two groups based on their response to sunitinib treatment: non-responders (progression), and responders (stable disease, regression). The effect of treatment was measured by comparing imaging studies before the initiation treatment with those performed at between 3rd and 7th months of treatment, depending on the patient. Histological samples of tumor tissue and healthy renal parenchyma, acquired during surgery of the primary tumor, were examined with immunohistochemistry to detect tissue targets involved in the signaling pathways of tumor growth and neoangiogenesis. We selected mammalian target of rapamycine, p53, vascular endothelial growth factor, hypoxia-inducible factor 1 and 2 and carbonic anhydrase IX. We compared the average levels of biomarker expression in both, tumor tissue, as well as in healthy renal parenchyma. Results were evaluated using the Student's t-test. For responders, statistically significant differences in marker expression in tumor tissue versus healthy parenchyma were found for mTOR (4%/16.7%; p=0.01031), p53 (4%/12.7%; p=0.042019), VEGF (62.7%/45%; p=0.019836) and CAIX (45%/15.33%; p=0.001624). A further significant difference was found in the frequency of high expression (more than 60%) between tumor tissue and healthy parenchyma in VEGF (65%/35%; p=0.026487) and CAIX (42%/8%; p=0.003328). CAIX was expressed at high levels in the tumor tissue in both evaluated groups. A significantly higher expression of VEGF in CRCC in comparison to healthy parenchyma can predict a better response to sunitinib. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings

NARCIS (Netherlands)

Revell, A. D.; Wang, D.; Wood, R.; Morrow, C.; Tempelman, H.; Hamers, R. L.; Alvarez-Uria, G.; Streinu-Cercel, A.; Ene, L.; Wensing, A. M. J.; DeWolf, F.; Nelson, M.; Montaner, J. S.; Lane, H. C.; Larder, B. A.

2013-01-01

Genotypic HIV drug-resistance testing is typically 6065 predictive of response to combination antiretroviral therapy (ART) and is valuable for guiding treatment changes. Genotyping is unavailable in many resource-limited settings (RLSs). We aimed to develop models that can predict response to ART

CADrx for GBM Brain Tumors: Predicting Treatment Response from Changes in Diffusion-Weighted MRI

Directory of Open Access Journals (Sweden)

Matthew S. Brown

2009-11-01

Full Text Available The goal of this study was to develop a computer-aided therapeutic response (CADrx system for early prediction of drug treatment response for glioblastoma multiforme (GBM brain tumors with diffusion weighted (DW MR images. In conventional Macdonald assessment, tumor response is assessed nine weeks or more post-treatment. However, we will investigate the ability of DW-MRI to assess response earlier, at five weeks post treatment. The apparent diffusion coefficient (ADC map, calculated from DW images, has been shown to reveal changes in the tumor’s microenvironment preceding morphologic tumor changes. ADC values in treated brain tumors could theoretically both increase due to the cell kill (and thus reduced cell density and decrease due to inhibition of edema. In this study, we investigated the effectiveness of features that quantify changes from pre- and post-treatment tumor ADC histograms to detect treatment response. There are three parts to this study: first, tumor regions were segmented on T1w contrast enhanced images by Otsu’s thresholding method, and mapped from T1w images onto ADC images by a 3D region of interest (ROI mapping tool using DICOM header information; second, ADC histograms of the tumor region were extracted from both pre- and five weeks post-treatment scans, and fitted by a two-component Gaussian mixture model (GMM. The GMM features as well as standard histogram-based features were extracted. Finally, supervised machine learning techniques were applied for classification of responders or non-responders. The approach was evaluated with a dataset of 85 patients with GBM under chemotherapy, in which 39 responded and 46 did not, based on tumor volume reduction. We compared adaBoost, random forest and support vector machine classification algorithms, using ten-fold cross validation, resulting in the best accuracy of 69.41% and the corresponding area under the curve (Az of 0.70.

Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings.

Science.gov (United States)

Revell, A D; Wang, D; Wood, R; Morrow, C; Tempelman, H; Hamers, R L; Alvarez-Uria, G; Streinu-Cercel, A; Ene, L; Wensing, A M J; DeWolf, F; Nelson, M; Montaner, J S; Lane, H C; Larder, B A

2013-06-01

Genotypic HIV drug-resistance testing is typically 60%-65% predictive of response to combination antiretroviral therapy (ART) and is valuable for guiding treatment changes. Genotyping is unavailable in many resource-limited settings (RLSs). We aimed to develop models that can predict response to ART without a genotype and evaluated their potential as a treatment support tool in RLSs. Random forest models were trained to predict the probability of response to ART (≤400 copies HIV RNA/mL) using the following data from 14 891 treatment change episodes (TCEs) after virological failure, from well-resourced countries: viral load and CD4 count prior to treatment change, treatment history, drugs in the new regimen, time to follow-up and follow-up viral load. Models were assessed by cross-validation during development, with an independent set of 800 cases from well-resourced countries, plus 231 cases from Southern Africa, 206 from India and 375 from Romania. The area under the receiver operating characteristic curve (AUC) was the main outcome measure. The models achieved an AUC of 0.74-0.81 during cross-validation and 0.76-0.77 with the 800 test TCEs. They achieved AUCs of 0.58-0.65 (Southern Africa), 0.63 (India) and 0.70 (Romania). Models were more accurate for data from the well-resourced countries than for cases from Southern Africa and India (P < 0.001), but not Romania. The models identified alternative, available drug regimens predicted to result in virological response for 94% of virological failures in Southern Africa, 99% of those in India and 93% of those in Romania. We developed computational models that predict virological response to ART without a genotype with comparable accuracy to genotyping with rule-based interpretation. These models have the potential to help optimize antiretroviral therapy for patients in RLSs where genotyping is not generally available.

Brain anatomy and chemistry may predict treatment response in paediatric obsessive--compulsive disorder.

Science.gov (United States)

Rosenberg, D R; MacMillan, S N; Moore, G J

2001-06-01

Obsessive--compulsive disorder (OCD) is a severe, highly prevalent and often chronically disabling illness with frequent onset in childhood and adolescence. This underscores the importance of studying the illness during childhood near the onset of illness to minimize potential confounds of long-term illness duration and treatment intervention as well as to examine the developmental underpinnings of the illness. In this review, the authors focus on an integrated series of brain-imaging studies in paediatric OCD suggesting a reversible glutamatergically mediated thalamo-cortical--striatal dysfunction in OCD and their relevance for improved diagnosis and treatment of the condition. Developmental neurobiological models for OCD are presented and particular attention is devoted to evaluating neuroimaging studies designed to test these models and how they may help predict treatment response in paediatric OCD.

Empirically derived personality subtyping for predicting clinical symptoms and treatment response in bulimia nervosa.

Science.gov (United States)

Haynos, Ann F; Pearson, Carolyn M; Utzinger, Linsey M; Wonderlich, Stephen A; Crosby, Ross D; Mitchell, James E; Crow, Scott J; Peterson, Carol B

2017-05-01

Evidence suggests that eating disorder subtypes reflecting under-controlled, over-controlled, and low psychopathology personality traits constitute reliable phenotypes that differentiate treatment response. This study is the first to use statistical analyses to identify these subtypes within treatment-seeking individuals with bulimia nervosa (BN) and to use these statistically derived clusters to predict clinical outcomes. Using variables from the Dimensional Assessment of Personality Pathology-Basic Questionnaire, K-means cluster analyses identified under-controlled, over-controlled, and low psychopathology subtypes within BN patients (n = 80) enrolled in a treatment trial. Generalized linear models examined the impact of personality subtypes on Eating Disorder Examination global score, binge eating frequency, and purging frequency cross-sectionally at baseline and longitudinally at end of treatment (EOT) and follow-up. In the longitudinal models, secondary analyses were conducted to examine personality subtype as a potential moderator of response to Cognitive Behavioral Therapy-Enhanced (CBT-E) or Integrative Cognitive-Affective Therapy for BN (ICAT-BN). There were no baseline clinical differences between groups. In the longitudinal models, personality subtype predicted binge eating (p = 0.03) and purging (p = 0.01) frequency at EOT and binge eating frequency at follow-up (p = 0.045). The over-controlled group demonstrated the best outcomes on these variables. In secondary analyses, there was a treatment by subtype interaction for purging at follow-up (p = 0.04), which indicated a superiority of CBT-E over ICAT-BN for reducing purging among the over-controlled group. Empirically derived personality subtyping appears to be a valid classification system with potential to guide eating disorder treatment decisions. © 2016 Wiley Periodicals, Inc.(Int J Eat Disord 2017; 50:506-514). © 2016 Wiley Periodicals, Inc.

Circulating HER2 DNA after trastuzumab treatment predicts survival and response in breast cancer

DEFF Research Database (Denmark)

Sorensen, Boe S; Mortensen, Lise S; Andersen, Jørn

2010-01-01

BACKGROUND: Only a subset of breast cancer patients responds to the HER2 inhibitor trastuzumab, and methods to identify responders are needed. PATIENTS AND METHODS: We studied 28 patients with metastatic breast cancer that had amplified human epidermal growth factor receptor 2 (HER2) genes...... in their primary tumour and were treated with a combination of trastuzumab and chemotherapy. Plasma was collected and amplification of the HER2 gene in circulating DNA and the amounts of the extracellular domain (ECD) of HER2 were measured just before first treatment (n=28) and just before second treatment three...... response (p=0.02), and overall survival (p=0.05). HER2 ECD kinetics did not correlate to clinical data. CONCLUSION: We suggest that a decrease in HER2 gene amplification in the plasma predicts a more favourable response to trastuzumab....

MRI evaluation of anterior knee pain: predicting response to nonoperative treatment

International Nuclear Information System (INIS)

Wittstein, Jocelyn R.; Garrett, William E.; O'Brien, Seth D.; Vinson, Emily N.

2009-01-01

Tibial tubercle lateral deviation and patellofemoral chondromalacia are associated with anterior knee pain (AKP). We hypothesized that increased tibial tubercle lateral deviation and patellofemoral chondromalacia on magnetic resonance imaging correlates with the presence of AKP and with failure of nonoperative management. In this retrospective comparative study, a blinded musculoskeletal radiologist measured tibial tubercle lateral deviation relative to the trochlear groove in 15 controls, 15 physical therapy responders with AKP, and 15 physical therapy nonresponders with AKP. Patellar and trochlear cartilage was assessed for signal abnormality, irregularity, and defects. The mean tibial tubercle lateral deviation in controls, physical therapy responders, and physical therapy nonresponders were 9.32 ± 0.68, 13.01 ± 0.82, and 16.07 ± 1.16 mm, respectively (data are mean ± standard deviation). The correlation coefficients for tubercle deviation, chondromalacia patellae, and trochlear chondromalacia were 0.51 (P < 0.01), 0.44 (P < 0.01), and 0.28 (P < 0.05), respectively. On analysis of variance, tubercle deviation and chondromalacia patellae contributed significantly to prediction of AKP and response to physical therapy. The presence of chondromalacia patellae and a tubercle deviation greater than 14.6 mm is 100% specific and 67% sensitive with a positive predictive value of 100% and negative predictive value of 75% for failure of nonoperative management. Subjects with AKP have more laterally positioned tibial tubercles and are more likely to have patellar chondromalacia. Patients with AKP, chondromalacia patellae, and a tubercle deviation greater than 14.6 mm are unlikely to respond to nonoperative treatment. Knowledge of tibial tubercle lateralization and presence of chondromalacia patellae may assist clinicians in determining patient prognosis and selecting treatment options. (orig.)

MRI evaluation of anterior knee pain: predicting response to nonoperative treatment

Energy Technology Data Exchange (ETDEWEB)

Wittstein, Jocelyn R.; Garrett, William E. [Duke University Medical Center, Division of Orthopaedic Surgery, Durham, NC (United States); O' Brien, Seth D. [Brooke Army Medical Center, Department of Radiology, San Antonio, TX (United States); Vinson, Emily N. [Duke University Medical Center, Department of Radiology, Durham, NC (United States)

2009-09-15

Tibial tubercle lateral deviation and patellofemoral chondromalacia are associated with anterior knee pain (AKP). We hypothesized that increased tibial tubercle lateral deviation and patellofemoral chondromalacia on magnetic resonance imaging correlates with the presence of AKP and with failure of nonoperative management. In this retrospective comparative study, a blinded musculoskeletal radiologist measured tibial tubercle lateral deviation relative to the trochlear groove in 15 controls, 15 physical therapy responders with AKP, and 15 physical therapy nonresponders with AKP. Patellar and trochlear cartilage was assessed for signal abnormality, irregularity, and defects. The mean tibial tubercle lateral deviation in controls, physical therapy responders, and physical therapy nonresponders were 9.32 {+-} 0.68, 13.01 {+-} 0.82, and 16.07 {+-} 1.16 mm, respectively (data are mean {+-} standard deviation). The correlation coefficients for tubercle deviation, chondromalacia patellae, and trochlear chondromalacia were 0.51 (P < 0.01), 0.44 (P < 0.01), and 0.28 (P < 0.05), respectively. On analysis of variance, tubercle deviation and chondromalacia patellae contributed significantly to prediction of AKP and response to physical therapy. The presence of chondromalacia patellae and a tubercle deviation greater than 14.6 mm is 100% specific and 67% sensitive with a positive predictive value of 100% and negative predictive value of 75% for failure of nonoperative management. Subjects with AKP have more laterally positioned tibial tubercles and are more likely to have patellar chondromalacia. Patients with AKP, chondromalacia patellae, and a tubercle deviation greater than 14.6 mm are unlikely to respond to nonoperative treatment. Knowledge of tibial tubercle lateralization and presence of chondromalacia patellae may assist clinicians in determining patient prognosis and selecting treatment options. (orig.)

Dynamic contrast-enhanced CT in advanced lung cancer after chemotherapy with/within radiation therapy: Can it predict treatment responsiveness of the tumor?

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Yoo, Mi Ri; Whang, Sung Ho; Park, Chul Hwan; Kim, Sang Jin; Kim, Tae Hoon [Dept. of Radiology and Research Institute of Radiological Science, Yonsei University Health System, Seoul (Korea, Republic of)

2013-08-15

To evaluate the contrast enhancement patterns of lung cancer after chemotherapy using a dynamic contrast-enhanced (DCE) CT and to determine whether the enhancement patterns of tumors at early stages of treatment can predict treatment responses. Forty-two patients with advanced lung cancers underwent DCE-CT and follow-up CT after chemotherapy. We evaluated peak and net enhancement (PE and NE, respectively) and time-density curves (TDCs) (type A, B, C, and D) on DCE-CT images. Treatment responses were evaluated using revised Response Evaluation Criteria in Solid Tumor criteria. NE and PE values were significantly higher in the progressive disease (PD) groups than in the stable disease (SD) or partial response (PR) groups (p < 0.05). Types B, C, and D on TDCs were observed mostly in the PR and SD groups (96.0%), whereas type A was most frequent in the SD and PD groups (97.2%), which were significantly different in terms of PE and NE. Contrast enhancement pattern regarding the response of treatment on DCE-CT images could be helpful in predicting treatment response of advanced lung cancer after treatment.

Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with Dasatinib.

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Klammer, Martin; Dybowski, J Nikolaj; Hoffmann, Daniel; Schaab, Christoph

2015-01-01

Multivariate biomarkers that can predict the effectiveness of targeted therapy in individual patients are highly desired. Previous biomarker discovery studies have largely focused on the identification of single biomarker signatures, aimed at maximizing prediction accuracy. Here, we present a different approach that identifies multiple biomarkers by simultaneously optimizing their predictive power, number of features, and proximity to the drug target in a protein-protein interaction network. To this end, we incorporated NSGA-II, a fast and elitist multi-objective optimization algorithm that is based on the principle of Pareto optimality, into the biomarker discovery workflow. The method was applied to quantitative phosphoproteome data of 19 non-small cell lung cancer (NSCLC) cell lines from a previous biomarker study. The algorithm successfully identified a total of 77 candidate biomarker signatures predicting response to treatment with dasatinib. Through filtering and similarity clustering, this set was trimmed to four final biomarker signatures, which then were validated on an independent set of breast cancer cell lines. All four candidates reached the same good prediction accuracy (83%) as the originally published biomarker. Although the newly discovered signatures were diverse in their composition and in their size, the central protein of the originally published signature - integrin β4 (ITGB4) - was also present in all four Pareto signatures, confirming its pivotal role in predicting dasatinib response in NSCLC cell lines. In summary, the method presented here allows for a robust and simultaneous identification of multiple multivariate biomarkers that are optimized for prediction performance, size, and relevance.

Intravoxel incoherent motion (IVIM histogram biomarkers for prediction of neoadjuvant treatment response in breast cancer patients

Directory of Open Access Journals (Sweden)

Gene Y. Cho

Full Text Available Objective: To examine the prognostic capabilities of intravoxel incoherent motion (IVIM metrics and their ability to predict response to neoadjuvant treatment (NAT. Additionally, to observe changes in IVIM metrics between pre- and post-treatment MRI. Methods: This IRB-approved, HIPAA-compliant retrospective study observed 31 breast cancer patients (32 lesions. Patients underwent standard bilateral breast MRI along with diffusion-weighted imaging before and after NAT. Six patients underwent an additional IVIM-MRI scan 12–14 weeks after initial scan and 2 cycles of treatment. In addition to apparent diffusion coefficients (ADC from monoexponential decay, IVIM mean values (tissue diffusivity Dt, perfusion fraction fp, and pseudodiffusivity Dp and histogram metrics were derived using a biexponential model. An additional filter identified voxels of highly vascular tumor tissue (VTT, excluding necrotic or normal tissue. Clinical data include histology of biopsy and clinical response to treatment through RECIST assessment. Comparisons of treatment response were made using Wilcoxon rank-sum tests. Results: Average, kurtosis, and skewness of pseudodiffusion Dp significantly differentiated RECIST responders from nonresponders. ADC and Dt values generally increased (∼70% and VTT% values generally decreased (∼20% post-treatment. Conclusion: Dp metrics showed prognostic capabilities; slow and heterogeneous pseudodiffusion offer poor prognosis. Baseline ADC/Dt parameters were not significant predictors of response. This work suggests that IVIM mean values and heterogeneity metrics may have prognostic value in the setting of breast cancer NAT. Keywords: Breast cancer, Diffusion weighted MRI, Intravoxel incoherent motion, Neoadjuvant treatment, Response evaluation criteria in solid tumors

Sulfotransferase activity in plucked hair follicles predicts response to topical minoxidil in the treatment of female androgenetic alopecia.

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Roberts, Janet; Desai, Nisha; McCoy, John; Goren, Andy

2014-01-01

Two percent topical minoxidil is the only US Food and Drug Administration-approved drug for the treatment of female androgenetic alopecia (AGA). Its success has been limited by the low percentage of responders. Meta-analysis of several studies reporting the number of responders to 2% minoxidil monotherapy indicates moderate hair regrowth in only 13-20% of female patients. Five percent minoxidil solution, when used off-label, may increase the percentage of responders to as much as 40%. As such, a biomarker for predicting treatment response would have significant clinical utility. In a previous study, Goren et al. reported an association between sulfotransferase activity in plucked hair follicles and minoxidil response in a mixed cohort of male and female patients. The aim of this study was to replicate these findings in a well-defined cohort of female patients with AGA treated with 5% minoxidil daily for a period of 6 months. Consistent with the prior study, we found that sulfotransferase activity in plucked hair follicles predicts treatment response with 93% sensitivity and 83% specificity. Our study further supports the importance of minoxidil sulfation in eliciting a therapeutic response and provides further insight into novel targets for increasing minoxidil efficacy. © 2014 Wiley Periodicals, Inc.

Assessing Prediction Performance of Neoadjuvant Chemotherapy Response in Bladder Cancer

OpenAIRE

Cremer, Chris

2016-01-01

Neoadjuvant chemotherapy is a treatment routinely prescribed to patients diagnosed with muscle-invasive bladder cancer. Unfortunately, not all patients are responsive to this treatment and would greatly benefit from an accurate prediction of their expected response to chemotherapy. In this project, I attempt to develop a model that will predict response using tumour microarray data. I show that using my dataset, every method is insufficient at accurately classifying responders and non-respond...

Sleep spindles may predict response to cognitive-behavioral therapy for chronic insomnia.

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Dang-Vu, Thien Thanh; Hatch, Benjamin; Salimi, Ali; Mograss, Melodee; Boucetta, Soufiane; O'Byrne, Jordan; Brandewinder, Marie; Berthomier, Christian; Gouin, Jean-Philippe

2017-11-01

While cognitive-behavioral therapy for insomnia constitutes the first-line treatment for chronic insomnia, only few reports have investigated how sleep architecture relates to response to this treatment. In this pilot study, we aimed to determine whether pre-treatment sleep spindle density predicts treatment response to cognitive-behavioral therapy for insomnia. Twenty-four participants with chronic primary insomnia participated in a 6-week cognitive-behavioral therapy for insomnia performed in groups of 4-6 participants. Treatment response was assessed using the Pittsburgh Sleep Quality Index and the Insomnia Severity Index measured at pre- and post-treatment, and at 3- and 12-months' follow-up assessments. Secondary outcome measures were extracted from sleep diaries over 7 days and overnight polysomnography, obtained at pre- and post-treatment. Spindle density during stage N2-N3 sleep was extracted from polysomnography at pre-treatment. Hierarchical linear modeling analysis assessed whether sleep spindle density predicted response to cognitive-behavioral therapy. After adjusting for age, sex, and education level, lower spindle density at pre-treatment predicted poorer response over the 12-month follow-up, as reflected by a smaller reduction in Pittsburgh Sleep Quality Index over time. Reduced spindle density also predicted lower improvements in sleep diary sleep efficiency and wake after sleep onset immediately after treatment. There were no significant associations between spindle density and changes in the Insomnia Severity Index or polysomnography variables over time. These preliminary results suggest that inter-individual differences in sleep spindle density in insomnia may represent an endogenous biomarker predicting responsiveness to cognitive-behavioral therapy. Insomnia with altered spindle activity might constitute an insomnia subtype characterized by a neurophysiological vulnerability to sleep disruption associated with impaired responsiveness to

What good are positive emotions for treatment? Trait positive emotionality predicts response to Cognitive Behavioral Therapy for anxiety.

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Taylor, Charles T; Knapp, Sarah E; Bomyea, Jessica A; Ramsawh, Holly J; Paulus, Martin P; Stein, Murray B

2017-06-01

Cognitive behavioral therapy (CBT) is empirically supported for the treatment of anxiety disorders; however, not all individuals achieve recovery following CBT. Positive emotions serve a number of functions that theoretically should facilitate response to CBT - they promote flexible patterns of information processing and assimilation of new information, encourage approach-oriented behavior, and speed physiological recovery from negative emotions. We conducted a secondary analysis of an existing clinical trial dataset to test the a priori hypothesis that individual differences in trait positive emotions would predict CBT response for anxiety. Participants meeting diagnostic criteria for panic disorder (n = 28) or generalized anxiety disorder (n = 31) completed 10 weekly individual CBT sessions. Trait positive emotionality was assessed at pre-treatment, and severity of anxiety symptoms and associated impairment was assessed throughout treatment. Participants who reported a greater propensity to experience positive emotions at pre-treatment displayed the largest reduction in anxiety symptoms as well as fewer symptoms following treatment. Positive emotions remained a robust predictor of change in symptoms when controlling for baseline depression severity. Initial evidence supports the predictive value of trait positive emotions as a prognostic indicator for CBT outcome in a GAD and PD sample. Copyright © 2017 Elsevier Ltd. All rights reserved.

Gene expression markers in circulating tumor cells may predict bone metastasis and response to hormonal treatment in breast cancer.

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Wang, Haiying; Molina, Julian; Jiang, John; Ferber, Matthew; Pruthi, Sandhya; Jatkoe, Timothy; Derecho, Carlo; Rajpurohit, Yashoda; Zheng, Jian; Wang, Yixin

2013-11-01

Circulating tumor cells (CTCs) have recently attracted attention due to their potential as prognostic and predictive markers for the clinical management of metastatic breast cancer patients. The isolation of CTCs from patients may enable the molecular characterization of these cells, which may help establish a minimally invasive assay for the prediction of metastasis and further optimization of treatment. Molecular markers of proven clinical value may therefore be useful in predicting disease aggressiveness and response to treatment. In our earlier study, we identified a gene signature in breast cancer that appears to be significantly associated with bone metastasis. Among the genes that constitute this signature, trefoil factor 1 (TFF1) was identified as the most differentially expressed gene associated with bone metastasis. In this study, we investigated 25 candidate gene markers in the CTCs of metastatic breast cancer patients with different metastatic sites. The panel of the 25 markers was investigated in 80 baseline samples (first blood draw of CTCs) and 30 follow-up samples. In addition, 40 healthy blood donors (HBDs) were analyzed as controls. The assay was performed using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) with RNA extracted from CTCs captured by the CellSearch system. Our study indicated that 12 of the genes were uniquely expressed in CTCs and 10 were highly expressed in the CTCs obtained from patients compared to those obtained from HBDs. Among these genes, the expression of keratin 19 was highly correlated with the CTC count. The TFF1 expression in CTCs was a strong predictor of bone metastasis and the patients with a high expression of estrogen receptor β in CTCs exhibited a better response to hormonal treatment. Molecular characterization of these genes in CTCs may provide a better understanding of the mechanism underlying tumor metastasis and identify gene markers in CTCs for predicting disease progression and

Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept.

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Abajian, Aaron; Murali, Nikitha; Savic, Lynn Jeanette; Laage-Gaupp, Fabian Max; Nezami, Nariman; Duncan, James S; Schlachter, Todd; Lin, MingDe; Geschwind, Jean-François; Chapiro, Julius

2018-06-01

To use magnetic resonance (MR) imaging and clinical patient data to create an artificial intelligence (AI) framework for the prediction of therapeutic outcomes of transarterial chemoembolization by applying machine learning (ML) techniques. This study included 36 patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization. The cohort (age 62 ± 8.9 years; 31 men; 13 white; 24 Eastern Cooperative Oncology Group performance status 0, 10 status 1, 2 status 2; 31 Child-Pugh stage A, 4 stage B, 1 stage C; 1 Barcelona Clinic Liver Cancer stage 0, 12 stage A, 10 stage B, 13 stage C; tumor size 5.2 ± 3.0 cm; number of tumors 2.6 ± 1.1; and 30 conventional transarterial chemoembolization, 6 with drug-eluting embolic agents). MR imaging was obtained before and 1 month after transarterial chemoembolization. Image-based tumor response to transarterial chemoembolization was assessed with the use of the 3D quantitative European Association for the Study of the Liver (qEASL) criterion. Clinical information, baseline imaging, and therapeutic features were used to train logistic regression (LR) and random forest (RF) models to predict patients as treatment responders or nonresponders under the qEASL response criterion. The performance of each model was validated using leave-one-out cross-validation. Both LR and RF models predicted transarterial chemoembolization treatment response with an overall accuracy of 78% (sensitivity 62.5%, specificity 82.1%, positive predictive value 50.0%, negative predictive value 88.5%). The strongest predictors of treatment response included a clinical variable (presence of cirrhosis) and an imaging variable (relative tumor signal intensity >27.0). Transarterial chemoembolization outcomes in patients with HCC may be predicted before procedures by combining clinical patient data and baseline MR imaging with the use of AI and ML techniques. Copyright © 2018 SIR. Published by Elsevier Inc. All rights reserved.

Prediction of Response to Treatment in a Randomized Clinical Trial of Couple Therapy: A 2-Year Follow-Up

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Baucom, Brian R.; Atkins, David C.; Simpson, Lorelei E.; Christensen, Andrew

2009-01-01

Many studies have examined pretreatment predictors of immediate posttreatment outcome, but few studies have examined prediction of long-term treatment response to couple therapies. Four groups of predictors (demographic, intrapersonal, communication, and other interpersonal) and 2 moderators (pretreatment severity and type of therapy) were…

Early prediction of treatment response by serum CRP levels in patients with advanced esophageal cancer who underwent definitive chemoradiotherapy

International Nuclear Information System (INIS)

Yoneda, Masayuki; Fujiwara, Hitoshi; Okamura, Shinichi

2010-01-01

Serum C reactive protein (CRP) has been shown to be associated with the progression of esophageal cancer. The purpose of this study was to examine the relationship between treatment response and serum CRP levels in time course during definitive chemoradiotherapy (CRT) in terms of early prediction of CRT response by serum CRP. The subjects of this study were 36 patients with cT3/cT4 esophageal squamous cell carcinoma who underwent definitive CRT in our hospital. Serum CRP levels during definitive CRT (pretreatment, 1W, 2W and 3W after CRT initiation) were compared between CR and non-CR group. In addition, partition model was constructed to discriminate CR with non-CR and the prediction accuracy was evaluated. The patients were consisted of 28 males and 8 females. At pretreatment diagnosis, tumors were categorized as T3 (n=21) and T4 (n=15). Thirty four patients received FP-based chemotherapy and 2 patients received docetaxel-based chemotherapy. Treatment responses were categorized as CR (n=8), partial response (PR) (n=14), no change (NC) (n=2) and progressive disease (PD) (n=12). Serum CRP levels at the time of 2W after CRT initiation (CRT2W) in CR group were low compared to those in non-CR group (p=0.071). The partition model was constructed based on CRP levels at CRT2W. The prediction accuracies to discriminate CR from non-CR by CRP ≤0.1 were 50%, 82%, and 75% in sensitivity, specificity and accuracy, respectively. Serum CRP is a useful biomarker for an early prediction of CRT response. (author)

Stroke and TIA survivors’ cognitive beliefs and affective responses regarding treatment and future stroke risk differentially predict medication adherence and categorised stroke risk

OpenAIRE

Phillips, L. Alison; Diefenbach, Michael A.; Abrams, Jessica; Horowitz, Carol R.

2014-01-01

Cognitive beliefs and affective responses to illness and treatment are known to independently predict health behaviours. The purpose of the current study is to assess the relative importance of four psychological domains – specifically, affective illness, cognitive illness, affective treatment and cognitive treatment – for predicting stroke and transient ischemic attack (TIA) survivors’ adherence to stroke prevention medications as well as their objective, categorised stroke risk. We assessed...

Epidemiological and Clinical Baseline Characteristics as Predictive Biomarkers of Response to Anti-VEGF Treatment in Patients with Neovascular AMD

Directory of Open Access Journals (Sweden)

Miltiadis K. Tsilimbaris

2016-01-01

Full Text Available Purpose. To review the current literature investigating patient response to antivascular endothelial growth factor-A (VEGF therapy in the treatment of neovascular age-related macular degeneration (nAMD and to identify baseline characteristics that might predict response. Method. A literature search of the PubMed database was performed, using the keywords: AMD, anti-VEGF, biomarker, optical coherence tomography, treatment outcome, and predictor. The search was limited to articles published from 2006 to date. Exclusion criteria included phase 1 trials, case reports, studies focusing on indications other than nAMD, and oncology. Results. A total of 1467 articles were identified, of which 845 were excluded. Of the 622 remaining references, 47 met all the search criteria and were included in this review. Conclusion. Several baseline characteristics correlated with anti-VEGF treatment response, including best-corrected visual acuity, age, lesion size, and retinal thickness. The majority of factors were associated with disease duration, suggesting that longer disease duration before treatment results in worse treatment outcomes. This highlights the need for early treatment for patients with nAMD to gain optimal treatment outcomes. Many of the identified baseline characteristics are interconnected and cannot be evaluated in isolation; therefore multivariate analyses will be required to determine any specific relationship with treatment response.

Nerve Ultrasound Predicts Treatment Response in Chronic Inflammatory Demyelinating Polyradiculoneuropathy-a Prospective Follow-Up.

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Härtig, Florian; Ross, Marlene; Dammeier, Nele Maria; Fedtke, Nadin; Heiling, Bianka; Axer, Hubertus; Décard, Bernhard F; Auffenberg, Eva; Koch, Marilin; Rattay, Tim W; Krumbholz, Markus; Bornemann, Antje; Lerche, Holger; Winter, Natalie; Grimm, Alexander

2018-04-01

As reliable biomarkers of disease activity are lacking, monitoring of therapeutic response in chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) remains a challenge. We sought to determine whether nerve ultrasound and electrophysiology scoring could close this gap. In CIDP patients (fulfilling EFNS/PNS criteria), we performed high-resolution nerve ultrasound to determine ultrasound pattern sum scores (UPSS) and predominant echotexture nerve conduction study scores (NCSS) as well as Medical Research Council sum scores (MRCSS) and inflammatory neuropathy cause and treatment disability scores (INCAT) at baseline and after 12 months of standard treatment. We retrospectively correlated ultrasound morphology with nerve histology when available. 72/80 CIDP patients featured multifocal nerve enlargement, and 35/80 were therapy-naïve. At baseline, clinical scores correlated with NCSS (r 2  = 0.397 and r 2  = 0.443, p  50% of measured segments, possibly reflecting axonal degeneration; and 3) almost no enlargement, reflecting "burned-out" or "cured" disease without active inflammation. Clinical improvement after 12 months was best in patients with pattern 1 (up to 75% vs up to 43% in pattern 2/3, Fisher's exact test p < 0.05). Nerve ultrasound has additional value not only for diagnosis, but also for classification of disease state and may predict treatment response.

Performance of immunological response in predicting virological failure.

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Ingole, Nayana; Mehta, Preeti; Pazare, Amar; Paranjpe, Supriya; Sarkate, Purva

2013-03-01

In HIV-infected individuals on antiretroviral therapy (ART), the decision on when to switch from first-line to second-line therapy is dictated by treatment failure, and this can be measured in three ways: clinically, immunologically, and virologically. While viral load (VL) decreases and CD4 cell increases typically occur together after starting ART, discordant responses may be seen. Hence the current study was designed to determine the immunological and virological response to ART and to evaluate the utility of immunological response to predict virological failure. All treatment-naive HIV-positive individuals aged >18 years who were eligible for ART were enrolled and assessed at baseline, 6 months, and 12 months clinically and by CD4 cell count and viral load estimations. The patients were categorized as showing concordant favorable (CF), immunological only (IO), virological only (VO), and concordant unfavorable responses (CU). The efficiency of immunological failure to predict virological failure was analyzed across various levels of virological failure (VL>50, >500, and >5,000 copies/ml). At 6 months, 87(79.81%), 7(5.5%), 13 (11.92%), and 2 (1.83%) patients and at 12 months 61(69.3%), 9(10.2%), 16 (18.2%), and 2 (2.3%) patients had CF, IO, VO, and CU responses, respectively. Immunological failure criteria had a very low sensitivity (11.1-40%) and positive predictive value (8.3-25%) to predict virological failure. Immunological criteria do not accurately predict virological failure resulting in significant misclassification of therapeutic responses. There is an urgent need for inclusion of viral load testing in the initiation and monitoring of ART.

Association of Elevated Reward Prediction Error Response With Weight Gain in Adolescent Anorexia Nervosa.

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DeGuzman, Marisa; Shott, Megan E; Yang, Tony T; Riederer, Justin; Frank, Guido K W

2017-06-01

Anorexia nervosa is a psychiatric disorder of unknown etiology. Understanding associations between behavior and neurobiology is important in treatment development. Using a novel monetary reward task during functional magnetic resonance brain imaging, the authors tested how brain reward learning in adolescent anorexia nervosa changes with weight restoration. Female adolescents with anorexia nervosa (N=21; mean age, 16.4 years [SD=1.9]) underwent functional MRI (fMRI) before and after treatment; similarly, healthy female control adolescents (N=21; mean age, 15.2 years [SD=2.4]) underwent fMRI on two occasions. Brain function was tested using the reward prediction error construct, a computational model for reward receipt and omission related to motivation and neural dopamine responsiveness. Compared with the control group, the anorexia nervosa group exhibited greater brain response 1) for prediction error regression within the caudate, ventral caudate/nucleus accumbens, and anterior and posterior insula, 2) to unexpected reward receipt in the anterior and posterior insula, and 3) to unexpected reward omission in the caudate body. Prediction error and unexpected reward omission response tended to normalize with treatment, while unexpected reward receipt response remained significantly elevated. Greater caudate prediction error response when underweight was associated with lower weight gain during treatment. Punishment sensitivity correlated positively with ventral caudate prediction error response. Reward system responsiveness is elevated in adolescent anorexia nervosa when underweight and after weight restoration. Heightened prediction error activity in brain reward regions may represent a phenotype of adolescent anorexia nervosa that does not respond well to treatment. Prediction error response could be a neurobiological marker of illness severity that can indicate individual treatment needs.

Potential Impact of a Free Online HIV Treatment Response Prediction System for Reducing Virological Failures and Drug Costs after Antiretroviral Therapy Failure in a Resource-Limited Setting

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Andrew D. Revell

2013-01-01

Full Text Available Objective. Antiretroviral drug selection in resource-limited settings is often dictated by strict protocols as part of a public health strategy. The objective of this retrospective study was to examine if the HIV-TRePS online treatment prediction tool could help reduce treatment failure and drug costs in such settings. Methods. The HIV-TRePS computational models were used to predict the probability of response to therapy for 206 cases of treatment change following failure in India. The models were used to identify alternative locally available 3-drug regimens, which were predicted to be effective. The costs of these regimens were compared to those actually used in the clinic. Results. The models predicted the responses to treatment of the cases with an accuracy of 0.64. The models identified alternative drug regimens that were predicted to result in improved virological response and lower costs than those used in the clinic in 85% of the cases. The average annual cost saving was $364 USD per year (41%. Conclusions. Computational models that do not require a genotype can predict and potentially avoid treatment failure and may reduce therapy costs. The use of such a system to guide therapeutic decision-making could confer health economic benefits in resource-limited settings.

EEG does not predict response to valproate treatment of aggression in patients with borderline and antisocial personality disorders.

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Reeves, Roy R; Struve, Frederick A; Patrick, Gloria

2003-04-01

Previous investigations of the role of EEG in predicting response of aggressive patients to valproate therapy have yielded mixed results. In this study of borderline and antisocial personality disorder patients hospitalized with aggressive behavior, EEGs were obtained prior to treatment with valproate. Eight of 22 (36.4%) patients subsequently responsive to valproate had nonepileptiform EEG abnormalities, while 5 of 20 (25%) patients not responsive to valproate had nonepileptiform EEG abnormalities. Although more of the valproate responders than nonresponders had EEG abnormalities, the presence of nonepileptiform EEG abnormalities was not a statistically significant (X2 = 0.213, df = 1, p = 0.64) predictor of valproate response in personality disorder patients with aggression.

Drug response prediction in high-risk multiple myeloma

DEFF Research Database (Denmark)

Vangsted, A J; Helm-Petersen, S; Cowland, J B

2018-01-01

from high-risk patients by GEP70 at diagnosis from Total Therapy 2 and 3A to predict the response by the DRP score of drugs used in the treatment of myeloma patients. The DRP score stratified patients further. High-risk myeloma with a predicted sensitivity to melphalan by the DRP score had a prolonged...

Application of PET/CT in treatment response evaluation and recurrence prediction in patients with newly-diagnosed multiple myeloma.

Science.gov (United States)

Li, Ying; Liu, Junru; Huang, Beihui; Chen, Meilan; Diao, Xiangwen; Li, Juan

2017-04-11

Multiple myeloma (MM) causes osteolytic lesions which can be detected by 18F-fluorodeoxyglucose positron emission tomography/Computed tomography (18F-FDG PET/CT). We prospectively involve 96 Newly diagnosed MM to take PET/CT scan at scheduled treatment time (figure 1), and 18F-FDG uptake of lesion was measured by SUVmax and T/Mmax. All MM patients took bortezomib based chemotherapy as induction and received ASCT and maintenance. All clinical features were analyzed with the PET/CT image changes, and some relationships between treatment response and FDG uptakes changes were found: Osteolytic lesions of MM uptakes higher FDG than healthy volunteers, and this trend is more obvious in extramedullary lesions. Compared to X-ray, PET/CT was more sensitive both in discoering bone as well as extramedullary lesions. In newly diagnosed MM, several adverse clinical factors were related to high FDG uptakes of bone lesions. Bone lesion FDG uptakes of MM with P53 mutation or with hypodiploidy and complex karyotype were also higher than those without such changes. In treatment response, PET/CT showed higher sensitivity in detecting tumor residual disease than immunofixation electrophoresis. But in relapse prediction, it might show false positive disease recurrences and the imaging changes might be influenced by infections and hemoglobulin levels. PET/CT is sensitive in discovering meduallary and extrameduallary lesions of MM, and the 18F-FDG uptake of lesions are related with clinical indictors and biological features of plasma cells. In evaluating treatment response and survival, PET/CT showed its superiority. But in predicting relapse or refractory, it may show false positive results.

Mood Predicts Response to Placebo CPAP

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Carl J. Stepnowsky

2012-01-01

Full Text Available Study Objectives. Continuous positive airway pressure (CPAP therapy is efficacious for treating obstructive sleep apnea (OSA, but recent studies with placebo CPAP (CPAP administered at subtherapeutic pressure have revealed nonspecific (or placebo responses to CPAP treatment. This study examined baseline psychological factors associated with beneficial effects from placebo CPAP treatment. Participants. Twenty-five participants were studied with polysomnography at baseline and after treatment with placebo CPAP. Design. Participants were randomized to either CPAP treatment or placebo CPAP. Baseline mood was assessed with the Profile of Mood States (POMS. Total mood disturbance (POMS-Total was obtained by summing the six POMS subscale scores, with Vigor weighted negatively. The dependent variable was changed in apnea-hypopnea index (ΔAHI, calculated by subtracting pre- from post-CPAP AHI. Negative values implied improvement. Hierarchical regression analysis was performed, with pre-CPAP AHI added as a covariate to control for baseline OSA severity. Results. Baseline emotional distress predicted the drop in AHI in response to placebo CPAP. Highly distressed patients showed greater placebo response, with a 34% drop (i.e., improvement in AHI. Conclusion. These findings underscore the importance of placebo-controlled studies of CPAP treatment. Whereas such trials are routinely included in drug trials, this paper argues for their importance even in mechanical-oriented sleep interventions.

HER family kinase domain mutations promote tumor progression and can predict response to treatment in human breast cancer

KAUST Repository

Boulbes, Delphine R.; Arold, Stefan T.; Chauhan, Gaurav B.; Blachno, Korina V.; Deng, Nanfu; Chang, Wei-Chao; Jin, Quanri; Huang, Tzu-Hsuan; Hsu, Jung-Mao; Brady, Samuel W.; Bartholomeusz, Chandra; Ladbury, John E.; Stone, Steve; Yu, Dihua; Hung, Mien-Chie; Esteva, Francisco J.

2014-01-01

Resistance to HER2-targeted therapies remains a major obstacle in the treatment of HER2-overexpressing breast cancer. Understanding the molecular pathways that contribute to the development of drug resistance is needed to improve the clinical utility of novel agents, and to predict the success of targeted personalized therapy based on tumor-specific mutations. Little is known about the clinical significance of HER family mutations in breast cancer. Because mutations within HER1/EGFR are predictive of response to tyrosine kinase inhibitors (TKI) in lung cancer, we investigated whether mutations in HER family kinase domains are predictive of response to targeted therapy in HER2-overexpressing breast cancer. We sequenced the HER family kinase domains from 76 HER2-overexpressing invasive carcinomas and identified 12 missense variants. Patients whose tumors carried any of these mutations did not respond to HER2 directed therapy in the metastatic setting. We developed mutant cell lines and used structural analyses to determine whether changes in protein conformation could explain the lack of response to therapy. We also functionally studied all HER2 mutants and showed that they conferred an aggressive phenotype and altered effects of the TKI lapatinib. Our data demonstrate that mutations in the finely tuned HER kinase domains play a critical function in breast cancer progression and may serve as prognostic and predictive markers.

HER family kinase domain mutations promote tumor progression and can predict response to treatment in human breast cancer

KAUST Repository

Boulbes, Delphine R.

2014-11-11

Resistance to HER2-targeted therapies remains a major obstacle in the treatment of HER2-overexpressing breast cancer. Understanding the molecular pathways that contribute to the development of drug resistance is needed to improve the clinical utility of novel agents, and to predict the success of targeted personalized therapy based on tumor-specific mutations. Little is known about the clinical significance of HER family mutations in breast cancer. Because mutations within HER1/EGFR are predictive of response to tyrosine kinase inhibitors (TKI) in lung cancer, we investigated whether mutations in HER family kinase domains are predictive of response to targeted therapy in HER2-overexpressing breast cancer. We sequenced the HER family kinase domains from 76 HER2-overexpressing invasive carcinomas and identified 12 missense variants. Patients whose tumors carried any of these mutations did not respond to HER2 directed therapy in the metastatic setting. We developed mutant cell lines and used structural analyses to determine whether changes in protein conformation could explain the lack of response to therapy. We also functionally studied all HER2 mutants and showed that they conferred an aggressive phenotype and altered effects of the TKI lapatinib. Our data demonstrate that mutations in the finely tuned HER kinase domains play a critical function in breast cancer progression and may serve as prognostic and predictive markers.

The accurate definition of metabolic volumes on 18F-FDG-PET before treatment allows the response to chemoradiotherapy to be predicted in the case of oesophagus cancers

International Nuclear Information System (INIS)

Hatt, M.; Cheze-Le Rest, C.; Visvikis, D.; Pradier, O.

2011-01-01

This study aims at assessing the possibility of prediction of the response of locally advanced oesophagus cancers, even before the beginning of treatment, by using metabolic volume measurements performed on 18 F-FDG PET images made before the treatment. Medical files of 50 patients have been analyzed. According to the observed responses, and to metabolic volume and Total Lesion Glycosis (TLG) values, it appears that the images allow the extraction of parameters, such as the TLG, which are criteria for the prediction of the therapeutic response. Short communication

Prediction of lung density changes after radiotherapy by cone beam computed tomography response markers and pre-treatment factors for non-small cell lung cancer patients.

Science.gov (United States)

Bernchou, Uffe; Hansen, Olfred; Schytte, Tine; Bertelsen, Anders; Hope, Andrew; Moseley, Douglas; Brink, Carsten

2015-10-01

This study investigates the ability of pre-treatment factors and response markers extracted from standard cone-beam computed tomography (CBCT) images to predict the lung density changes induced by radiotherapy for non-small cell lung cancer (NSCLC) patients. Density changes in follow-up computed tomography scans were evaluated for 135 NSCLC patients treated with radiotherapy. Early response markers were obtained by analysing changes in lung density in CBCT images acquired during the treatment course. The ability of pre-treatment factors and CBCT markers to predict lung density changes induced by radiotherapy was investigated. Age and CBCT markers extracted at 10th, 20th, and 30th treatment fraction significantly predicted lung density changes in a multivariable analysis, and a set of response models based on these parameters were established. The correlation coefficient for the models was 0.35, 0.35, and 0.39, when based on the markers obtained at the 10th, 20th, and 30th fraction, respectively. The study indicates that younger patients without lung tissue reactions early into their treatment course may have minimal radiation induced lung density increase at follow-up. Further investigations are needed to examine the ability of the models to identify patients with low risk of symptomatic toxicity. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Early 18F-FDG-PET/CT as a predictive marker for treatment response and survival in patients with metastatic colorectal cancer treated with irinotecan and cetuximab

DEFF Research Database (Denmark)

Skougaard, K; Nielsen, Dorte; Vittrup Jensen, Benny

2016-01-01

to RECIST 1.0. Results: By EORTC criteria, early metabolic response predicted partial metabolic response (PMR) with a high positive predictive value (PPV) of 0.875 and a high negative predictive value (NPV) of 0.714. Partial radiologic response was predicted with a low PPV of 0.368 but a high NPV of 1.......0. By PERCIST, PMR was predicted with a high PPV of 0.826 and an intermediate NPV of 0.667 and partial radiologic response was predicted with a low PPV of 0.5 but a high NPV of 1.0. Median OS was nearly the same with the two criteria sets; 14.1 months for early metabolic responders and 9.9 months for non......-responders using EORTC criteria and 13.5 and 10.1 months, respectively, using PERCIST. Conclusions: With both EORTC criteria and PERCIST, early reduction in FDG uptake was predictive of a later partial metabolic and partial radiologic response to treatment. It was also predictive of significantly longer survival...

Caregiver Responsiveness to the Family Bereavement Program: What predicts responsiveness? What does responsiveness predict?

OpenAIRE

Schoenfelder, Erin N.; Sandler, Irwin N.; Millsap, Roger E.; Wolchik, Sharlene A.; Berkel, Cady; Ayers, Timothy S.

2013-01-01

The study developed a multi-dimensional measure to assess participant responsiveness to a preventive intervention, and applied this measure to study how participant baseline characteristics predict responsiveness and how responsiveness predicts program outcomes. The study was conducted with caregivers who participated in the parenting-focused component of the Family Bereavement Program (FBP), a prevention program for families that have experienced parental death. The sample consisted of 89 ca...

Startle and spider phobia : Unilateral probes and the prediction of treatment effects

NARCIS (Netherlands)

de Jong, Peter J.; Visser, Sylvia; Merckelbach, Harald

1996-01-01

The present study explored two issues: (1) the predictive value of startle responses for treatment success and (2) the lateralization of affect-modulated startle responses. Approximately 40 days before behavioral treatment, monaural startle probes were presented to 20 women who were spider phobic

The predictive value of baseline NAA/Cr for treatment response of first-episode schizophrenia: A ¹H MRS study.

Science.gov (United States)

Liu, Weibo; Yu, Hualiang; Jiang, Biao; Pan, Bing; Yu, Shaohua; Li, Huichun; Zheng, Leilei

2015-07-23

The study focused on the predictive value of baseline metabolite ratios in bilateral hippocampus of first-episode schizophrenia by using proton magnetic resonance spectroscopy ((1)H MRS). (1)H MRS data were acquired from 23 hallucination and 17 non-hallucination first-episode schizophrenia patients compared with 17 healthy participants. Clinical characteristics of patients were rated using the Positive and Negative Syndrome Scale (PANSS) before and after 3-month treatment. The schizophrenia patients showed lower NAA/Cr ratio than healthy participants respectively (p=0.024; p=0.001), and non-hallucination patients had even lower NAA/Cr ratio than hallucination patients (p=0.033). After 3-month treatment, hallucination patients had greater improvement in negative symptoms than non-hallucination patients (p=0.018). The reduction of PANSS total score and negative factor score was positively correlated with the left NAA/Cr in both group patients (pNAA/Cr had predictive value for the whole treatment response, and the left hippocampal NAA/Cr can predict the prognosis of negative symptoms during acute phase medication in first-episode schizophrenia. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Personality traits predict treatment outcome with an antidepressant in patients with functional gastrointestinal disorder.

Science.gov (United States)

Tanum, L; Malt, U F

2000-09-01

We investigated the relationship between personality traits and response to treatment with the tetracyclic antidepressant mianserin or placebo in patients with functional gastrointestinal disorder (FGD) without psychopathology. Forty-eight patients completed the Buss-Durkee Hostility Inventory, Neuroticism Extroversion Openness -Personality Inventory (NEO-PI), and Eysenck Personality Questionnaire (EPQ), neuroticism + lie subscales, before they were consecutively allocated to a 7-week double-blind treatment study with mianserin or placebo. Treatment response to pain and target symptoms were recorded daily with the Visual Analogue Scale and Clinical Global Improvement Scale at every visit. A low level of neuroticism and little concealed aggressiveness predicted treatment outcome with the antidepressant drug mianserin in non-psychiatric patients with FGD. Inversely, moderate to high neuroticism and marked concealed aggressiveness predicted poor response to treatment. These findings were most prominent in women. Personality traits were better predictors of treatment outcome than serotonergic sensitivity assessed with the fenfluramine test. Assessment of the personality traits negativism, irritability, aggression, and neuroticism may predict response to drug treatment of FGD even when serotonergic sensitivity is controlled for. If confirmed in future studies, the findings point towards a more differential psychopharmacologic treatment of FGD.

Poor Response to Periodontal Treatment May Predict Future Cardiovascular Disease.

Science.gov (United States)

Holmlund, A; Lampa, E; Lind, L

2017-07-01

Periodontal disease has been associated with cardiovascular disease (CVD), but whether the response to the treatment of periodontal disease affects this association has not been investigated in any large prospective study. Periodontal data obtained at baseline and 1 y after treatment were available in 5,297 individuals with remaining teeth who were treated at a specialized clinic for periodontal disease. Poor response to treatment was defined as having >10% sites with probing pocket depth >4 mm deep and bleeding on probing at ≥20% of the sites 1 y after active treatment. Fatal/nonfatal incidence rate of CVD (composite end point of myocardial infarction, stroke, and heart failure) was obtained from the Swedish cause-of-death and hospital discharge registers. Poisson regression analysis was performed to analyze future risk of CVD. During a median follow-up of 16.8 y (89,719 person-years at risk), those individuals who did not respond well to treatment (13.8% of the sample) had an increased incidence of CVD ( n = 870) when compared with responders (23.6 vs. 15.3%, P 4 mm, and number of teeth, the incidence rate ratio for CVD among poor responders was 1.28 (95% CI, 1.07 to 1.53; P = 0.007) as opposed to good responders. The incidence rate ratio among poor responders increased to 1.39 (95% CI, 1.13 to 1.73; P = 0.002) for those with the most remaining teeth. Individuals who did not respond well to periodontal treatment had an increased risk for future CVD, indicating that successful periodontal treatment might influence progression of subclinical CVD.

Predictive Treatment Management: Incorporating a Predictive Tumor Response Model Into Robust Prospective Treatment Planning for Non-Small Cell Lung Cancer

Energy Technology Data Exchange (ETDEWEB)

Zhang, Pengpeng, E-mail: zhangp@mskcc.org [Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York (United States); Yorke, Ellen; Hu, Yu-Chi; Mageras, Gig [Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York (United States); Rimner, Andreas [Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, New York (United States); Deasy, Joseph O. [Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York (United States)

2014-02-01

Purpose: We hypothesized that a treatment planning technique that incorporates predicted lung tumor regression into optimization, predictive treatment planning (PTP), could allow dose escalation to the residual tumor while maintaining coverage of the initial target without increasing dose to surrounding organs at risk (OARs). Methods and Materials: We created a model to estimate the geometric presence of residual tumors after radiation therapy using planning computed tomography (CT) and weekly cone beam CT scans of 5 lung cancer patients. For planning purposes, we modeled the dynamic process of tumor shrinkage by morphing the original planning target volume (PTV{sub orig}) in 3 equispaced steps to the predicted residue (PTV{sub pred}). Patients were treated with a uniform prescription dose to PTV{sub orig}. By contrast, PTP optimization started with the same prescription dose to PTV{sub orig} but linearly increased the dose at each step, until reaching the highest dose achievable to PTV{sub pred} consistent with OAR limits. This method is compared with midcourse adaptive replanning. Results: Initial parenchymal gross tumor volume (GTV) ranged from 3.6 to 186.5 cm{sup 3}. On average, the primary GTV and PTV decreased by 39% and 27%, respectively, at the end of treatment. The PTP approach gave PTV{sub orig} at least the prescription dose, and it increased the mean dose of the true residual tumor by an average of 6.0 Gy above the adaptive approach. Conclusions: PTP, incorporating a tumor regression model from the start, represents a new approach to increase tumor dose without increasing toxicities, and reduce clinical workload compared with the adaptive approach, although model verification using per-patient midcourse imaging would be prudent.

Predictive value of brain perfusion SPECT for ketamine response in hyperalgesic fibromyalgia

Energy Technology Data Exchange (ETDEWEB)

Guedj, Eric; Cammilleri, Serge; Colavolpe, Cecile; Taieb, David; Laforte, Catherine de; Mundler, Olivier [Centre Hospitalo-Universitaire de la Timone, Service Central de Biophysique et de Medecine Nucleaire, Assistance Publique des Hopitaux de Marseille, Marseille Cedex 5 (France); Niboyet, Jean [Clinique La Phoceanne, Unite d' Etude et de Traitement de la Douleur, Marseille (France)

2007-08-15

Ketamine has been used successfully in various proportions of fibromyalgia (FM) patients. However, the response to this specific treatment remains largely unpredictable. We evaluated brain SPECT perfusion before treatment with ketamine, using voxel-based analysis. The objective was to determine the predictive value of brain SPECT for ketamine response. Seventeen women with FM (48 {+-} 11 years; ACR criteria) were enrolled in the study. Brain SPECT was performed before any change was made in therapy in the pain care unit. We considered that a patient was a good responder to ketamine if the VAS score for pain decreased by at least 50% after treatment. A voxel-by-voxel group analysis was performed using SPM2, in comparison to a group of ten healthy women matched for age. The VAS score for pain was 81.8 {+-} 4.2 before ketamine and 31.8 {+-} 27.1 after ketamine. Eleven patients were considered ''good responders'' to ketamine. Responder and non-responder subgroups were similar in terms of pain intensity before ketamine. In comparison to responding patients and healthy subjects, non-responding patients exhibited a significant reduction in bilateral perfusion of the medial frontal gyrus. This cluster of hypoperfusion was highly predictive of non-response to ketamine (positive predictive value 100%, negative predictive value 91%). Brain perfusion SPECT may predict response to ketamine in hyperalgesic FM patients. (orig.)

Predictive value of brain perfusion SPECT for ketamine response in hyperalgesic fibromyalgia

International Nuclear Information System (INIS)

Guedj, Eric; Cammilleri, Serge; Colavolpe, Cecile; Taieb, David; Laforte, Catherine de; Mundler, Olivier; Niboyet, Jean

2007-01-01

Ketamine has been used successfully in various proportions of fibromyalgia (FM) patients. However, the response to this specific treatment remains largely unpredictable. We evaluated brain SPECT perfusion before treatment with ketamine, using voxel-based analysis. The objective was to determine the predictive value of brain SPECT for ketamine response. Seventeen women with FM (48 ± 11 years; ACR criteria) were enrolled in the study. Brain SPECT was performed before any change was made in therapy in the pain care unit. We considered that a patient was a good responder to ketamine if the VAS score for pain decreased by at least 50% after treatment. A voxel-by-voxel group analysis was performed using SPM2, in comparison to a group of ten healthy women matched for age. The VAS score for pain was 81.8 ± 4.2 before ketamine and 31.8 ± 27.1 after ketamine. Eleven patients were considered ''good responders'' to ketamine. Responder and non-responder subgroups were similar in terms of pain intensity before ketamine. In comparison to responding patients and healthy subjects, non-responding patients exhibited a significant reduction in bilateral perfusion of the medial frontal gyrus. This cluster of hypoperfusion was highly predictive of non-response to ketamine (positive predictive value 100%, negative predictive value 91%). Brain perfusion SPECT may predict response to ketamine in hyperalgesic FM patients. (orig.)

The value of (18) F-fluorodeoxyglucose positron emission tomography for prediction of treatment response in gastrointestinal stromal tumors: a systematic review and meta-analysis.

Science.gov (United States)

Hassanzadeh-Rad, Arman; Yousefifard, Mahmoud; Katal, Sanaz; Asady, Hadi; Fard-Esfahani, Armaghan; Moghadas Jafari, Ali; Hosseini, Mostafa

2016-05-01

Early detection of response to treatment is critically important in gastrointestinal stromal tumors (GIST). Therefore, the present systematic review and meta-analysis assessed the value of (18) f-fluorodeoxyglucose positron emission tomography ((18) FDG-PET) on prediction of therapeutic response of GIST patients to systemic treatments. The literature search was conducted using PubMed, SCOPUS, Cochrane, and Google Scholar databases, and review article references. Eligible articles were defined as studies included confirmed GIST patients who underwent (18) FDG-PET as well as assessing the screening role of it. Finally, 21 relevant articles were included. The analysis showed the pooled sensitivity and specificity of 18FDG-PET in evaluation of response to treatment of GIST patient were 0.90 (95% CI: 0.85-0.94; I(2)  = 52.59, P = 0.001) and 0.62 (95% CI: 0.49-0.75; I(2)  = 69.7, P = 0.001), respectively. In addition, the pooled prognostic odds ratio of (18) FDG-PET for was 14.99 (95% CI, 6.42-34.99; I(2)  = 100.0, P present meta-analysis showed (18) FDG-PET has a significant value in predicting treatment response in GIST patients. © 2015 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Motivation for change as a predictor of treatment response for dysthymia.

Science.gov (United States)

Frías Ibáñez, Álvaro; González Vallespí, Laura; Palma Sevillano, Carol; Farriols Hernando, Núria

2016-05-01

Dysthymia constitutes a chronic, mild affective disorder characterized by heterogeneous treatment effects. Several predictors of clinical response and attendance have been postulated, although research on the role of the psychological variables involved in this mental disorder is still scarce. Fifty-four adult patients, who met criteria for dysthymia completed an ongoing naturalistic treatment based on the brief interpersonal psychotherapy (IPT-B), which was delivered bimonthly over 16 months. As potential predictor variables, the therapeutic alliance, coping strategies, perceived self-efficacy, and motivation for change were measured at baseline. Outcome variables were response to treatment (Clinical Global Impression and Beck’s Depression Inventory) and treatment attendance. Stepwise multiple linear regression analyses revealed that higher motivation for change predicted better response to treatment. Moreover, higher motivation for change also predicted treatment attendance. Therapeutic alliance was not a predictor variable of neither clinical response nor treatment attendance. These preliminary findings support the adjunctive use of motivational interviewing (MI) techniques in the treatment of dysthymia. Further research with larger sample size and follow-up assessment is warranted.

Predicting the In-Hospital Responsiveness to Treatment of Alcoholics. Social Factors as Predictors of Outcome. Brain Damage as a Factor in Treatment Outcome of Chronic Alcoholic Patients.

Science.gov (United States)

Mascia, George V.; And Others

The authors attempt to locate predictor variables associated with the outcome of alcoholic treatment programs. Muscia's study focuses on the predictive potential of: (1) response to a GSR conditioning procedure; (2) several personality variables; and (3) age and IQ measures. Nine variables, reflecting diverse perspectives, were selected as a basis…

Transferrin Level Before Treatment and Genetic Polymorphism in HFE Gene as Predictive Markers for Response to Adalimumab in Crohn's Disease Patients.

Science.gov (United States)

Repnik, Katja; Koder, Silvo; Skok, Pavel; Ferkolj, Ivan; Potočnik, Uroš

2016-08-01

Tumor necrosis factor α inhibitors (anti-TNF) have improved treatment of several complex diseases, including Crohn's disease (CD). However, the effect varies and approximately one-third of the patients do not respond. Since blood parameters as well as genetic factors have shown a great potential to predict response during treatment, the aim of the study was to evaluate response to anti-TNF treatment with adalimumab (ADA) between genes HFE and TF and haematological parameters in Slovenian refractory CD patients. Single nucleotide polymorphisms (SNPs) rs1799852 in gene TF and rs2071303 in gene HFE were genotyped in 68 refractory CD patients for which response has been measured using inflammatory bowel disease questionnaire (IBDQ) index. Haematological parameters and IBDQ index were determined before therapy and after 4, 12, 20 and 30 weeks. We found novel strong association between SNP rs2071303 in gene HFE and response to ADA treatment, particularly patients with G allele comparing to A allele had better response after 20 weeks (p = 0.008). Further, we found strong association between transferrin level at baseline and treatment response after 12, 20 and 30 weeks, where average transferrin level before therapy was lower in responders (2.38 g/L) compared to non-responders (2.89 g/L, p = 0.005). Association was found between transferrin level in week 30 and SNP rs1799852 (p = 0.023), and between MCHC level before treatment and SNP rs2071303 (p = 0.007). Our results suggest that SNP in gene HFE as well as haematological markers serve as promising prognostic markers of response to anti-TNF treatment in CD patients.

Neoadjuvant chemotherapy in breast cancer-response evaluation and prediction of response to treatment using dynamic contrast-enhanced and diffusion-weighted MR imaging

International Nuclear Information System (INIS)

Fangberget, A.; Holmen, M.M.; Nilsen, L.B.; Hole, K.H.; Engebraaten, O.; Naume, B.; Smith, H.J.; Olsen, D.R.; Seierstad, T.

2011-01-01

To explore the predictive value of MRI parameters and tumour characteristics before neoadjuvant chemotherapy (NAC) and to compare changes in tumour size and tumour apparent diffusion coefficient (ADC) during treatment, between patients who achieved pathological complete response (pCR) and those who did not. Approval by the Regional Ethics Committee and written informed consent were obtained. Thirty-one patients with invasive breast carcinoma scheduled for NAC were enrolled (mean age, 50.7; range, 37-72). Study design included MRI before treatment (Tp0), after four cycles of NAC (Tp1) and before surgery (Tp2). Data in pCR versus non-pCR groups were compared and cut-off values for pCR prediction were evaluated. Before NAC, HER2 overexpression was the single significant predictor of pCR (p = 0.006). At Tp1 ADC, tumour size and changes in tumour size were all significantly different in the pCR and non-pCR groups. Using 1.42 x 10 -3 mm 2 /s as the cut-off value for ADC, pCR was predicted with sensitivity and specificity of 88% and 80%, respectively. Using a cut-off value of 83% for tumour volume reduction, sensitivity and specificity for pCR were 91% and 80%. ADC, tumour size and tumour size reduction at Tp1 were strong independent predictors of pCR. (orig.)

Use of molecular markers for predicting therapy response in cancer patients.

LENUS (Irish Health Repository)

Duffy, Michael J

2012-02-01

Predictive markers are factors that are associated with upfront response or resistance to a particular therapy. Predictive markers are important in oncology as tumors of the same tissue of origin vary widely in their response to most available systemic therapies. Currently recommended oncological predictive markers include both estrogen and progesterone receptors for identifying patients with breast cancers likely to benefit from hormone therapy, HER-2 for the identification of breast cancer patients likely to benefit from trastuzumab, specific K-RAS mutations for the identification of patients with advanced colorectal cancer unlikely to benefit from either cetuximab or panitumumab and specific EGFR mutations for selecting patients with advanced non-small-cell lung cancer for treatment with tyrosine kinase inhibitors such as gefitinib and erlotinib. The availability of predictive markers should increase drug efficacy and decrease toxicity, thus leading to a more personalized approach to cancer treatment.

Angiotensinogen and HLA class II predict bevacizumab response in recurrent glioblastoma patients

DEFF Research Database (Denmark)

Urup, Thomas; Michaelsen, Signe Regner; Olsen, Lars Rønn

2016-01-01

Background: Bevacizumab combination therapy is among the most frequently used treatments in recurrent glioblastoma and patients who achieve response to bevacizumab have improved survival as well as quality of life. Accordingly, the aim of this study was to identify predictive biomarkers for bevac......Background: Bevacizumab combination therapy is among the most frequently used treatments in recurrent glioblastoma and patients who achieve response to bevacizumab have improved survival as well as quality of life. Accordingly, the aim of this study was to identify predictive biomarkers...... for bevacizumab response in recurrent glioblastoma patients. Methods: The study included a total of 82 recurrent glioblastoma patients treated with bevacizumab combination therapy whom were both response and biomarker evaluable. Gene expression of tumor tissue was analyzed by using a customized Nano...

One-week acid suppression trial in uninvestigated dyspepsia patients with epigastric pain or burning to predict response to 8 weeks' treatment with esomeprazole

DEFF Research Database (Denmark)

van Zanten, S V; Flook, N; Talley, N J

2007-01-01

BACKGROUND: While empiric acid-suppressive therapy for uninvestigated dyspepsia patients with symptoms of epigastric pain or burning is standard practice, it is unknown whether an early response to therapy predicts outcome. AIM: To evaluate whether a 1-w acid suppression trial is effective...... for predicting 8-w response in such patients. METHODS: Helicobacter pylori-negative patients (aged 18-50 years) in primary care with uninvestigated epigastric pain or burning were randomized to esomeprazole 40 mg q.d.s. or b.d. for 1w, followed by esomeprazole 40 mg q.d.s. or placebo for 7w. Each day, patients......, respectively, and 47% (339 of 716) and 34% (124 of 368), respectively, at 8w (both P treatment were 58% and 70%, respectively, at 8w. CONCLUSION: A 1-w acid suppression trial is of limited clinical value for predicting 8-w response...

Clinical utility of pretreatment prediction of chemoradiotherapy response in rectal cancer: a review.

Science.gov (United States)

Yoo, Byong Chul; Yeo, Seung-Gu

2017-03-01

Approximately 20% of all patients with locally advanced rectal cancer experience pathologically complete responses following neoadjuvant chemoradiotherapy (CRT) and standard surgery. The utility of radical surgery for patients exhibiting good CRT responses has been challenged. Organ-sparing strategies for selected patients exhibiting complete clinical responses include local excision or no immediate surgery. The subjects of this tailored management are patients whose presenting disease corresponds to current indications of neoadjuvant CRT, and their post-CRT tumor response is assessed by clinical and radiological examinations. However, a model predictive of the CRT response, applied before any treatment commenced, would be valuable to facilitate such a personalized approach. This would increase organ preservation, particularly in patients for whom upfront CRT is not generally prescribed. Molecular biomarkers hold the greatest promise for development of a pretreatment predictive model of CRT response. A combination of clinicopathological, radiological, and molecular markers will be necessary to render the model robust. Molecular research will also contribute to the development of drugs that can overcome the radioresistance of rectal tumors. Current treatments for rectal cancer are based on the expected prognosis given the presenting disease extent. In the future, treatment schemes may be modified by including the predicted CRT response evaluated at presentation.

Predictors of response to neuroleptic treatment in schizophrenia.

Science.gov (United States)

Stern, R G; Kahn, R S; Davidson, M

1993-06-01

Baseline symptom severity, early reduction in symptom severity, initial subjective response to neuroleptic treatment, the degree of brain atrophy, and early changes in pHVA levels appear to predict treatment outcome in schizophrenic patients. Computerized EEG results, neuropsychological and neurophysiologic tests, and baseline pHVA concentrations require further examination. Only a limited proportion of variance in treatment response, however, could be explained by either of the nine predictors alone or combined. Therefore, further research is necessary to discover yet unidentified determinants of treatment response. Future studies should test the validity and reliability of these five promising predictors in large groups of male and female patients, employ high standards for assessment reliability of clinical parameters, and use absolute rating scores on psychopathology as well as functional scales for the definition of good and poor treatment response. Furthermore, the statistical approach for data analysis should take in consideration the need for appropriate corrections when multiple correlations are performed and should test the extent to which these predictors are interdependent.

Can a physician predict the clinical response to first-line immunomodulatory treatment in relapsing–remitting multiple sclerosis?

Directory of Open Access Journals (Sweden)

Mezei Z

2012-10-01

Full Text Available Zsolt Mezei,1 Daniel Bereczki,1,2 Lilla Racz,1 Laszlo Csiba,1 Tunde Csepany11Department of Neurology, University of Debrecen, Debrecen, Hungary; 2Department of Neurology, Semmelweis University, Budapest, HungaryBackground: Decreased relapse rate and slower disease progression have been reported with long-term use of immunomodulatory treatments (IMTs, interferon beta or glatiramer acetate in relapsing–remitting multiple sclerosis. There are, however, patients who do not respond to such treatments, and they can be potential candidates for alternative therapeutic approaches.Objective: To identify clinical factors as possible predictors of poor long-term response.Methods: A 9-year prospective, continuous follow-up at a single center in Hungary to assess clinical efficacy of IMT.Results: In a patient group of 81 subjects with mean IMT duration of 54 ± 33 months, treatment efficacy expressed as annual relapse rate and change in clinical severity from baseline did not depend on the specific IMT (any of the interferon betas or glatiramer acetate, and on mono- or multifocal features of the initial appearance of the disease. Responders had shorter disease duration and milder clinical signs at the initiation of treatment. Relapse-rate reduction in the initial 2 years of treatment predicted clinical efficacy in subsequent years.Conclusion: Based on these observations, we suggest that a 2-year trial period is sufficient to decide on the efficacy of a specific IMT. For those with insufficient relapse reduction in the first 2 years of treatment, a different IMT or other therapeutic approaches should be recommended.Keywords: multiple sclerosis, immunomodulatory, EDSS, relapse, response

Characteristics of MRI features in Alzheimer's disease patients predicting response to donepezil treatment

International Nuclear Information System (INIS)

Tanaka, Yuriko; Hanyu, Haruo; Sakurai, Hirofumi; Shimizu, Souichiro; Takasaki, Masaru

2003-01-01

We attempted to investigate whether morphological features as shown on magnetic resonance imaging (MRI) predict response to donepezil treatment in patients with Alzheimer's disease (AD). Sixty-three patients with AD were divided into responders (n=16) and non-responders (n=47) based on the changes in the mini mental state examinations (MMSE) score between baseline and endpoint. Atrophy of the substantia innominata was more pronounced in responders than non-responders. Although no significant difference in the medial temporal lobe atrophy between responders and non-responders was found, magnetization transfer ratios (MTRs) of the hippocampus and parahippocampus, indicators of structural damage, in the non-responder group were significantly reduced compared to those in the responder group. There were no significant differences in the severity of white matter lesions between the two groups. Logistic regression analysis revealed that the overall discrimination rate was 81%, with 85% of non-responders and 69% of responders, through measurement of the thickness of the substantia innominata and MTR of the hippocampus and parahippocampus. These results suggest that AD patients who show more severe cholinergic dysfunction and less severe structural damage of the hippocampus and parahippocampus as shown on MRI are likely to respond to donepezil treatment. (author)

Doppler laser imaging predicts response to topical minoxidil in the treatment of female pattern hair loss.

Science.gov (United States)

McCoy, J; Kovacevic, M; Situm, M; Stanimirovic, A; Bolanca, Z; Goren, A

2016-01-01

Topical minoxidil is the only drug approved by the US FDA for the treatment of female pattern hair loss. Unfortunately, following 16 weeks of daily application, less than 40% of patients regrow hair. Several studies have demonstrated that sulfotransferase enzyme activity in plucked hair follicles predicts topical minoxidil response in female pattern hair loss patients. However, due to patients’ discomfort with the procedure, and the time required to perform the enzymatic assay it would be ideal to develop a rapid, non-invasive test for sulfotransferase enzyme activity. Minoxidil is a pro-drug converted to its active form, minoxidil sulfate, by sulfotransferase enzymes in the outer root sheath of hair. Minoxidil sulfate is the active form required for both the promotion of hair regrowth and the vasodilatory effects of minoxidil. We thus hypothesized that laser Doppler velocimetry measurement of scalp blood perfusion subsequent to the application of topical minoxidil would correlate with sulfotransferase enzyme activity in plucked hair follicles. In this study, plucked hair follicles from female pattern hair loss patients were analyzed for sulfotransferase enzyme activity. Additionally, laser Doppler velocimetry was used to measure the change in scalp perfusion at 15, 30, 45, and 60 minutes, after the application of minoxidil. In agreement with our hypothesis, we discovered a correlation (r=1.0) between the change in scalp perfusion within 60 minutes after topical minoxidil application and sulfotransferase enzyme activity in plucked hairs. To our knowledge, this is the first study demonstrating the feasibility of using laser Doppler imaging as a rapid, non-invasive diagnostic test to predict topical minoxidil response in the treatment of female pattern hair loss.

Prediction of lung density changes after radiotherapy by cone beam computed tomography response markers and pre-treatment factors for non-small cell lung cancer patients

DEFF Research Database (Denmark)

Bernchou, Uffe; Hansen, Olfred; Schytte, Tine

2015-01-01

BACKGROUND AND PURPOSE: This study investigates the ability of pre-treatment factors and response markers extracted from standard cone-beam computed tomography (CBCT) images to predict the lung density changes induced by radiotherapy for non-small cell lung cancer (NSCLC) patients. METHODS...... AND MATERIALS: Density changes in follow-up computed tomography scans were evaluated for 135 NSCLC patients treated with radiotherapy. Early response markers were obtained by analysing changes in lung density in CBCT images acquired during the treatment course. The ability of pre-treatment factors and CBCT...

Can quantitative contrast-enhanced ultrasonography predict cervical tumor response to neoadjuvant chemotherapy?

Energy Technology Data Exchange (ETDEWEB)

Peng, Chuan; Liu, Long-Zhong; Zheng, Wei [Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060 (China); Xie, Yan-Jun [Department of Gynecology and Obstetrics, Zhongcun Town hospital, 140 Renmin Road, Zhongcun Town, Panyu District, Guangzhou, 511400 (China); Xiong, Yong-Hong; Li, An-Hua [Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060 (China); Pei, Xiao-Qing, E-mail: peixq@sysucc.org.cn [Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060 (China)

2016-11-15

Highlights: • We assessed the clinical value of quantitative CEUS for prediction of cervical tumor perfusion response to NACT. • IMAX, RT, and TTP changed significantly after one NACT cycle. • Pre-treatment IMAX positively correlated with the absolute and percentage changes in all cervical tumor IMAX after NACT. • Pre-treatment IMAX may be predictive of NACT perfusion response in cervical tumor. - Abstract: Objective: To evaluate the feasibility of quantitative contrast-enhanced ultrasonography (CEUS) for predicting and assessing cervical tumor response to neoadjuvant chemotherapy (NACT). Methods: Thirty-eight cases with stage IB2 or IIA cervical cancer were studied using CEUS before and after one cycle of NACT. The quantitative CEUS parameters maximum intensity (IMAX), rise time (RT), time to peak (TTP), and mean transit time (MTT) were compared between cervical tumors and myometrium (reference zone) using Sonoliver software. Absolute and relative changes in quantitative CEUS parameters were also compared among complete response, partial response, and non-responsive groups. Correlations between pre-treatment IMAX and changes in quantitative parameters were assessed after one cycle of NACT. Results: There were significant changes in cervical tumor IMAX (P < 0.001), RT (P < 0.05), and TTP (P < 0.05) after one cycle of NACT. According to the Response Evaluation Criteria In Solid Tumors guidelines, the enrollments were divided into complete response, partial response, stable disease and progressive disease groups. There were no significant differences in quantitative CEUS parameters among complete response, partial response, and non-responsive groups (P > 0.05). In the stable disease group (n = 17), cervical tumor IMAX, RT, and TTP decreased significantly after NACT (P < 0.001). The absolute and percentage changes in IMAX were positively correlated with pre-treatment IMAX in all 38 patients (r = 0.576, P < 0.001 and r = 0.429, P < 0.001). Conclusion

Volumetric response analysis during chemoradiation as predictive tool for optimizing treatment strategy in locally advanced unresectable NSCLC

International Nuclear Information System (INIS)

Bral, Samuel; Duchateau, Michael; De Ridder, Mark; Everaert, Hendrik; Tournel, Koen; Schallier, Denis; Verellen, Dirk; Storme, Guy

2009-01-01

Purpose: To study the feasibility of measuring volumetric changes in the primary tumor on megavoltage-computed tomography (MVCT) during chemoradiation and to examine the correlation with local response. Patients and methods: Fifteen consecutive patients with stage III, inoperable, locally advanced non-small cell lung cancer (NSCLC) were treated in a prospective dose escalation study protocol of concurrent chemoradiation. They were monitored for acute toxicity and evaluated with daily MVCT imaging. The volumetric changes were fitted to a negative exponential resulting in a regression coefficient (RC). Local response evaluation was done with positron emission tomography using the radio-labeled glucose analogue F18 fluorodeoxyglucose (FDG-PET). Results: The mean volume decrease (±standard deviation) was 73% (±18%). With a mean treatment time of 42 days this treatment schedule resulted in a mean decrease of 1.74%/day. Of the 13 evaluable patients seven developed a metabolic complete remission (MCR). The mean RC of the patients with MCR is 0.050 versus a mean RC of 0.023 in non-responders (p = 0.0074). Using a proposed cut-off value for the RC of 0.03 80% of the non-responders will be detected correctly while misclassifying 16.4% of patients who will eventually achieve an MCR. The total cumulative percentage of esophageal grade 3 or more toxicity was 46.7%. Conclusion: The RC derived from volumetric analysis of daily MVCT is prognostic and predictive for local response in patients treated with chemoradiation for a locally advanced NSCLC. Because this treatment schedule is toxic in nearly half of the patient population, MVCT is a tool in the implementation of patient-individualized treatment strategies.

Early perception of medication benefit predicts subsequent antipsychotic response in schizophrenia: "the consumer has a point" revisited.

Science.gov (United States)

Ascher-Svanum, Haya; Weiden, Peter; Nyhuis, Allen W; Faries, Douglas E; Stauffer, Virginia; Kollack-Walker, Sara; Kinon, Bruce J

2014-07-01

An easy-to-administer tool for predicting response to antipsychotic treatment could improve the acute management of patients with schizophrenia. We assessed whether a patient's perception of medication benefit early in treatment could predict subsequent response or nonresponse to continued use of the same treatment. This post hoc analysis used data from a randomized, open-label trial of antipsychotics for treatment of schizophrenia in which attitudes about medication adherence were assessed after two weeks of antipsychotic treatment using the Rating of Medication Influences (ROMI) scale. The analysis included 439 patients who had Positive and Negative Syndrome Scale (PANSS) and ROMI scale data at Weeks 2 and 8. Scores on the ROMI subscale Perceived Medication Benefit factor were used to predict subsequent antipsychotic response at Week 8, defined as a .20% reduction from baseline on the PANSS. Logistic regression was used to identify a cut-off score for the Perceived Medication Benefit factor that could accurately identify antipsychotic responders vs. nonresponders at Week 8. A score of .2.75 (equal to a mean subscale score of .11.00) on the ROMI scale Perceived Medication Benefit factor at Week 2 predicted response at Week 8 with high specificity (72%) and negative predictive value (70%), moderate sensitivity (44%) and positive predictive value (47%), and with a 38% misclassification rate. A brief assessment of the patient's perception of medication benefit at two weeks into treatment appears to be a good predictor of subsequent response and nonresponse after eight weeks of treatment with the same antipsychotic.

The Impact of Early Substance Use Disorder Treatment Response on Treatment Outcomes Among Pregnant Women With Primary Opioid Use.

Science.gov (United States)

Tuten, Michelle; Fitzsimons, Heather; Hochheimer, Martin; Jones, Hendree E; Chisolm, Margaret S

2018-03-13

This study examined the impact of early patient response on treatment utilization and substance use among pregnant participants enrolled in substance use disorder (SUD) treatment. Treatment responders (TRs) and treatment nonresponders (TNRs) were compared on pretreatment and treatment measures. Regression models predicted treatment utilization and substance use. TR participants attended more treatment and had lower rates of substance use relative to TNR participants. Regression models for treatment utilization and substance use were significant. Maternal estimated gestational age (EGA) and baseline cocaine use were negatively associated with treatment attendance. Medication-assisted treatment, early treatment response, and baseline SUD treatment were positively associated with treatment attendance. Maternal EGA was negatively associated with counseling attendance; early treatment response was positively associated with counseling attendance. Predictors of any substance use at 1 month were maternal education, EGA, early treatment nonresponse, and baseline cocaine use. The single predictor of any substance use at 2 months was early treatment nonresponse. Predictors of opioid use at 1 month were maternal education, EGA, early treatment nonresponse, and baseline SUD treatment. Predictors of opioid use at 2 months were early treatment nonresponse, and baseline cocaine and marijuana use. Predictors of cocaine use at 1 month were early treatment nonresponse, baseline cocaine use, and baseline SUD treatment. Predictors of cocaine use at 2 months were early treatment nonresponse and baseline cocaine use. Early treatment response predicts more favorable maternal treatment utilization and substance use outcomes. Treatment providers should implement interventions to maximize patient early response to treatment.

Pre-treatment cortisol awakening response predicts symptom reduction in posttraumatic stress disorder after treatment

NARCIS (Netherlands)

Rapcencu, A.E.; Gorter, R.; Kennis, Mitzy; van Rooij, S.J.H.; Geuze, E.

2017-01-01

Dysfunction of the HPA-axis has frequently been found in the aftermath of trauma exposure with or without PTSD. Decreasing HPA-axis reactivity to different stress cues has been reported during PTSD treatment. The cortisol awakening response (CARi) is a well-validated, standardized measure of

Pre-treatment cortisol awakening response predicts symptom reduction in posttraumatic stress disorder after treatment

NARCIS (Netherlands)

Rapcencu, A E; Gorter, R; Kennis, M; van Rooij, S J H; Geuze, E

Dysfunction of the HPA-axis has frequently been found in the aftermath of trauma exposure with or without PTSD. Decreasing HPA-axis reactivity to different stress cues has been reported during PTSD treatment. The cortisol awakening response (CARi) is a well-validated, standardized measure of

Predictive Modeling of Response to Pregabalin for the Treatment of Neuropathic Pain Using 6-Week Observational Data: A Spectrum of Modern Analytics Applications.

Science.gov (United States)

Emir, Birol; Johnson, Kjell; Kuhn, Max; Parsons, Bruce

2017-01-01

This post hoc analysis used 11 predictive models of data from a large observational study in Germany to evaluate potential predictors of achieving at least 50% pain reduction by week 6 after treatment initiation (50% pain response) with pregabalin (150-600 mg/d) in patients with neuropathic pain (NeP). The potential predictors evaluated included baseline demographic and clinical characteristics, such as patient-reported pain severity (0 [no pain] to 10 [worst possible pain]) and pain-related sleep disturbance scores (0 [sleep not impaired] to 10 [severely impaired sleep]) that were collected during clinic visits (baseline and weeks 1, 3, and 6). Baseline characteristics were also evaluated combined with pain change at week 1 or weeks 1 and 3 as potential predictors of end-of-treatment 50% pain response. The 11 predictive models were linear, nonlinear, and tree based, and all predictors in the training dataset were ranked according to their variable importance and normalized to 100%. The training dataset comprised 9187 patients, and the testing dataset had 6114 patients. To adjust for the high imbalance in the responder distribution (75% of patients were 50% responders), which can skew the parameter tuning process, the training set was balanced into sets of 1000 responders and 1000 nonresponders. The predictive modeling approaches that were used produced consistent results. Baseline characteristics alone had fair predictive value (accuracy range, 0.61-0.72; κ range, 0.17-0.30). Baseline predictors combined with pain change at week 1 had moderate predictive value (accuracy, 0.73-0.81; κ range, 0.37-0.49). Baseline predictors with pain change at weeks 1 and 3 had substantial predictive value (accuracy, 0.83-0.89; κ range, 0.54-0.71). When variable importance across the models was estimated, the best predictor of 50% responder status was pain change at week 3 (average importance 100.0%), followed by pain change at week 1 (48.1%), baseline pain score (14

Prediction of Early Response to Chemotherapy in Lung Cancer by Using Diffusion-Weighted MR Imaging

Directory of Open Access Journals (Sweden)

Jing Yu

2014-01-01

Full Text Available Purpose. To determine whether change of apparent diffusion coefficient (ADC value could predict early response to chemotherapy in lung cancer. Materials and Methods. Twenty-five patients with advanced non-small cell lung cancer underwent chest MR imaging including DWI before and at the end of the first cycle of chemotherapy. The tumor’s mean ADC value and diameters on MR images were calculated and compared. The grouping reference was based on serial CT scans according to Response Evaluation Criteria in Solid Tumors. Logistic regression was applied to assess treatment response prediction ability of ADC value and diameters. Results. The change of ADC value in partial response group was higher than that in stable disease group (P=0.004. ROC curve showed that ADC value could predict treatment response with 100% sensitivity, 64.71% specificity, 57.14% positive predictive value, 100% negative predictive value, and 82.7% accuracy. The area under the curve for combination of ADC value and longest diameter change was higher than any parameter alone (P≤0.01. Conclusions. The change of ADC value may be a sensitive indicator to predict early response to chemotherapy in lung cancer. Prediction ability could be improved by combining the change of ADC value and longest diameter.

Predictors of response in the treatment of moderate depression

Directory of Open Access Journals (Sweden)

Andre G. Bastos

Full Text Available Objective: To identify neurocognitive and sociodemographic variables that could be associated with clinical response to three modalities of treatment for depression, as well as variables that predicted superior response to one treatment over the others. Method: The present study derives from a research project in which depressed patients (n=272 received one of three treatments – long-term psychodynamic psychotherapy (n=90, fluoxetine therapy (n=91, or a combination thereof (n=91 – over a 24-month period. Results: Sociodemographic variables were not found to be predictive. Six predictive neurocognitive variables were identified: three prognostic variables related to working memory and abstract reasoning; one prescriptive variable related to working memory; and two variables found to be moderators. Conclusions: The results of this study indicate subgroups of patients who might benefit from specific therapeutic strategies and subgroups that seem to respond well to long-term psychodynamic psychotherapy and combined therapy. The moderators found suggest that abstract reasoning and processing speed may influence the magnitude and/or direction of clinical improvement.

Functional network architecture predicts psychologically mediated analgesia related to treatment in chronic knee pain patients.

Science.gov (United States)

Hashmi, Javeria Ali; Kong, Jian; Spaeth, Rosa; Khan, Sheraz; Kaptchuk, Ted J; Gollub, Randy L

2014-03-12

Placebo analgesia is an indicator of how efficiently the brain translates psychological signals conveyed by a treatment procedure into pain relief. It has been demonstrated that functional connectivity between distributed brain regions predicts placebo analgesia in chronic back pain patients. Greater network efficiency in baseline brain networks may allow better information transfer and facilitate adaptive physiological responses to psychological aspects of treatment. Here, we theorized that topological network alignments in resting state scans predict psychologically conditioned analgesic responses to acupuncture treatment in chronic knee osteoarthritis pain patients (n = 45). Analgesia was induced by building positive expectations toward acupuncture treatment with verbal suggestion and heat pain conditioning on a test site of the arm. This procedure induced significantly more analgesia after sham or real acupuncture on the test site than in a control site. The psychologically conditioned analgesia was invariant to sham versus real treatment. Efficiency of information transfer within local networks calculated with graph-theoretic measures (local efficiency and clustering coefficients) significantly predicted conditioned analgesia. Clustering coefficients in regions associated with memory, motivation, and pain modulation were closely involved in predicting analgesia. Moreover, women showed higher clustering coefficients and marginally greater pain reduction than men. Overall, analgesic response to placebo cues can be predicted from a priori resting state data by observing local network topology. Such low-cost synchronizations may represent preparatory resources that facilitate subsequent performance of brain circuits in responding to adaptive environmental cues. This suggests a potential utility of network measures in predicting placebo response for clinical use.

Nurse-Administered, Gut-Directed Hypnotherapy in IBS: Efficacy and Factors Predicting a Positive Response.

Science.gov (United States)

Lövdahl, Jenny; Ringström, Gisela; Agerforz, Pia; Törnblom, Hans; Simrén, Magnus

2015-07-01

Hypnotherapy is an effective treatment in irritable bowel syndrome (IBS). It is often delivered by a psychotherapist and is costly and time consuming. Nurse-administered hypnotherapy could increase availability and reduce costs. In this study the authors evaluate the effectiveness of nurse-administered, gut-directed hypnotherapy and identify factors predicting treatment outcome. Eighty-five patients were included in the study. Participants received hypnotherapy by a nurse once/week for 12 weeks. Patients reported marked improvement in gastrointestinal (GI) and extra-colonic symptoms after treatment, as well as a reduction in GI-specific anxiety, general anxiety, and depression. Fifty-eight percent were responders after the 12 weeks treatment period, and of these 82% had a favorable clinical response already at week 6. Women were more likely than men to respond favorably to the treatment. Nurse-administered hypnotherapy is an effective treatment for IBS. Being female and reporting a favorable response to treatment by week 6 predicted a positive treatment response at the end of the 12 weeks treatment period.

MTR-18 Predictive Biomarkers Of Bevacizumab Response In Recurrent Glioblastoma Patients

DEFF Research Database (Denmark)

Urup, Thomas; Michaelsen, Signe Regner; Olsen, Lars Rønn

2015-01-01

Bevacizumab (BEV) plus chemotherapy has shown activity in recurrent glioblastoma (GBM). However, the prognosis varies and only one third of patients have a durable clinical response to BEV combination therapy. Recent findings from a randomized phase-3 study (AVAglio) indicate that patients...... with the proneural GBM subtype have a survival benefit when treated with BEV in combination with standard treatment. However, no validated biomarkers able to predict BEV response have been identified and the biology reflecting a clinical BEV response is poorly understood. The primary objective of this study...... was to evaluate the predictive and prognostic value of GBM subtypes in recurrent GBM patients treated with BEV therapy. The secondary objective was to identify biomarkers able to predict response to BEV therapy in recurrent GBM patients. METHODS: A total of 90 recurrent GBM patients treated with BEV combination...

The Predictive Value of Early In-Treatment 18F-FDG PET/CT Response to Chemotherapy in Combination with Bevacizumab in Advanced Nonsquamous Non-Small Cell Lung Cancer.

Science.gov (United States)

Usmanij, Edwin A; Natroshvili, Tinatin; Timmer-Bonte, Johanna N H; Oyen, Wim J G; van der Drift, Miep A; Bussink, Johan; Geus-Oei, Lioe-Fee de

2017-08-01

18 F-FDG PET/CT is potentially applicable to predict response to chemotherapy in combination with bevacizumab in patients with advanced non-small cell lung cancer (NSCLC). Methods: In 25 patients with advanced nonsquamous NSCLC, 18 F-FDG PET/CT was performed before treatment and after 2 wk, at the end of the second week of first cycle carboplatin-paclitaxel and bevacizumab (CPB) treatment. Patients received up to a total of 4 cycles of CPB treatment. Maintenance treatment with bevacizumab monotherapy was continued until progressive disease without significant treatment-related toxicities of first-line treatment. In the case of progressive disease, bevacizumab was combined with erlotinib. SUV corrected for lean body mass (SUL and SUL peak ) were obtained. PERCIST were used for response evaluation. These semiquantitative parameters were correlated with progression-free survival and overall survival (OS). Results: Metabolic response, defined by a significant reduction in SUL peak of 30% or more after 2 wk of CPB, was predictive of progression-free survival and OS. For partial metabolic responders ( n = 19), the median OS was 22.8 mo. One-year and 2-y OS were 79% and 47%, respectively. Nonmetabolic responders ( n = 6) (stable metabolic disease or progressive disease) showed a median OS of 4.4 mo (1-y and 2-y OS was 33% and 0%, respectively) ( P predictive of outcome to first-line chemotherapy with bevacizumab in patients with advanced nonsquamous NSCLC. This enables identification of patients at risk of treatment failure, permitting treatment alternatives such as early switch to a different therapy. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Novelty response and 50 kHz ultrasonic vocalizations: Differential prediction of locomotor and affective response to amphetamine in Sprague-Dawley rats.

Science.gov (United States)

Garcia, Erik J; Cain, Mary E

2016-02-01

Novelty and sensation seeking (NSS) predisposes humans and rats to experiment with psychostimulants. In animal models, different tests of NSS predict different phases of drug dependence. Ultrasonic vocalizations (USVs) are evoked by psychomotor stimulants and measure the affective/motivation response to stimuli, yet the role NSS has on USVs in response to amphetamine is not determined. The aim of the present study was to determine if individual differences in NSS and USVs can predict locomotor and USV response to amphetamine (0.0, 0.3, and 1.0 mg/kg) after acute and chronic exposure. Thirty male rats were tested for their response to novelty (IEN), choice to engage in novelty (NPP), and heterospecific play (H-USV). Rats were administered non-contingent amphetamine or saline for seven exposures, and USVs and locomotor activity were measured. After a 14-day rest, rats were administered a challenge dose of amphetamine. Regression analyses indicated that amphetamine dose-dependently increased locomotor activity and the NPP test negatively predicted treatment-induced locomotor activity. The H-USV test predicted treatment-induced frequency-modulated (FM) USVs, but the strength of prediction depended on IEN response. Results provide evidence that locomotor activity and FM USVs induced by amphetamine represent different behavioral responses. The prediction of amphetamine-induced FM USVs by the H-USV screen was changed by the novelty response, indicating that the affective value of amphetamine-measured by FM USVs-depends on novelty response. This provides evidence that higher novelty responders may develop a tolerance faster and may escalate intake faster.

Predicting Response of ADHD Symptoms to Methylphenidate Treatment Based on Comorbid Anxiety

Science.gov (United States)

Blouin, Brittany; Maddeaux, Cindy; Stanley Firestone, Jill; van Stralen, Judy

2010-01-01

Objective: In this small pilot study, the association of comorbid anxiety with the treatment of ADHD is studied. Methods: Eighteen volunteers from a pediatric clinic are tested for ADHD and anxiety and assessed for behavioral and cognitive ADHD symptomology. Response to methylphenidate as treatment for ADHD symptoms is measured 2 to 3 weeks, and…

Stroke and TIA survivors’ cognitive beliefs and affective responses regarding treatment and future stroke risk differentially predict medication adherence and categorised stroke risk

Science.gov (United States)

Phillips, L. Alison; Diefenbach, Michael A.; Abrams, Jessica; Horowitz, Carol R.

2014-01-01

Cognitive beliefs and affective responses to illness and treatment are known to independently predict health behaviours. The purpose of the current study is to assess the relative importance of four psychological domains – specifically, affective illness, cognitive illness, affective treatment and cognitive treatment – for predicting stroke and transient ischemic attack (TIA) survivors’ adherence to stroke prevention medications as well as their objective, categorised stroke risk. We assessed these domains among stroke/TIA survivors (n = 600), and conducted correlation and regression analyses with concurrent and prospective outcomes to determine the relative importance of each cognitive and affective domain for adherence and stroke risk. As hypothesised, patients’ affective treatment responses explained the greatest unique variance in baseline and six-month adherence reports (8 and 5%, respectively, of the variance in adherence, compared to 1–3% explained by other domains). Counter to hypotheses, patients’ cognitive illness beliefs explained the greatest unique variance in baseline and six-month objective categorised stroke risk (3 and 2%, respectively, compared to 0–1% explained by other domains). Results indicate that domain type (i.e. cognitive and affective) and domain referent (illness and treatment) may be differentially important for providers to assess when treating patients for stroke/TIA. More research is required to further distinguish between these domains and their relative importance for stroke prevention. PMID:25220292

Stroke and TIA survivors' cognitive beliefs and affective responses regarding treatment and future stroke risk differentially predict medication adherence and categorised stroke risk.

Science.gov (United States)

Phillips, L Alison; Diefenbach, Michael A; Abrams, Jessica; Horowitz, Carol R

2015-01-01

Cognitive beliefs and affective responses to illness and treatment are known to independently predict health behaviours. The purpose of the current study is to assess the relative importance of four psychological domains - specifically, affective illness, cognitive illness, affective treatment and cognitive treatment - for predicting stroke and transient ischemic attack (TIA) survivors' adherence to stroke prevention medications as well as their objective, categorised stroke risk. We assessed these domains among stroke/TIA survivors (n = 600), and conducted correlation and regression analyses with concurrent and prospective outcomes to determine the relative importance of each cognitive and affective domain for adherence and stroke risk. As hypothesised, patients' affective treatment responses explained the greatest unique variance in baseline and six-month adherence reports (8 and 5%, respectively, of the variance in adherence, compared to 1-3% explained by other domains). Counter to hypotheses, patients' cognitive illness beliefs explained the greatest unique variance in baseline and six-month objective categorised stroke risk (3 and 2%, respectively, compared to 0-1% explained by other domains). Results indicate that domain type (i.e. cognitive and affective) and domain referent (illness and treatment) may be differentially important for providers to assess when treating patients for stroke/TIA. More research is required to further distinguish between these domains and their relative importance for stroke prevention.

Do Assault-Related Variables Predict Response to Cognitive Behavioral Treatment for PTSD?

Science.gov (United States)

Hembree, Elizabeth A.; Street, Gordon P.; Riggs, David S.; Foa, Edna B.

2004-01-01

This study examined the hypothesis that variables such as history of prior trauma, assault severity, and type of assault, previously found to be associated with natural recovery, would also predict treatment outcome. Trauma-related variables were examined as predictors of posttreatment posttraumatic stress disorder (PTSD) severity in a sample of…

Improved darunavir genotypic mutation score predicting treatment response for patients infected with HIRaben-1 subtype B and non-subtype B receiving a salvage regimen

NARCIS (Netherlands)

De Luca, Andrea; Flandre, Philippe; Dunn, David; Zazzi, Maurizio; Wensing, Annemarie; Santoro, Maria Mercedes; Günthard, Huldrych F.; Wittkop, Linda; Kordossis, Theodoros; Garcia, Federico; Castagna, Antonella; Cozzi-Lepri, Alessandro; Churchill, Duncan; De Wit, Stéphane; Brockmeyer, Norbert H.; Imaz, Arkaitz; Mussini, Cristina; Obel, Niels; Perno, Carlo Federico; Roca, Bernardino; Reiss, Peter; Schülter, Eugen; Torti, Carlo; van Sighem, Ard; Zangerle, Robert; Descamps, Diane; Mocroft, Amanda; Kirk, Ole; Sabin, Caroline; De Wit, Stéphane; Casabona, Jordi; Miró, Jose M.; Touloumi, Giota; Garrido, Myriam; Teira, Ramon; Wit, Ferdinand; Warszawski, Josiane; Meyer, Laurence; Dabis, François; Krause, Murielle Mary; Ghosn, Jade; Leport, Catherine; Prins, Maria; Bucher, Heiner; Gibb, Diana; Fätkenheuer, Gerd; del Amo, Julia; Thorne, Claire; Stephan, Christoph; Pérez-Hoyos, Santiago; Hamouda, Osamah; Bartmeyer, Barbara; Chkhartishvili, Nikoloz; Noguera-Julian, Antoni; Antinori, Andrea; d'Arminio Monforte, Antonella; Prieto, Luis; Conejo, Pablo Rojo; Soriano-Arandes, Antoni; Battegay, Manuel; Kouyos, Roger; Tookey, Pat; Konopnick, Deborah; Goetghebuer, Tessa; Sönnerborg, Anders; Haerry, David; de Wit, Stéphane; Costagliola, Dominique; Raben, Dorthe; Chêne, Geneviève; Ceccherini-Silberstein, Francesca; Günthard, Huldrych; Judd, Ali; Barger, Diana; Schwimmer, Christine; Termote, Monique; Campbell, Maria; Frederiksen, Casper M.; Friis-Møller, Nina; Kjaer, Jesper; Brandt, Rikke Salbøl; Berenguer, Juan; Bohlius, Julia; Bouteloup, Vincent; Davies, Mary Anne; Dorrucci, Maria; Egger, Matthias; Furrer, Hansjakob; Guiguet, Marguerite; Grabar, Sophie; Lambotte, Olivier; Leroy, Valériane; Lodi, Sara; Matheron, Sophie; Monge, Susana; Nakagawa, Fumiyo; Paredes, Roger; Phillips, Andrew; Puoti, Massimo; Schomaker, Michael; Smit, Colette; Sterne, Jonathan; Thiebaut, Rodolphe; van der Valk, Marc; Wyss, Natasha; Aubert, V.; Battegay, M.; Bernasconi, E.; Böni, J.; Burton-Jeangros, C.; Calmy, A.; Cavassini, M.; Dollenmaier, G.; Egger, M.; Elzi, L.; Fehr, J.; Fellay, J.; Furrer, H.; Fux, C. A.; Gorgievski, M.; Günthard, H.; Haerry, D.; Hasse, B.; Hirsch, H. H.; Hoffmann, M.; Hösli, I.; Kahlert, C.; Kaiser, L.; Keiser, O.; Klimkait, T.; Kouyos, R.; Kovari, H.; Ledergerber, B.; Martinetti, G.; Martinez de Tejada, B.; Metzner, K.; Müller, N.; Nadal, D.; Nicca, D.; Pantaleo, G.; Rauch, A.; Regenass, S.; Rickenbach, M.; Rudin, C.; Schöni-Affolter, F.; Schmid, P.; Schüpbach, J.; Speck, R.; Tarr, P.; Telenti, A.; Trkola, A.; Vernazza, P.; Weber, R.; Yerly, S.

2016-01-01

Objectives: The objective of this studywas to improve the prediction of the impact of HIV-1 protease mutations in different viral subtypes on virological response to darunavir. Methods: Darunavir-containing treatment change episodes (TCEs) in patients previously failing PIs were selected from large

Predicting meaningful outcomes to medication and self-help treatments for binge-eating disorder in primary care: The significance of early rapid response.

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Grilo, Carlos M; White, Marney A; Masheb, Robin M; Gueorguieva, Ralitza

2015-04-01

We examined rapid response among obese patients with binge-eating disorder (BED) in a randomized clinical trial testing antiobesity medication and self-help cognitive-behavioral therapy (shCBT), alone and in combination, in primary-care settings. One hundred four obese patients with BED were randomly assigned to 1 of 4 treatments: sibutramine, placebo, shCBT + sibutramine, or shCBT + placebo. Treatments were delivered by generalist primary-care physicians and the medications were given double-blind. Independent assessments were performed by trained and monitored doctoral research clinicians monthly throughout treatment, posttreatment (4 months), and at 6- and 12-month follow-ups (i.e., 16 months after randomization). Rapid response, defined as ≥65% reduction in binge eating by the fourth treatment week, was used to predict outcomes. Rapid response characterized 47% of patients, was unrelated to demographic and baseline clinical characteristics, and was significantly associated, prospectively, with remission from binge eating at posttreatment (51% vs. 9% for nonrapid responders), 6-month (53% vs. 23.6%), and 12-month (46.9% vs. 23.6%) follow-ups. Mixed-effects model analyses revealed that rapid response was significantly associated with greater decreases in binge-eating or eating-disorder psychopathology, depression, and percent weight loss. Our findings, based on a diverse obese patient group receiving medication and shCBT for BED in primary-care settings, indicate that patients who have a rapid response achieve good clinical outcomes through 12-month follow-ups after ending treatment. Rapid response represents a strong prognostic indicator of clinically meaningful outcomes, even in low-intensity medication and self-help interventions. Rapid response has important clinical implications for stepped-care treatment models for BED. clinicaltrials.gov: NCT00537810 (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

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Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

2016-07-01

The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

A New Prediction Model for Evaluating Treatment-Resistant Depression.

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Kautzky, Alexander; Baldinger-Melich, Pia; Kranz, Georg S; Vanicek, Thomas; Souery, Daniel; Montgomery, Stuart; Mendlewicz, Julien; Zohar, Joseph; Serretti, Alessandro; Lanzenberger, Rupert; Kasper, Siegfried

2017-02-01

Despite a broad arsenal of antidepressants, about a third of patients suffering from major depressive disorder (MDD) do not respond sufficiently to adequate treatment. Using the data pool of the Group for the Study of Resistant Depression and machine learning, we intended to draw new insights featuring 48 clinical, sociodemographic, and psychosocial predictors for treatment outcome. Patients were enrolled starting from January 2000 and diagnosed according to DSM-IV. Treatment-resistant depression (TRD) was defined by a 17-item Hamilton Depression Rating Scale (HDRS) score ≥ 17 after at least 2 antidepressant trials of adequate dosage and length. Remission was defined by an HDRS score depressive episode, age at first antidepressant treatment, response to first antidepressant treatment, severity, suicidality, melancholia, number of lifetime depressive episodes, patients' admittance type, education, occupation, and comorbid diabetes, panic, and thyroid disorder. While single predictors could not reach a prediction accuracy much different from random guessing, by combining all predictors, we could detect resistance with an accuracy of 0.737 and remission with an accuracy of 0.850. Consequently, 65.5% of predictions for TRD and 77.7% for remission can be expected to be accurate. Using machine learning algorithms, we could demonstrate success rates of 0.737 for predicting TRD and 0.850 for predicting remission, surpassing predictive capabilities of clinicians. Our results strengthen data mining and suggest the benefit of focus on interaction-based statistics. Considering that all predictors can easily be obtained in a clinical setting, we hope that our model can be tested by other research groups. © Copyright 2017 Physicians Postgraduate Press, Inc.

A Personalized Approach to Biological Therapy Using Prediction of Clinical Response Based on MRP8/14 Serum Complex Levels in Rheumatoid Arthritis Patients.

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S C Nair

Full Text Available Measurement of MRP8/14 serum levels has shown potential in predicting clinical response to different biological agents in rheumatoid arthritis (RA. We aimed to develop a treatment algorithm based on a prediction score using MRP8/14 measurements and clinical parameters predictive for response to different biological agents.Baseline serum levels of MRP8/14 were measured in 170 patients starting treatment with infliximab, adalimumab or rituximab. We used logistic regression analysis to develop a predictive score for clinical response at 16 weeks. MRP8/14 levels along with clinical variables at baseline were investigated. We also investigated how the predictive effect of MRP8/14 was modified by drug type. A treatment algorithm was developed based on categorizing the expected response per drug type as high, intermediate or low for each patient and optimal treatment was defined. Finally, we present the utility of using this treatment algorithm in clinical practice.The probability of response increased with higher baseline MRP8/14 complex levels (OR = 1.39, differentially between the TNF-blockers and rituximab (OR of interaction term = 0.78, and also increased with higher DAS28 at baseline (OR = 1.28. Rheumatoid factor positivity, functional disability (a higher HAQ, and previous use of a TNF-inhibitor decreased the probability of response. Based on the treatment algorithm 80 patients would have been recommended for anti-TNF treatment, 8 for rituximab, 13 for another biological treatment (other than TNFi or rituximab and for 69 no recommendation was made. The predicted response rates matched the observed response in the cohort well. On group level the predicted response based on the algorithm resulted in a modest 10% higher response rate in our cohort with much higher differences in response probability in individual patients treated contrary to treatment recommendation.Prediction of response using MRP8/14 levels along with clinical predictors has

Predicting post-traumatic stress disorder treatment response in refugees : Multilevel analysis

NARCIS (Netherlands)

Haagen, Joris F G; Ter Heide, F Jackie June; Mooren, Trudy M; Knipscheer, Jeroen W; Kleber, Rolf J

2017-01-01

OBJECTIVES: Given the recent peak in refugee numbers and refugees' high odds of developing post-traumatic stress disorder (PTSD), finding ways to alleviate PTSD in refugees is of vital importance. However, there are major differences in PTSD treatment response between refugees, the determinants of

Predictive value of early {sup 18}F-FDG PET/CT studies for treatment response evaluation to ipilimumab in metastatic melanoma: preliminary results of an ongoing study

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Sachpekidis, Christos; Pan, Leyun; Dimitrakopoulou-Strauss, Antonia [German Cancer Research Center, Clinical Cooperation Unit Nuclear Medicine, Heidelberg (Germany); Larribere, Lionel [German Cancer Research Center, Clinical Cooperation Unit Dermato-Oncology, Heidelberg (Germany); Haberkorn, Uwe [German Cancer Research Center, Clinical Cooperation Unit Nuclear Medicine, Heidelberg (Germany); University of Heidelberg, Division of Nuclear Medicine, Heidelberg (Germany); Hassel, Jessica C. [University Hospital Heidelberg, Skin Cancer Center, Department of Dermatology, Heidelberg (Germany); National Center for Tumor Diseases Heidelberg, Heidelberg (Germany)

2014-10-31

Ipilimumab is a newly approved immunotherapeutic agent that has been shown to provide a survival benefit in patients with metastatic melanoma. {sup 18}F-FDG PET/CT has demonstrated very satisfying results in detecting melanoma metastases in general. Using {sup 18}F-FDG PET/CT we monitored patients with metastatic melanoma undergoing ipilimumab therapy during the course of treatment. The aim of our study was to evaluate the role of {sup 18}F-FDG PET/CT performed after two cycles of ipilimumab in predicting the final response to therapy. In 22 patients suffering from unresectable metastatic melanoma, scheduled for ipilimumab treatment PET/CT scanning was performed before the start of treatment (baseline scan), after two cycles of treatment (early response) and at the end of treatment after four cycles (late response). Evaluation of the patient response to treatment on PET was based on the European Organization for Research and Treatment of Cancer 1999 criteria. Progression-free survival (PFS) and overall survival (OS) data are presented. After the end of treatment, 15 patients were characterized as having progressive metabolic disease (PMD) and five as having stable metabolic disease (SMD), and two patients showed a partial metabolic response (PMR). Early PET/CT performed after two ipilimumab cycles predicted treatment response in 13 of the 15 PMD patients, in five of the five SMD patients and in neither of the two PMR patients. Both patients with PMR showed pseudoprogression after the second cycle and were therefore wrongly classified. According to the patients' clinical outcome, patients with late PMD had a median PFS of 3.6 months (mean 5.6 months), while patients with late SMD had a median PFS of 9.8 months (mean 9.0 months). In comparison, patients with early PMD had a median PFS of 2.7 months (mean 5.5 months) and patients with early SMD had a median PFS of 6.3 months (mean 7.5 months). The difference in PFS between the two groups was statistically

Do circulating long non-coding RNAs (lncRNAs) (LincRNA-p21, GAS 5, HOTAIR) predict the treatment response in patients with head and neck cancer treated with chemoradiotherapy?

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Fayda, Merdan; Isin, Mustafa; Tambas, Makbule; Guveli, Murat; Meral, Rasim; Altun, Musa; Sahin, Dilek; Ozkan, Gozde; Sanli, Yasemin; Isin, Husniye; Ozgur, Emre; Gezer, Ugur

2016-03-01

Long non-coding RNAs (lncRNAs) have been shown to be aberrantly expressed in head and neck cancer (HNC). The aim of the present study was to evaluate plasma levels of three lncRNA molecules (lincRNA-p21, GAS5, and HOTAIR) in the treatment response in HNC patients treated with radical chemoradiotherapy (CRT). Forty-one patients with HNC were enrolled in the study. Most of the patients had nasopharyngeal carcinoma (n = 27, 65.9 %) and locally advanced disease. Blood was drawn at baseline and treatment evaluation 4.5 months after therapy. lncRNAs in plasma were measured by semiquantitative PCR. Treatment response was evaluated according to clinical examination, RECIST and PERCIST criteria based on magnetic resonance imaging (MRI), and positron emission tomography with computed tomography (PET/CT) findings. Complete response (CR) rates were 73.2, 36.6, and 50 % for clinical investigation, PET/CT-, or MRI-based response evaluation, respectively. Predictive value of lncRNAs was investigated in patients with CR vs. those with partial response (PR)/progressive disease (PD). We found that post-treatment GAS5 levels in patients with PR/PD were significantly higher compared with patients with CR based on clinical investigation (p = 0.01). Receiver operator characteristic (ROC) analysis showed that at a cutoff value of 0.3 of GAS5, sensitivity and specificity for clinical tumor response were 82 and 77 %, respectively. Interestingly, pretreatment GAS5 levels were significantly increased in patients with PR/PD compared to those with CR upon MRI-based response evaluation (p = 0.042). In contrast to GAS5, pretreatment or post-treatment lincRNA-p21 and HOTAIR levels were not informative for treatment response. Our results suggest that circulating GAS5 could be a biomarker in predicting treatment response in HNC patients.

Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression.

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Zhang, Xinyan; Li, Bingzong; Han, Huiying; Song, Sha; Xu, Hongxia; Hong, Yating; Yi, Nengjun; Zhuang, Wenzhuo

2018-05-10

Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients' response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered. It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm. We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response. The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies.

Striatal Activation Predicts Differential Therapeutic Responses to Methylphenidate and Atomoxetine.

Science.gov (United States)

Schulz, Kurt P; Bédard, Anne-Claude V; Fan, Jin; Hildebrandt, Thomas B; Stein, Mark A; Ivanov, Iliyan; Halperin, Jeffrey M; Newcorn, Jeffrey H

2017-07-01

Methylphenidate has prominent effects in the dopamine-rich striatum that are absent for the selective norepinephrine transporter inhibitor atomoxetine. This study tested whether baseline striatal activation would predict differential response to the two medications in youth with attention-deficit/hyperactivity disorder (ADHD). A total of 36 youth with ADHD performed a Go/No-Go test during functional magnetic resonance imaging at baseline and were treated with methylphenidate and atomoxetine using a randomized cross-over design. Whole-brain task-related activation was regressed on clinical response. Task-related activation in right caudate nucleus was predicted by an interaction of clinical responses to methylphenidate and atomoxetine (F 1,30  = 17.00; p atomoxetine. The rate of robust response was higher for methylphenidate than for atomoxetine in youth with high (94.4% vs. 38.8%; p = .003; number needed to treat = 2, 95% CI = 1.31-3.73) but not low (33.3% vs. 50.0%; p = .375) caudate activation. Furthermore, response to atomoxetine predicted motor cortex activation (F 1,30  = 14.99; p atomoxetine in youth with ADHD, purportedly reflecting the dopaminergic effects of methylphenidate but not atomoxetine in the striatum, whereas motor cortex activation may predict response to atomoxetine. These data do not yet translate directly to the clinical setting, but the approach is potentially important for informing future research and illustrates that it may be possible to predict differential treatment response using a biomarker-driven approach. Stimulant Versus Nonstimulant Medication for Attention Deficit Hyperactivity Disorder in Children; https://clinicaltrials.gov/; NCT00183391. Copyright © 2017 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Quantitative DCE-MRI for prediction of pathological complete response following neoadjuvant treatment for locally advanced breast cancer: the impact of breast cancer subtypes on the diagnostic accuracy

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Drisis, Stylianos; Stathopoulos, Konstantinos; Chao, Shih-Li; Lemort, Marc [Institute Jules Bordet, Radiology Department, Brussels (Belgium); Metens, Thierry [Erasme University Hospital, Radiology Department, Brussels (Belgium); Ignatiadis, Michael [Institute Jules Bordet, Oncology Department, Brussels (Belgium)

2016-05-15

To assess whether DCE-MRI pharmacokinetic (PK) parameters obtained before and during chemotherapy can predict pathological complete response (pCR) differently for different breast cancer groups. Eighty-four patients who received neoadjuvant chemotherapy for locally advanced breast cancer were retrospectively included. All patients underwent two DCE-MRI examinations, one before (EX1) and one during treatment (EX2). Tumours were classified into different breast cancer groups, namely triple negative (TNBC), HER2+ and ER+/HER2-, and compared with the whole population (WP). PK parameters Ktrans and Ve were extracted using a two-compartment Tofts model. At EX1, Ktrans predicted pCR for WP and TNBC. At EX2, maximum diameter (Dmax) predicted pCR for WP and ER+/HER2-. Both PK parameters predicted pCR in WP and TNBC and only Ktrans for the HER2+. pCR was predicted from relative difference (EX1 - EX2)/EX1 of Dmax and both PK parameters in the WP group and only for Ve in the TNBC group. No PK parameter could predict response for ER+/HER-. ROC comparison between WP and breast cancer groups showed higher but not statistically significant values for TNBC for the prediction of pCR Quantitative DCE-MRI can better predict pCR after neoadjuvant treatment for TNBC but not for the ER+/HER2- group. (orig.)

Early prediction of the response of breast tumors to neoadjuvant chemotherapy using quantitative MRI and machine learning.

Science.gov (United States)

Mani, Subramani; Chen, Yukun; Arlinghaus, Lori R; Li, Xia; Chakravarthy, A Bapsi; Bhave, Sandeep R; Welch, E Brian; Levy, Mia A; Yankeelov, Thomas E

2011-01-01

The ability to predict early in the course of treatment the response of breast tumors to neoadjuvant chemotherapy can stratify patients based on response for patient-specific treatment strategies. Currently response to neoadjuvant chemotherapy is evaluated based on physical exam or breast imaging (mammogram, ultrasound or conventional breast MRI). There is a poor correlation among these measurements and with the actual tumor size when measured by the pathologist during definitive surgery. We tested the feasibility of using quantitative MRI as a tool for early prediction of tumor response. Between 2007 and 2010 twenty consecutive patients diagnosed with Stage II/III breast cancer and receiving neoadjuvant chemotherapy were enrolled on a prospective imaging study. Our study showed that quantitative MRI parameters along with routine clinical measures can predict responders from non-responders to neoadjuvant chemotherapy. The best predictive model had an accuracy of 0.9, a positive predictive value of 0.91 and an AUC of 0.96.

Response to a combination of oxygen and a hypnotic as treatment for obstructive sleep apnoea is predicted by a patient's therapeutic CPAP requirement.

Science.gov (United States)

Landry, Shane A; Joosten, Simon A; Sands, Scott A; White, David P; Malhotra, Atul; Wellman, Andrew; Hamilton, Garun S; Edwards, Bradley A

2017-08-01

Upper airway collapsibility predicts the response to several non-continuous positive airway pressure (CPAP) interventions for obstructive sleep apnoea (OSA). Measures of upper airway collapsibility cannot be easily performed in a clinical context; however, a patient's therapeutic CPAP requirement may serve as a surrogate measure of collapsibility. The present work aimed to compare the predictive use of CPAP level with detailed physiological measures of collapsibility. Therapeutic CPAP levels and gold-standard pharyngeal collapsibility measures (passive pharyngeal critical closing pressure (P crit ) and ventilation at CPAP level of 0 cmH 2 O (V passive )) were retrospectively analysed from a randomized controlled trial (n = 20) comparing the combination of oxygen and eszopiclone (treatment) versus placebo/air control. Responders (9/20) to treatment were defined as those who exhibited a 50% reduction in apnoea/hypopnoea index (AHI) plus an AHICPAP requirement compared with non-responders (6.6 (5.4-8.1)  cmH 2 O vs 8.9 (8.4-10.4) cmH 2 O, P = 0.007), consistent with their reduced collapsibility (lower P crit , P = 0.017, higher V passive P = 0.025). Therapeutic CPAP level provided the highest predictive accuracy for differentiating responders from non-responders (area under the curve (AUC) = 0.86 ± 0.9, 95% CI: 0.68-1.00, P = 0.007). However, both P crit (AUC = 0.83 ± 0.11, 95% CI: 0.62-1.00, P = 0.017) and V passive (AUC = 0.77 ± 0.12, 95% CI: 0.53-1.00, P = 0.44) performed well, and the difference in AUC for these three metrics was not statistically different. A therapeutic CPAP level ≤8 cmH 2 O provided 78% sensitivity and 82% specificity (positive predictive value = 78%, negative predictive value = 82%) for predicting a response to these therapies. Therapeutic CPAP requirement, as a surrogate measure of pharyngeal collapsibility, predicts the response to non-anatomical therapy (oxygen and

Predicting Treatment Response of Colorectal Cancer Liver Metastases to Conventional Lipiodol-Based Transarterial Chemoembolization Using Diffusion-Weighted MR Imaging: Value of Pretreatment Apparent Diffusion Coefficients (ADC) and ADC Changes Under Therapy.

Science.gov (United States)

Lahrsow, Maximilian; Albrecht, Moritz H; Bickford, Matthew W; Vogl, Thomas J

2017-06-01

To use absolute pretreatment apparent diffusion coefficients (ADC) derived from diffusion-weighted MR imaging (DWI) to predict response to repetitive cTACE for unresectable liver metastases of colorectal carcinoma (CRLM) at 1 and 3 months after start of treatment. Fifty-five metastases in 34 patients were examined with DWI prior to treatment and 1 month after initial cTACE. Treatment was performed in 4-week intervals. Response was evaluated at 1 and 3 months after start of therapy. Metastases showing a decrease of ≥30% in axial diameter were classified as responding lesions. One month after initial cTACE, seven lesions showed early response. There was no significant difference in absolute pretreatment ADC values between responding and non-responding lesions (p = 0.94). Three months after initial cTACE, 17 metastases showed response. There was a significant difference (p = 0.021) between absolute pretreatment ADC values of lesions showing response (median 1.08 × 10 -3  mm 2 /s) and no response (median 1.30 × 10 -3  mm 2 /s). Pretreatment ADC showed fair diagnostic value to predict response (AUC 0.7). Lesions showing response at 3 months also revealed a significant increase in ADC between measurements before treatment and at one month after initial cTACE (p < 0.001). Applying an increase in ADC of 12.17%, response at 3 months after initial cTACE could be predicted with a sensitivity and specificity of 77 and 74%, respectively (AUC 0.817). Furthermore, there was a strong and significant correlation (r = 0.651, p < 0.001) between percentage change in size after third cTACE and percentage change in ADC. In patients with CRLM, ADC measurements are potential biomarkers for assessing response to cTACE.

Excessive biologic response to IFNβ is associated with poor treatment response in patients with multiple sclerosis.

Directory of Open Access Journals (Sweden)

Richard A Rudick

Full Text Available BACKGROUND: Interferon-beta (IFNβ is used to inhibit disease activity in multiple sclerosis (MS, but its mechanisms of action are incompletely understood, individual treatment response varies, and biological markers predicting response to treatment have yet to be identified. METHODS: The relationship between the molecular response to IFNβ and treatment response was determined in 85 patients using a longitudinal design in which treatment effect was categorized by brain magnetic resonance imaging as good (n = 70 or poor response (n = 15. Molecular response was quantified using a customized cDNA macroarray assay for 166 IFN-regulated genes (IRGs. RESULTS: The molecular response to IFNβ differed significantly between patients in the pattern and number of regulated genes. The molecular response was strikingly stable for individuals for as long as 24 months, however, suggesting an individual 'IFN response fingerprint'. Unexpectedly, patients with poor response showed an exaggerated molecular response. IRG induction ratios demonstrated an exaggerated molecular response at both the first and 6-month IFNβ injections. CONCLUSION: MS patients exhibit individually unique but temporally stable biological responses to IFNβ. Poor treatment response is not explained by the duration of biological effects or the specific genes induced. Rather, individuals with poor treatment response have a generally exaggerated biological response to type 1 IFN injections. We hypothesize that the molecular response to type I IFN identifies a pathogenetically distinct subset of MS patients whose disease is driven in part by innate immunity. The findings suggest a strategy for biologically based, rational use of IFNβ for individual MS patients.

Expression of estrogen-related gene markers in breast cancer tissue predicts aromatase inhibitor responsiveness.

Directory of Open Access Journals (Sweden)

Irene Moy

Full Text Available Aromatase inhibitors (AIs are the most effective class of drugs in the endocrine treatment of breast cancer, with an approximate 50% treatment response rate. Our objective was to determine whether intratumoral expression levels of estrogen-related genes are predictive of AI responsiveness in postmenopausal women with breast cancer. Primary breast carcinomas were obtained from 112 women who received AI therapy after failing adjuvant tamoxifen therapy and developing recurrent breast cancer. Tumor ERα and PR protein expression were analyzed by immunohistochemistry (IHC. Messenger RNA (mRNA levels of 5 estrogen-related genes-AKR1C3, aromatase, ERα, and 2 estradiol/ERα target genes, BRCA1 and PR-were measured by real-time PCR. Tumor protein and mRNA levels were compared with breast cancer progression rates to determine predictive accuracy. Responsiveness to AI therapy-defined as the combined complete response, partial response, and stable disease rates for at least 6 months-was 51%; rates were 56% in ERα-IHC-positive and 14% in ERα-IHC-negative tumors. Levels of ERα, PR, or BRCA1 mRNA were independently predictive for responsiveness to AI. In cross-validated analyses, a combined measurement of tumor ERα and PR mRNA levels yielded a more superior specificity (36% and identical sensitivity (96% to the current clinical practice (ERα/PR-IHC. In patients with ERα/PR-IHC-negative tumors, analysis of mRNA expression revealed either non-significant trends or statistically significant positive predictive values for AI responsiveness. In conclusion, expression levels of estrogen-related mRNAs are predictive for AI responsiveness in postmenopausal women with breast cancer, and mRNA expression analysis may improve patient selection.

Brain responses to biological motion predict treatment outcome in young adults with autism receiving Virtual Reality Social Cognition Training: Preliminary findings.

Science.gov (United States)

Yang, Y J Daniel; Allen, Tandra; Abdullahi, Sebiha M; Pelphrey, Kevin A; Volkmar, Fred R; Chapman, Sandra B

2017-06-01

Autism Spectrum Disorder (ASD) is characterized by remarkable heterogeneity in social, communication, and behavioral deficits, creating a major barrier in identifying effective treatments for a given individual with ASD. To facilitate precision medicine in ASD, we utilized a well-validated biological motion neuroimaging task to identify pretreatment biomarkers that can accurately forecast the response to an evidence-based behavioral treatment, Virtual Reality-Social Cognition Training (VR-SCT). In a preliminary sample of 17 young adults with high-functioning ASD, we identified neural predictors of change in emotion recognition after VR-SCT. The predictors were characterized by the pretreatment brain activations to biological vs. scrambled motion in the neural circuits that support (a) language comprehension and interpretation of incongruent auditory emotions and prosody, and (b) processing socio-emotional experience and interpersonal affective information, as well as emotional regulation. The predictive value of the findings for individual adults with ASD was supported by regression-based multivariate pattern analyses with cross validation. To our knowledge, this is the first pilot study that shows neuroimaging-based predictive biomarkers for treatment effectiveness in adults with ASD. The findings have potentially far-reaching implications for developing more precise and effective treatments for ASD. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Long-term response to recombinant human growth hormone treatment: a new predictive mathematical method.

Science.gov (United States)

Migliaretti, G; Ditaranto, S; Guiot, C; Vannelli, S; Matarazzo, P; Cappello, N; Stura, I; Cavallo, F

2018-07-01

Recombinant GH has been offered to GH-deficient (GHD) subjects for more than 30 years, in order to improve height and growth velocity in children and to enhance metabolic effects in adults. The aim of our work is to describe the long-term effect of rhGH treatment in GHD pediatric patients, suggesting a growth prediction model. A homogeneous database is defined for diagnosis and treatment modalities, based on GHD patients afferent to Hospital Regina Margherita in Turin (Italy). In this study, 232 GHD patients are selected (204 idiopathic GHD and 28 organic GHD). Each measure is shown in terms of mean with relative standard deviations (SD) and 95% confidence interval (95% CI). To estimate the final height of each patient on the basis of few measures, a mathematical growth prediction model [based on Gompertzian function and a mixed method based on the radial basis functions (RBFs) and the particle swarm optimization (PSO) models] was performed. The results seem to highlight the benefits of an early start of treatment, further confirming what is suggested by the literature. Generally, the RBF-PSO method shows a good reliability in the prediction of the final height. Indeed, RMSE is always lower than 4, i.e., in average the forecast will differ at most of 4 cm to the real value. In conclusion, the large and accurate database of Italian GHD patients allowed us to assess the rhGH treatment efficacy and compare the results with those obtained in other Countries. Moreover, we proposed and validated a new mathematical model forecasting the expected final height after therapy which was validated on our cohort.

Improved darunavir genotypic mutation score predicting treatment response for patients infected with HIV-1 subtype B and non-subtype B receiving a salvage regimen

DEFF Research Database (Denmark)

De Luca, Andrea; Flandre, Philippe; Dunn, David

2016-01-01

OBJECTIVES: The objective of this study was to improve the prediction of the impact of HIV-1 protease mutations in different viral subtypes on virological response to darunavir. METHODS: Darunavir-containing treatment change episodes (TCEs) in patients previously failing PIs were selected from...... was derived based on best subset least squares estimation with mutational weights corresponding to regression coefficients. Virological outcome prediction accuracy was compared with that from existing genotypic resistance interpretation systems (GISs) (ANRS 2013, Rega 9.1.0 and HIVdb 7.0). RESULTS: TCEs were...

The negativity bias predicts response rate to Behavioral Activation for depression.

Science.gov (United States)

Gollan, Jackie K; Hoxha, Denada; Hunnicutt-Ferguson, Kallio; Norris, Catherine J; Rosebrock, Laina; Sankin, Lindsey; Cacioppo, John

2016-09-01

This treatment study investigated the extent to which asymmetric dimensions of affective responding, specifically the positivity offset and the negativity bias, at pretreatment altered the rate of response to Behavioral Activation treatment for depression. Forty-one depressed participants were enrolled into 16 weekly sessions of BA. An additional 36 lifetime healthy participants were evaluated prospectively for 16 weeks to compare affective responding between healthy and remitted patients at post-treatment. All participants were assessed at Weeks 0, 8 and 16 using repeated measures, involving a structured clinical interview for DSM-IV Axis I disorders, questionnaires, and a computerized task designed to measure affective responses to unpleasant, neutral, and pleasant images. The negativity bias at pre-treatment predicted the rate of response to BA, while the positivity offset did not. Only one treatment condition was used in this study and untreated depressed participants were not enrolled, limiting our ability to compare the effect of BA. Baseline negativity bias may serve as a signal for patients to engage in and benefit from the goal-directed BA strategies, thereby accelerating rate of response. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prediction of electroconvulsive therapy response and remission in major depression : meta-analysis

OpenAIRE

Diermen, van, Linda; Ameele, van den, Seline; Kamperman, Astrid M.; Sabbe, Bernard G.C.; Vermeulen, Tom; Schrijvers, Didier; Birkenhager, Tom K.

2018-01-01

Abstract: Background Electroconvulsive therapy (ECT) is considered to be the most effective treatment in severe major depression. The identification of reliable predictors of ECT response could contribute to a more targeted patient selection and consequently increased ECT response rates. Aims To investigate the predictive value of age, depression severity, psychotic and melancholic features for ECT response and remission in major depression. Method A meta-analysis was conducted according to t...

Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction.

Science.gov (United States)

Patel, Meenal J; Andreescu, Carmen; Price, Julie C; Edelman, Kathryn L; Reynolds, Charles F; Aizenstein, Howard J

2015-10-01

Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate accurate prediction models for late-life depression diagnosis and treatment response using multiple machine learning methods with inputs of multi-modal imaging and non-imaging whole brain and network-based features. Late-life depression patients (medicated post-recruitment) (n = 33) and older non-depressed individuals (n = 35) were recruited. Their demographics and cognitive ability scores were recorded, and brain characteristics were acquired using multi-modal magnetic resonance imaging pretreatment. Linear and nonlinear learning methods were tested for estimating accurate prediction models. A learning method called alternating decision trees estimated the most accurate prediction models for late-life depression diagnosis (87.27% accuracy) and treatment response (89.47% accuracy). The diagnosis model included measures of age, Mini-mental state examination score, and structural imaging (e.g. whole brain atrophy and global white mater hyperintensity burden). The treatment response model included measures of structural and functional connectivity. Combinations of multi-modal imaging and/or non-imaging measures may help better predict late-life depression diagnosis and treatment response. As a preliminary observation, we speculate that the results may also suggest that different underlying brain characteristics defined by multi-modal imaging measures-rather than region-based differences-are associated with depression versus depression recovery because to our knowledge this is the first depression study to accurately predict both using the same approach. These findings may help better understand late-life depression and identify preliminary steps toward personalized late-life depression treatment. Copyright © 2015 John Wiley

Frontal lobe functioning during a simple response conflict task in first-episode psychosis and its relationship to treatment response.

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Shafritz, Keith M; Ikuta, Toshikazu; Greene, Allison; Robinson, Delbert G; Gallego, Juan; Lencz, Todd; DeRosse, Pamela; Kingsley, Peter B; Szeszko, Philip R

2018-05-09

Prior functional magnetic resonance imaging (fMRI) studies have investigated the neural mechanisms underlying cognitive control in patients with psychosis with findings of both hypo- and hyperfrontality. One factor that may contribute to inconsistent findings is the use of complex and polyfactorial tasks to investigate frontal lobe functioning. In the current study we employed a simple response conflict task during fMRI to examine differences in brain activation between patients experiencing their first-episode of psychosis (n = 33) and age- and sex-matched healthy volunteers (n = 33). We further investigated whether baseline brain activation among patients predicted changes in symptom severity and treatment response following 12 weeks of controlled antipsychotic treatment. During the task subjects were instructed to press a response button on the same side or opposite side of a circle that appeared on either side of a central fixation point. Imaging data revealed that for the contrast of opposite-side vs. same-side, patients showed significantly greater activation compared with healthy volunteers in the anterior cingulate cortex and intraparietal sulcus. Among patients, greater baseline anterior cingulate cortex, temporal-parietal junction, and superior temporal cortex activation predicted greater symptom reduction and therapeutic response following treatment. All findings remained significant after covarying for task performance. Intact performance on this relatively parsimonious task was associated with frontal hyperactivity suggesting the need for patients to utilize greater neural resources to achieve task performance comparable to healthy individuals. Moreover, frontal hyperactivity observed using a simple fMRI task may provide a biomarker for predicting treatment response in first-episode psychosis.

Barriers to Quitting Smoking among Substance Dependent Patients Predict Smoking Cessation Treatment Outcome

Science.gov (United States)

Martin, Rosemarie A.; Cassidy, Rachel; Murphy, Cara M.; Rohsenow, Damaris J.

2016-01-01

For smokers with substance use disorders (SUD), perceived barriers to quitting smoking include concerns unique to effects on sobriety as well as usual concerns. We expanded our Barriers to Quitting Smoking in Substance Abuse Treatment (BQS-SAT) scale, added importance ratings, validated it, and then used the importance scores to predict smoking treatment response in smokers with substance use disorders (SUD) undergoing smoking treatment in residential treatment programs in two studies (n = 184 and 340). Both components (General Barriers, Weight Concerns) were replicated with excellent internal consistency reliability. Construct validity was supported by significant correlations with pretreatment nicotine dependence, smoking variables, smoking self-efficacy, and expected effects of smoking. General Barriers significantly predicted 1-month smoking abstinence, frequency and heaviness, and 3-month smoking frequency; Weight Concerns predicted 1-month smoking frequency. Implications involve addressing barriers with corrective information in smoking treatment for smokers with SUD. PMID:26979552

Ursodeoxycholic acid therapy in intrahepatic cholestasis of pregnancy: Results in real-world conditions and factors predictive of response to treatment.

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Bacq, Yannick; le Besco, Matthieu; Lecuyer, Anne-Isabelle; Gendrot, Chantal; Potin, Jérôme; Andres, Christian R; Aubourg, Alexandre

2017-01-01

Ursodeoxycholic acid (UDCA) therapy is commonly used in intrahepatic cholestasis of pregnancy (ICP). To evaluate the efficacy and tolerance of UDCA in real-world conditions and to search for factors predictive of response to treatment. This observational study included 98 consecutive patients suffering from pruritus during pregnancy associated with increased ALT levels or total bile acid (TBA) concentrations, without other causes of cholestasis. The entire ABCB4 gene coding sequence was analyzed by DNA sequencing. UDCA was prescribed until delivery in all patients (mean dose 14.0mg/kg/day; mean duration 30.4 days). Pruritus improved in 75/98 (76.5%) patients, and totally disappeared before delivery in 25/98 (25.5%). After 2-3 weeks of treatment, ALT levels decreased by more than 50% of base line in 67/86 (77.9%) patients and normalized in 34/86 (39.5%), and TBA concentrations decreased in 28/81 (34.6%). Only one patient stopped the treatment before delivery. On multivariate analysis, ALT >175IU/l before treatment was associated with improvement of pruritus (OR 2.97, 95% CI 1.12-7.89, P=0.029) and with decreased ALT (OR 18.61, 95% CI 3.94-87.99, P=0.0002). ABCB4 gene mutation was not associated with response to treatment. This study supports the use of UDCA as first line therapy in ICP. Copyright © 2016 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

Predicting methylphenidate response in attention deficit hyperactivity disorder: a preliminary study.

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Johnston, Blair A; Coghill, David; Matthews, Keith; Steele, J Douglas

2015-01-01

Methylphenidate (MPH) is established as the main pharmacological treatment for patients with attention deficit hyperactivity disorder (ADHD). Whilst MPH is generally a highly effective treatment, not all patients respond, and some experience adverse reactions. Currently, there is no reliable method to predict how patients will respond, other than by exposure to a trial of medication. In this preliminary study, we sought to investigate whether an accurate predictor of clinical response to methylphenidate could be developed for individual patients, using sociodemographic, clinical and neuropsychological measures. Of the 43 boys with ADHD included in this proof-of-concept study, 30 were classed as responders and 13 as non-responders to MPH, with no significant differences in age nor verbal intelligence quotient (IQ) between the groups. Here we report the application of a multivariate analysis approach to the prediction of clinical response to MPH, which achieved an accuracy of 77% (p = 0.005). The most important variables to the classifier were performance on a 'go/no go' task and comorbid conduct disorder. This preliminary study suggested that further investigation is merited. Achieving a highly significant accuracy of 77% for the prediction of MPH response is an encouraging step towards finding a reliable and clinically useful method that could minimise the number of children needlessly being exposed to MPH. © The Author(s) 2014.

Pharmacogenetics of clozapine treatment response and side-effects in schizophrenia: an update.

Science.gov (United States)

Sriretnakumar, Venuja; Huang, Eric; Müller, Daniel J

2015-01-01

Clozapine (CLZ) is the most effective treatment for treatment-resistant schizophrenia (SCZ) patients, with potential added benefits of reduction in suicide risk and aggression. However, CLZ is also mainly underused due to its high risk for the potentially lethal side-effect of agranulocytosis as well as weight gain and related metabolic dysregulation. Pharmacogenetics promises to enable the prediction of patient treatment response and risk of adverse effects based on patients' genetics, paving the way toward individualized treatment. This article reviews pharmacogenetics studies of CLZ response and side-effects with a focus on articles from January 2012 to February 2015, as an update to the previous reviews. Pharmacokinetic genes explored primarily include CYP1A2, while pharmacodynamic genes consisted of traditional pharmacogenetic targets such as brain-derived neurotrophic factor as well novel mitochondrial genes, NDUFS-1 and translocator protein. Pharmacogenetics is a promising avenue for individualized medication of CLZ in SCZ, with several consistently replicated gene variants predicting CLZ response and side-effects. However, a large proportion of studies have yielded mixed results. Large-scale Genome-wide association studies (e.g., CRESTAR) and targeted gene studies with standardized designs (response measurements, treatment durations, plasma level monitoring) are required for further progress toward clinical translation. Additionally, in order to improve study quality, we recommend accounting for important confounders, including polypharmacy, baseline measurements, treatment duration, gender, and age at onset.

Barriers to Quitting Smoking Among Substance Dependent Patients Predict Smoking Cessation Treatment Outcome.

Science.gov (United States)

Martin, Rosemarie A; Cassidy, Rachel N; Murphy, Cara M; Rohsenow, Damaris J

2016-05-01

For smokers with substance use disorders (SUD), perceived barriers to quitting smoking include concerns unique to effects on sobriety as well as usual concerns. We expanded our Barriers to Quitting Smoking in Substance Abuse Treatment (BQS-SAT) scale, added importance ratings, validated it, and then used the importance scores to predict smoking treatment response in smokers with substance use disorders (SUD) undergoing smoking treatment in residential treatment programs in two studies (n=184 and 340). Both components (general barriers, weight concerns) were replicated with excellent internal consistency reliability. Construct validity was supported by significant correlations with pretreatment nicotine dependence, smoking variables, smoking self-efficacy, and expected effects of smoking. General barriers significantly predicted 1-month smoking abstinence, frequency and heaviness, and 3-month smoking frequency; weight concerns predicted 1-month smoking frequency. Implications involve addressing barriers with corrective information in smoking treatment for smokers with SUD. Copyright © 2016 Elsevier Inc. All rights reserved.

Predicting Treatment Response of Colorectal Cancer Liver Metastases to Conventional Lipiodol-Based Transarterial Chemoembolization Using Diffusion-Weighted MR Imaging: Value of Pretreatment Apparent Diffusion Coefficients (ADC) and ADC Changes Under Therapy

International Nuclear Information System (INIS)

Lahrsow, Maximilian; Albrecht, Moritz H.; Bickford, Matthew W.; Vogl, Thomas J.

2017-01-01

PurposeTo use absolute pretreatment apparent diffusion coefficients (ADC) derived from diffusion-weighted MR imaging (DWI) to predict response to repetitive cTACE for unresectable liver metastases of colorectal carcinoma (CRLM) at 1 and 3 months after start of treatment.Materials and MethodsFifty-five metastases in 34 patients were examined with DWI prior to treatment and 1 month after initial cTACE. Treatment was performed in 4-week intervals. Response was evaluated at 1 and 3 months after start of therapy. Metastases showing a decrease of ≥30% in axial diameter were classified as responding lesions.ResultsOne month after initial cTACE, seven lesions showed early response. There was no significant difference in absolute pretreatment ADC values between responding and non-responding lesions (p = 0.94). Three months after initial cTACE, 17 metastases showed response. There was a significant difference (p = 0.021) between absolute pretreatment ADC values of lesions showing response (median 1.08 × 10 −3  mm 2 /s) and no response (median 1.30 × 10 −3  mm 2 /s). Pretreatment ADC showed fair diagnostic value to predict response (AUC 0.7). Lesions showing response at 3 months also revealed a significant increase in ADC between measurements before treatment and at one month after initial cTACE (p < 0.001). Applying an increase in ADC of 12.17%, response at 3 months after initial cTACE could be predicted with a sensitivity and specificity of 77 and 74%, respectively (AUC 0.817). Furthermore, there was a strong and significant correlation (r = 0.651, p < 0.001) between percentage change in size after third cTACE and percentage change in ADC.ConclusionIn patients with CRLM, ADC measurements are potential biomarkers for assessing response to cTACE.

Predicting Treatment Response of Colorectal Cancer Liver Metastases to Conventional Lipiodol-Based Transarterial Chemoembolization Using Diffusion-Weighted MR Imaging: Value of Pretreatment Apparent Diffusion Coefficients (ADC) and ADC Changes Under Therapy

Energy Technology Data Exchange (ETDEWEB)

Lahrsow, Maximilian, E-mail: mlahrsow@gmail.com; Albrecht, Moritz H. [University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology (Germany); Bickford, Matthew W. [Medical University of South Carolina, Department of Radiology and Radiological Science (United States); Vogl, Thomas J. [University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology (Germany)

2017-06-15

PurposeTo use absolute pretreatment apparent diffusion coefficients (ADC) derived from diffusion-weighted MR imaging (DWI) to predict response to repetitive cTACE for unresectable liver metastases of colorectal carcinoma (CRLM) at 1 and 3 months after start of treatment.Materials and MethodsFifty-five metastases in 34 patients were examined with DWI prior to treatment and 1 month after initial cTACE. Treatment was performed in 4-week intervals. Response was evaluated at 1 and 3 months after start of therapy. Metastases showing a decrease of ≥30% in axial diameter were classified as responding lesions.ResultsOne month after initial cTACE, seven lesions showed early response. There was no significant difference in absolute pretreatment ADC values between responding and non-responding lesions (p = 0.94). Three months after initial cTACE, 17 metastases showed response. There was a significant difference (p = 0.021) between absolute pretreatment ADC values of lesions showing response (median 1.08 × 10{sup −3} mm{sup 2}/s) and no response (median 1.30 × 10{sup −3} mm{sup 2}/s). Pretreatment ADC showed fair diagnostic value to predict response (AUC 0.7). Lesions showing response at 3 months also revealed a significant increase in ADC between measurements before treatment and at one month after initial cTACE (p < 0.001). Applying an increase in ADC of 12.17%, response at 3 months after initial cTACE could be predicted with a sensitivity and specificity of 77 and 74%, respectively (AUC 0.817). Furthermore, there was a strong and significant correlation (r = 0.651, p < 0.001) between percentage change in size after third cTACE and percentage change in ADC.ConclusionIn patients with CRLM, ADC measurements are potential biomarkers for assessing response to cTACE.

Systematic review genetic biomarkers associated with anti-TNF treatment response in inflammatory bowel diseases

DEFF Research Database (Denmark)

Sørensen, Signe Bek; Nielsen, J V; Bo Bojesen, Anders

2016-01-01

BACKGROUND: Personalised medicine, including biomarkers for treatment selection, may provide new algorithms for more effective treatment of patients. Genetic variation may impact drug response and genetic markers could help selecting the best treatment strategy for the individual patient. AIM......2430561) [OR = 1.66 (1.05-2.63)], IL6 (rs10499563) [OR = 1.65 (1.04-2.63)] and IL1B (rs4848306) [OR = 1.88 (1.05-3.35)] were significantly associated with response among IBD patients using clinical response criteria. A positive predictive value of 0.96 was achieved by combining five genetic markers...... in an explorative analysis. CONCLUSIONS: There are no genetic markers currently available which are adequately predictive of anti-TNF response for use in the clinic. Genetic markers bear the advantage that they do not change over time. Therefore, hypothesis-free approaches, testing a large number of polymorphisms...

Barriers to Quitting Smoking among Substance Dependent Patients Predict Smoking Cessation Treatment Outcome

OpenAIRE

Martin, Rosemarie A.; Cassidy, Rachel; Murphy, Cara M.; Rohsenow, Damaris J.

2016-01-01

For smokers with substance use disorders (SUD), perceived barriers to quitting smoking include concerns unique to effects on sobriety as well as usual concerns. We expanded our Barriers to Quitting Smoking in Substance Abuse Treatment (BQS-SAT) scale, added importance ratings, validated it, and then used the importance scores to predict smoking treatment response in smokers with substance use disorders (SUD) undergoing smoking treatment in residential treatment programs in two studies (n = 18...

Bladder cancer treatment response assessment with radiomic, clinical, and radiologist semantic features

Science.gov (United States)

Gordon, Marshall N.; Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Cohan, Richard H.; Caoili, Elaine M.; Paramagul, Chintana; Alva, Ajjai; Weizer, Alon Z.

2018-02-01

We are developing a decision support system for assisting clinicians in assessment of response to neoadjuvant chemotherapy for bladder cancer. Accurate treatment response assessment is crucial for identifying responders and improving quality of life for non-responders. An objective machine learning decision support system may help reduce variability and inaccuracy in treatment response assessment. We developed a predictive model to assess the likelihood that a patient will respond based on image and clinical features. With IRB approval, we retrospectively collected a data set of pre- and post- treatment CT scans along with clinical information from surgical pathology from 98 patients. A linear discriminant analysis (LDA) classifier was used to predict the likelihood that a patient would respond to treatment based on radiomic features extracted from CT urography (CTU), a radiologist's semantic feature, and a clinical feature extracted from surgical and pathology reports. The classification accuracy was evaluated using the area under the ROC curve (AUC) with a leave-one-case-out cross validation. The classification accuracy was compared for the systems based on radiomic features, clinical feature, and radiologist's semantic feature. For the system based on only radiomic features the AUC was 0.75. With the addition of clinical information from examination under anesthesia (EUA) the AUC was improved to 0.78. Our study demonstrated the potential of designing a decision support system to assist in treatment response assessment. The combination of clinical features, radiologist semantic features and CTU radiomic features improved the performance of the classifier and the accuracy of treatment response assessment.

Response-predictive gene expression profiling of glioma progenitor cells in vitro.

Directory of Open Access Journals (Sweden)

Sylvia Moeckel

Full Text Available High-grade gliomas are amongst the most deadly human tumors. Treatment results are disappointing. Still, in several trials around 20% of patients respond to therapy. To date, diagnostic strategies to identify patients that will profit from a specific therapy do not exist.In this study, we used serum-free short-term treated in vitro cell cultures to predict treatment response in vitro. This approach allowed us (a to enrich specimens for brain tumor initiating cells and (b to confront cells with a therapeutic agent before expression profiling.As a proof of principle we analyzed gene expression in 18 short-term serum-free cultures of high-grade gliomas enhanced for brain tumor initiating cells (BTIC before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated progenitor cells allowed to predict therapy-induced impairment of proliferation in vitro.For the tyrosine kinase inhibitor Sunitinib used in this dataset, the approach revealed additional predictive information in comparison to the evaluation of classical signaling analysis.

The bioenergetic signature of isogenic colon cancer cells predicts the cell death response to treatment with 3-bromopyruvate, iodoacetate or 5-fluorouracil.

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Sánchez-Aragó, María; Cuezva, José M

2011-02-08

Metabolic reprogramming resulting in enhanced glycolysis is a phenotypic trait of cancer cells, which is imposed by the tumor microenvironment and is linked to the down-regulation of the catalytic subunit of the mitochondrial H+-ATPase (β-F1-ATPase). The bioenergetic signature is a protein ratio (β-F1-ATPase/GAPDH), which provides an estimate of glucose metabolism in tumors and serves as a prognostic indicator for cancer patients. Targeting energetic metabolism could be a viable alternative to conventional anticancer chemotherapies. Herein, we document that the bioenergetic signature of isogenic colon cancer cells provides a gauge to predict the cell-death response to the metabolic inhibitors, 3-bromopyruvate (3BrP) and iodoacetate (IA), and the anti-metabolite, 5-fluorouracil (5-FU). The bioenergetic signature of the cells was determined by western blotting. Aerobic glycolysis was determined from lactate production rates. The cell death was analyzed by fluorescence microscopy and flow cytometry. Cellular ATP concentrations were determined using bioluminiscence. Pearson's correlation coefficient was applied to assess the relationship between the bioenergetic signature and the cell death response. In vivo tumor regression activities of the compounds were assessed using a xenograft mouse model injected with the highly glycolytic HCT116 colocarcinoma cells. We demonstrate that the bioenergetic signature of isogenic HCT116 cancer cells inversely correlates with the potential to execute necrosis in response to 3BrP or IA treatment. Conversely, the bioenergetic signature directly correlates with the potential to execute apoptosis in response to 5-FU treatment in the same cells. However, despite the large differences observed in the in vitro cell-death responses associated with 3BrP, IA and 5-FU, the in vivo tumor regression activities of these agents were comparable. Overall, we suggest that the determination of the bioenergetic signature of colon carcinomas could

The bioenergetic signature of isogenic colon cancer cells predicts the cell death response to treatment with 3-bromopyruvate, iodoacetate or 5-fluorouracil

Directory of Open Access Journals (Sweden)

Cuezva José M

2011-02-01

Full Text Available Abstract Background Metabolic reprogramming resulting in enhanced glycolysis is a phenotypic trait of cancer cells, which is imposed by the tumor microenvironment and is linked to the down-regulation of the catalytic subunit of the mitochondrial H+-ATPase (β-F1-ATPase. The bioenergetic signature is a protein ratio (β-F1-ATPase/GAPDH, which provides an estimate of glucose metabolism in tumors and serves as a prognostic indicator for cancer patients. Targeting energetic metabolism could be a viable alternative to conventional anticancer chemotherapies. Herein, we document that the bioenergetic signature of isogenic colon cancer cells provides a gauge to predict the cell-death response to the metabolic inhibitors, 3-bromopyruvate (3BrP and iodoacetate (IA, and the anti-metabolite, 5-fluorouracil (5-FU. Methods The bioenergetic signature of the cells was determined by western blotting. Aerobic glycolysis was determined from lactate production rates. The cell death was analyzed by fluorescence microscopy and flow cytometry. Cellular ATP concentrations were determined using bioluminiscence. Pearson's correlation coefficient was applied to assess the relationship between the bioenergetic signature and the cell death response. In vivo tumor regression activities of the compounds were assessed using a xenograft mouse model injected with the highly glycolytic HCT116 colocarcinoma cells. Results We demonstrate that the bioenergetic signature of isogenic HCT116 cancer cells inversely correlates with the potential to execute necrosis in response to 3BrP or IA treatment. Conversely, the bioenergetic signature directly correlates with the potential to execute apoptosis in response to 5-FU treatment in the same cells. However, despite the large differences observed in the in vitro cell-death responses associated with 3BrP, IA and 5-FU, the in vivo tumor regression activities of these agents were comparable. Conclusions Overall, we suggest that the

Predicting Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer with Textural Features Derived from Pretreatment F-18-FDG PET/CT Imaging

NARCIS (Netherlands)

Beukinga, Roelof J.; Hulshoff, Jan B.; van Dijk, Lisanne V.; Muijs, Christina T.; Burgerhof, Johannes G. M.; Kats-Ugurlu, Gursah; Slart, Riemer H. J. A.; Slump, Cornelis H.; Mul, Veronique E. M.; Plukker, John Th. M.

Adequate prediction of tumor response to neoadjuvant chemoradiotherapy (nCRT) in esophageal cancer (EC) patients is important in a more personalized treatment. The current best clinical method to predict pathologic complete response is SUVmax in F-18-FDG PET/ CT imaging. To improve the prediction of

18F-fluorodeoxyglucose positron emission tomography for predicting tumor response to radiochemotherapy in nasopharyngeal carcinoma

International Nuclear Information System (INIS)

Su, Meng; Wei, Hangping; Lin, Ruifang; Zhang, Xuebang; Zou, Changlin; Zhao, Liang

2015-01-01

The aim of this study was to evaluate the value of 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in predicting tumor response to radiochemotherapy in nasopharyngeal carcinoma (NPC). From July 2012 to March 2014, 46 NPC patients who had undergone PET scanning before receiving definitive intensity-modulated radiotherapy (IMRT) treatment in our hospital were enrolled. Factors potentially affecting tumor response to treatment were studied by multiple logistic regression analysis. After radiochemotherapy, 32 patients had a clinical complete response (CR), making the CR rate 69.6 %. Multiple logistic regression analysis demonstrated that the maximal standard uptake value (SUV max ) of the primary tumor was the only factor related to tumor response (p = 0.001), and that the logistic model had a high positive predictive value (90.6 %). The area under the receiver operating characteristic (ROC) curve was 0.809, with a best cutoff threshold at 10.05. Patients with SUV max ≤ 10 had a higher CR rate than those with SUV max > 10 (p < 0.001). The SUV max of the primary tumor before treatment is an independent predictor of tumor response in NPC. (orig.) [de

Subjective response as a consideration in the pharmacogenetics of alcoholism treatment.

Science.gov (United States)

Roche, Daniel Jo; Ray, Lara A

2015-01-01

Currently available pharmacological treatments for alcoholism have modest efficacy and high individual variability in treatment outcomes, both of which have been partially attributed to genetic factors. One path to reducing the variability and improving the efficacy associated with these pharmacotherapies may be to identify overlapping genetic contributions to individual differences in both subjective responses to alcohol and alcoholism pharmacotherapy outcomes. As acute subjective response to alcohol is highly predictive of future alcohol related problems, identifying such shared genetic mechanisms may inform the development of personalized treatments that can effectively target converging pathophysiological mechanisms that convey risk for alcoholism. The focus of this review is to revisit the association between subjective response to alcohol and the etiology of alcoholism while also describing genetic contributions to this relationship, discuss potential pharmacogenetic approaches to target subjective response to alcohol in order to improve the treatment of alcoholism and examine conceptual and methodological issues associated with these topics, and outline future approaches to overcome these challenges.

HPA axis response to psychological stress and treatment retention in residential substance abuse treatment: a prospective study.

Science.gov (United States)

Daughters, Stacey B; Richards, Jessica M; Gorka, Stephanie M; Sinha, Rajita

2009-12-01

Substance abuse treatment programs are often characterized by high rates of premature treatment dropout, which increases the likelihood of relapse to drug use. Negative reinforcement models of addiction emphasize an individual's inability to tolerate stress as a key factor for understanding poor substance use treatment outcomes, and evidence indicates that dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis contributes to an individual's inability to respond adaptively to stress. The aim of the current study was to examine whether HPA axis response to stress is predictive of treatment retention among a sample of drug users in residential substance abuse treatment. Prospective study assessing treatment retention among 102 individuals enrolled in residential substance abuse treatment. Participants completed two computerized stress tasks, and HPA axis response to stress was measured via salivary cortisol at five time points from baseline (pre-stress) to 30 min post-stress exposure. The main outcome measures were treatment dropout (categorical) and total number of days in treatment (continuous). A significantly higher salivary cortisol response to stress was observed in treatment dropouts compared to treatment completers. Further, Cox proportional hazards survival analyses indicated that a higher peak cortisol response to stress was associated with a shorter number of days to treatment dropout. Results indicate that a higher salivary cortisol level in response to stress is associated with an inability to remain in substance abuse treatment. These findings are the first to document a biological marker of stress as a predictor of substance abuse treatment dropout, and support the development and implementation of treatments targeting this vulnerability.

Predictive Factors of Clinical Response of Infliximab Therapy in Active Nonradiographic Axial Spondyloarthritis Patients

Directory of Open Access Journals (Sweden)

Zhiming Lin

2015-01-01

Full Text Available Objectives. To evaluate the efficiency and the predictive factors of clinical response of infliximab in active nonradiographic axial spondyloarthritis patients. Methods. Active nonradiographic patients fulfilling ESSG criteria for SpA but not fulfilling modified New York criteria were included. All patients received infliximab treatment for 24 weeks. The primary endpoint was ASAS20 response at weeks 12 and 24. The abilities of baseline parameters and response at week 2 to predict ASAS20 response at weeks 12 and 24 were assessed using ROC curve and logistic regression analysis, respectively. Results. Of 70 axial SpA patients included, the proportions of patients achieving an ASAS20 response at weeks 2, 6, 12, and 24 were 85.7%, 88.6%, 87.1%, and 84.3%, respectively. Baseline MRI sacroiliitis score (AUC = 0.791; P=0.005, CRP (AUC = 0.75; P=0.017, and ASDAS (AUC = 0.778, P=0.007 significantly predicted ASAS20 response at week 12. However, only ASDAS (AUC = 0.696, P=0.040 significantly predicted ASAS20 response at week 24. Achievement of ASAS20 response after the first infliximab infusion was a significant predictor of subsequent ASAS20 response at weeks 12 and 24 (wald χ2=6.87, P=0.009, and wald χ2=5.171, P=0.023. Conclusions. Infliximab shows efficiency in active nonradiographic axial spondyloarthritis patients. ASDAS score and first-dose response could help predicting clinical efficacy of infliximab therapy in these patients.

Race, Genetic Ancestry and Response to Antidepressant Treatment for Major Depression

Science.gov (United States)

Murphy, Eleanor; Hou, Liping; Maher, Brion S; Woldehawariat, Girma; Kassem, Layla; Akula, Nirmala; Laje, Gonzalo; McMahon, Francis J

2013-01-01

The Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Study revealed poorer antidepressant treatment response among black compared with white participants. This racial disparity persisted even after socioeconomic and baseline clinical factors were taken into account. Some studies have suggested genetic contributions to this disparity, but none have attempted to disentangle race and genetic ancestry. Here we used genome-wide single-nucleotide polymorphism (SNP) data to examine independent contributions of race and genetic ancestry to citalopram response. Secondary data analyses included 1877 STAR*D participants who completed an average of 10 weeks of citalopram treatment and provided DNA samples. Participants reported their race as White (n=1464), black (n=299) or other/mixed (n=114). Genetic ancestry was estimated by multidimensional scaling (MDS) analyses of about 500 000 SNPs. Ancestry proportions were estimated by STRUCTURE. Structural equation modeling was used to examine the direct and indirect effects of observed and latent predictors of response, defined as change in the Quick Inventory of Depressive Symptomatology (QIDS) score from baseline to exit. Socioeconomic and baseline clinical factors, race, and anxiety significantly predicted response, as previously reported. However, direct effects of race disappeared in all models that included genetic ancestry. Genetic African ancestry predicted lower treatment response in all models. Although socioeconomic and baseline clinical factors drive racial differences in antidepressant response, genetic ancestry, rather than self-reported race, explains a significant fraction of the residual differences. Larger samples would be needed to identify the specific genetic mechanisms that may be involved, but these findings underscore the importance of including more African-American patients in drug trials. PMID:23827886

Callous-unemotional traits and early life stress predict treatment effects on stress and sex hormone functioning in incarcerated male adolescents.

Science.gov (United States)

Johnson, Megan; Vitacco, Michael J; Shirtcliff, Elizabeth A

2018-03-01

The stress response system is highly plastic, and hormone rhythms may "adaptively calibrate" in response to treatment. This investigation assessed whether stress and sex hormone diurnal rhythms changed over the course of behavioral treatment, and whether callous-unemotional (CU) traits and history of early adversity affected treatment results on diurnal hormone functioning in a sample of 28 incarcerated adolescent males. It was hypothesized that the treatment would have beneficial effects, such that healthier diurnal rhythms would emerge post-treatment. Diurnal cortisol, testosterone, and dehydroepiandrosterone (DHEA) were sampled two weeks after admission to the correctional/treatment facility, and again approximately four months later. Positive treatment effects were detected for the whole sample, such that testosterone dampened across treatment. CU traits predicted a non-optimal hormone response to treatment, potentially indicating biological preparedness to respond to acts of social dominance and aggression. The interaction between CU traits and adversity predicted a promising and sensitized response to treatment including increased cortisol and a steeper testosterone drop across treatment. Results suggest that stress and sex hormones are highly receptive to treatment during this window of development.

Fourier and non-Fourier bio-heat transfer models to predict ex vivo temperature response to focused ultrasound heating

Science.gov (United States)

Li, Chenghai; Miao, Jiaming; Yang, Kexin; Guo, Xiasheng; Tu, Juan; Huang, Pintong; Zhang, Dong

2018-05-01

Although predicting temperature variation is important for designing treatment plans for thermal therapies, research in this area is yet to investigate the applicability of prevalent thermal conduction models, such as the Pennes equation, the thermal wave model of bio-heat transfer, and the dual phase lag (DPL) model. To address this shortcoming, we heated a tissue phantom and ex vivo bovine liver tissues with focused ultrasound (FU), measured the temperature response, and compared the results with those predicted by these models. The findings show that, for a homogeneous-tissue phantom, the initial temperature increase is accurately predicted by the Pennes equation at the onset of FU irradiation, although the prediction deviates from the measured temperature with increasing FU irradiation time. For heterogeneous liver tissues, the predicted response is closer to the measured temperature for the non-Fourier models, especially the DPL model. Furthermore, the DPL model accurately predicts the temperature response in biological tissues because it increases the phase lag, which characterizes microstructural thermal interactions. These findings should help to establish more precise clinical treatment plans for thermal therapies.

Predictive value of brain perfusion SPECT for rTMS response in pharmacoresistant depression

International Nuclear Information System (INIS)

Richieri, Raphaelle; Lancon, Christophe; Boyer, Laurent; Farisse, Jean; Colavolpe, Cecile; Mundler, Olivier; Guedj, Eric

2011-01-01

The aim of this study was to determine the predictive value of whole-brain voxel-based regional cerebral blood flow (rCBF) for repetitive transcranial magnetic stimulation (rTMS) response in patients with pharmacoresistant depression. Thirty-three right-handed patients who met DSM-IV criteria for major depressive disorder (unipolar or bipolar depression) were included before rTMS. rTMS response was defined as at least 50% reduction in the baseline Beck Depression Inventory scores. The predictive value of 99m Tc-ethyl cysteinate dimer (ECD) single photon emission computed tomography (SPECT) for rTMS response was studied before treatment by comparing rTMS responders to non-responders at voxel level using Statistical Parametric Mapping (SPM) (p 0.10). In comparison to responders, non-responders showed significant hypoperfusions (p < 0.001, uncorrected) in the left medial and bilateral superior frontal cortices (BA10), the left uncus/parahippocampal cortex (BA20/BA35) and the right thalamus. The area under the curve for the combination of SPECT clusters to predict rTMS response was 0.89 (p < 0.001). Sensitivity, specificity, positive predictive value and negative predictive value for the combination of clusters were: 94, 73, 81 and 92%, respectively. This study shows that, in pharmacoresistant depression, pretreatment rCBF of specific brain regions is a strong predictor for response to rTMS in patients with homogeneous demographic/clinical features. (orig.)

Prediction of methylphenidate treatment outcome in adults with attention-deficit/hyperactivity disorder (ADHD).

Science.gov (United States)

Retz, Wolfgang; Retz-Junginger, Petra

2014-11-01

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent mental disorder of childhood, which often persists in adulthood. Methylphenidate (MPH) is one of the most effective medications to treat ADHD, but also few adult patients show no sufficient response to this drug. In this paper, we give an overview regarding genetic, neuroimaging, clinical and other studies which have tried to reveal the reasons for non-response in adults with ADHD, based on a systematic literature search. Although MPH is a well-established treatment for adults with ADHD, research regarding the prediction of treatment outcome is still limited and has resulted in inconsistent findings. No reliable neurobiological markers of treatment response have been identified so far. Some findings from clinical studies suggest that comorbidity with substance use disorders and personality disorders has an impact on treatment course and outcome. As MPH is widely used in the treatment of adults with ADHD, much more work is needed regarding positive and negative predictors of long-term treatment outcome in order to optimize the pharmacological treatment of adult ADHD patients.

Nonverbal interpersonal attunement and extravert personality predict outcome of light treatment in seasonal affective disorder

NARCIS (Netherlands)

Geerts, E; Kouwert, E; Bouhuys, N; Meesters, Y; Jansen, J

We investigated whether personality and nonverbal interpersonal processes can predict the subsequent response to light treatment in seasonal affective disorder (SAD) patients. In 60 SAD patients, Neuroticism and Extraversion were assessed prior to light treatment (4 days with 30 min of 10.000 lux).

Interpersonal impacts mediate the association between personality and treatment response in major depression.

Science.gov (United States)

Dermody, Sarah S; Quilty, Lena C; Bagby, R Michael

2016-07-01

Personality, as characterized by the Five-Factor Model, predicts response to psychotherapy for depression. To explain how personality impacts treatment response, the present study investigated patient and therapist interpersonal processes in treatment sessions as an explanatory pathway. A clinical trial was conducted in which 103 outpatients (mean age: 41.17 years, 65% female) with primary major depressive disorder completed 16-20 weeks of cognitive-behavioral or interpersonal therapy. Before treatment, patients completed the Revised NEO Personality Inventory to assess personality domains (neuroticism, extraversion, openness-to-experience, agreeableness, and conscientiousness). After 3 and 13 weeks, patient interpersonal behavior was rated by the therapist and vice versa to determine levels of patient and therapist communal and agentic behaviors. Depression levels were measured before and after treatment. Structural equation modeling supported that patients' interpersonal behavior during therapy mediated the associations between pretreatment personality and depression treatment outcome. Specifically, extraversion, conscientiousness, and neuroticism (inverse) predicted higher levels of patient communion throughout treatment, which was in turn associated with improved treatment outcomes. Furthermore, patient agreeableness was inversely associated with agency throughout treatment, which was linked to poorer treatment response. Therapist interpersonal behavior was not a significant mediator. Results suggest that patient interpersonal behavior during treatment may be one way that patient personality impacts clinical outcomes in depression. Results underscore the clinical utility of Five-Factor Model domains in treatment process and outcome. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Incorporating Psychological Predictors of Treatment Response into Health Economic Simulation Models: A Case Study in Type 1 Diabetes.

Science.gov (United States)

Kruger, Jen; Pollard, Daniel; Basarir, Hasan; Thokala, Praveen; Cooke, Debbie; Clark, Marie; Bond, Rod; Heller, Simon; Brennan, Alan

2015-10-01

. Health economic modeling has paid limited attention to the effects that patients' psychological characteristics have on the effectiveness of treatments. This case study tests 1) the feasibility of incorporating psychological prediction models of treatment response within an economic model of type 1 diabetes, 2) the potential value of providing treatment to a subgroup of patients, and 3) the cost-effectiveness of providing treatment to a subgroup of responders defined using 5 different algorithms. . Multiple linear regressions were used to investigate relationships between patients' psychological characteristics and treatment effectiveness. Two psychological prediction models were integrated with a patient-level simulation model of type 1 diabetes. Expected value of individualized care analysis was undertaken. Five different algorithms were used to provide treatment to a subgroup of predicted responders. A cost-effectiveness analysis compared using the algorithms to providing treatment to all patients. . The psychological prediction models had low predictive power for treatment effectiveness. Expected value of individualized care results suggested that targeting education at responders could be of value. The cost-effectiveness analysis suggested, for all 5 algorithms, that providing structured education to a subgroup of predicted responders would not be cost-effective. . The psychological prediction models tested did not have sufficient predictive power to make targeting treatment cost-effective. The psychological prediction models are simple linear models of psychological behavior. Collection of data on additional covariates could potentially increase statistical power. . By collecting data on psychological variables before an intervention, we can construct predictive models of treatment response to interventions. These predictive models can be incorporated into health economic models to investigate more complex service delivery and reimbursement strategies. �

Predictive value of early viriological response for sustained viriological response in chronic hepatitis c with conventional interferon therapy

International Nuclear Information System (INIS)

Awan, A.; Umar, M.; Khaar, H.T.B.; Kulsoom, A.; Minhas, Z.; Ambreen, S.; Habib, N.; Mumtaz, W.; Habib, F.

2016-01-01

Background: Hepatitis is a major public health problem in Pakistan due to its strong association with liver failure and hepatocellular carcinoma. In Pakistan, conventional interferon therapy along with Ribavirin is favoured especially in Government funded programs for treatment of Hepatitis C, over the more expensive Pegylated Interferon and Ribavirin combination therapy as recommended by Pakistan society of Gastroenterology and GI endoscopy due to its favourable results observed in genotype 3 which is the dominant genotype of this region. Objective of our study was to assess the viriological responses with standard interferon therapy and to determine the predictive values of early viriological response (EVR) for Sustained Viriological Response (SVR) in chronic hepatitis C patients treated with standard interferon therapy. Methods: A cross sectional study was conducted on patients with chronic hepatitis C having received standard interferon and ribavirin therapy for six months. EVR and SVR were noted for analysis. Positive and negative predictive values of EVR on SVR were calculated. Results: Out of the total sample (N=3075), 1946 (63.3 percentage) patients were tested for EVR. 1386 (71.2 percentage) were positive while 560 (28.8 percentage) were negative while 516 (16.8 percentage) were tested for SVR. Two hundred and eighty-five (55.2 percentage) were positive while 231 (44.8 percentage) were negative. EVR and SVR tested were N=117. Positive predictive value of EVR on SVR was 67.1 percentage and negative predictive value was 65.8 percentage. Statistically significant association between EVR and SVR was determined with Chi square statistic of 11.8 (p-value <0.0001). Conclusion: EVR is a good predictor of response of patients to standard interferon and ribavirin therapy. In the absence of an EVR, it seems imperative to stop further treatment. Virilogical responses with conventional interferon therapy are comparable to those of pegylated interferon therapy so

Effect of breast cancer phenotype on diagnostic performance of MRI in the prediction to response to neoadjuvant treatment

Energy Technology Data Exchange (ETDEWEB)

Bufi, Enida, E-mail: reagandus@alice.it [Department of Bioimaging and Radiological Sciences, Catholic University, Rome (Italy); Belli, Paolo; Di Matteo, Marialuisa [Department of Bioimaging and Radiological Sciences, Catholic University, Rome (Italy); Terribile, Daniela; Franceschini, Gianluca [Department of Surgery, Breast Unit, Catholic University, Rome (Italy); Nardone, Luigia [Department of Radiotherapy, Catholic University, Rome (Italy); Petrone, Gianluigi [Department of Pathology, Catholic University, Rome (Italy); Bonomo, Lorenzo [Department of Bioimaging and Radiological Sciences, Catholic University, Rome (Italy)

2014-09-15

Aim: The estimation of response to neoadjuvant chemotherapy (NAC) is useful in the surgical decision in breast cancer. We addressed the diagnostic reliability of conventional MRI, of diffusion weighted imaging (DWI) and of a merged criterion coupling morphological MRI and DWI. Diagnostic performance was analysed separately in different tumor subtypes, including HER2+ (human epidermal growth factor receptor 2)/HR+ (hormone receptor) (hybrid phenotype). Materials and methods: Two-hundred and twenty-five patients underwent MRI before and after NAC. The response to treatment was defined according to the RECIST classification and the evaluation of DWI with apparent diffusion coefficient (ADC). The complete pathological response – pCR was assessed (Mandard classification). Results: Tumor phenotypes were Luminal (63.6%), Triple Negative (16.4%), HER2+ (7.6%) or Hybrid (12.4%). After NAC, pCR was observed in 17.3% of cases. Average ADC was statistically higher after NAC (p < 0.001) among patients showing pCR vs. those who had not pCR. The RECIST classification showed adequate performance in predicting the pCR in Triple Negative (area under the receiver operating characteristic curve, ROC AUC = 0.9) and in the HER2+ subgroup (AUC = 0.826). Lower performance was found in the Luminal and Hybrid subgroups (AUC 0.693 and 0.611, respectively), where the ADC criterion yielded an improved performance (AUC = 0.787 and 0.722). The coupling of morphological and DWI criteria yielded maximally improved performance in the Luminal and Hybrid subgroups (AUC = 0.797 and 0.761). Conclusion: The diagnostic reliability of MRI in predicting the pCR to NAC depends on the tumor phenotype, particularly in the Luminal and Hybrid subgroups. In these cases, the coupling of morphological MRI evaluation and DWI assessment may facilitate the diagnosis.

Immunological correlates of treatment and response in stage IV malignant melanoma patients treated with Ipilimumab

DEFF Research Database (Denmark)

Bjoern, Jon; Juul Nitschke, Nikolaj; Zeeberg Iversen, Trine

2016-01-01

Introduction: Ipilimumab is effective in the treatment of metastatic malignant melanoma, but few biomarkers reliably predict treatment response. Methods: Patients were treated with Ipilimumab for metastatic malignant melanoma. Blood and serum samples were collected before and during treatment. Mo...

Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data.

Science.gov (United States)

Redlich, Ronny; Opel, Nils; Grotegerd, Dominik; Dohm, Katharina; Zaremba, Dario; Bürger, Christian; Münker, Sandra; Mühlmann, Lisa; Wahl, Patricia; Heindel, Walter; Arolt, Volker; Alferink, Judith; Zwanzger, Peter; Zavorotnyy, Maxim; Kugel, Harald; Dannlowski, Udo

2016-06-01

Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depression. However, biomarkers that accurately predict a response to ECT remain unidentified. To investigate whether certain factors identified by structural magnetic resonance imaging (MRI) techniques are able to predict ECT response. In this nonrandomized prospective study, gray matter structure was assessed twice at approximately 6 weeks apart using 3-T MRI and voxel-based morphometry. Patients were recruited through the inpatient service of the Department of Psychiatry, University of Muenster, from March 11, 2010, to March 27, 2015. Two patient groups with acute major depressive disorder were included. One group received an ECT series in addition to antidepressants (n = 24); a comparison sample was treated solely with antidepressants (n = 23). Both groups were compared with a sample of healthy control participants (n = 21). Binary pattern classification was used to predict ECT response by structural MRI that was performed before treatment. In addition, univariate analysis was conducted to predict reduction of the Hamilton Depression Rating Scale score by pretreatment gray matter volumes and to investigate ECT-related structural changes. One participant in the ECT sample was excluded from the analysis, leaving 67 participants (27 men and 40 women; mean [SD] age, 43.7 [10.6] years). The binary pattern classification yielded a successful prediction of ECT response, with accuracy rates of 78.3% (18 of 23 patients in the ECT sample) and sensitivity rates of 100% (13 of 13 who responded to ECT). Furthermore, a support vector regression yielded a significant prediction of relative reduction in the Hamilton Depression Rating Scale score. The principal findings of the univariate model indicated a positive association between pretreatment subgenual cingulate volume and individual ECT response (Montreal Neurological Institute [MNI] coordinates x = 8, y = 21, z = -18

Predicting response to physiotherapy treatment for musculoskeletal shoulder pain: protocol for a longitudinal cohort study

Science.gov (United States)

2013-01-01

Background Shoulder pain affects all ages, with a lifetime prevalence of one in three. The most effective treatment is not known. Physiotherapy is often recommended as the first choice of treatment. At present, it is not possible to identify, from the initial physiotherapy assessment, which factors predict the outcome of physiotherapy for patients with shoulder pain. The primary objective of this study is to identify which patient characteristics and baseline measures, typically assessed at the first physiotherapy appointment, are related to the functional outcome of shoulder pain 6 weeks and 6 months after starting physiotherapy treatment. Methods/Design Participants with musculoskeletal shoulder pain of any duration will be recruited from participating physiotherapy departments. For this longitudinal cohort study, the participants care pathway, including physiotherapy treatment will be therapist determined. Potential prognostic variables will be collected from participants during their first physiotherapy appointment and will include demographic details, lifestyle, psychosocial factors, shoulder symptoms, general health, clinical examination, activity limitations and participation restrictions. Outcome measures (Shoulder Pain and Disability Index, Quick Disability of the Arm, Shoulder and Hand, and Global Impression of Change) will be collected by postal self-report questionnaires 6 weeks and 6 months after commencing physiotherapy. Details of attendance and treatment will be collected by the treating physiotherapist. Participants will be asked to complete an exercise dairy. An initial exploratory analysis will assess the relationship between potential prognostic factors at baseline and outcome using univariate statistical tests. Those factors significant at the 5% level will be further considered as prognostic factors using a general linear model. It is estimated that 780 subjects will provide more than 90% power to detect an effect size of less than 0

The value of perfusion CT in predicting the short-term response to synchronous radiochemotherapy for cervical squamous cancer

International Nuclear Information System (INIS)

Li, Xiang Sheng; Fan, Hong Xia; Zhu, Hong Xian; Song, Yun Long; Zhou, Chun Wu

2012-01-01

To determine the value of the perfusion parameters in predicting short-term tumour response to synchronous radiochemotherapy for cervical squamous carcinoma. Ninety-three patients with cervical squamous carcinoma later than stage IIB were included in this study. Perfusion CT was performed for all these patients who subsequently received the same synchronous radiochemotherapy. The patients were divided into responders and non-responders according to short-term response to treatment. Baseline perfusion parameters of the two groups were compared. The perfusion parameters that might affect treatment effect were analysed by using a multivariate multi-regression analysis. The responders group had higher baseline permeability-surface area product (PS) and blood volume (BV) values than the non-responders group (P 0.05). At multivariate multi-regression analysis, BV, PS and tumour size were significant factors in the prediction of treatment effect. Small tumours usually had high PS and BV values, and thus had a good treatment response. Perfusion CT can provide some helpful information for the prediction of the short-term effect. Synchronous radiochemotherapy may be more effective in cervical squamous carcinoma with higher baseline PS and BV. (orig.)

Transcription-based prediction of response to IFNbeta using supervised computational methods.

Directory of Open Access Journals (Sweden)

Sergio E Baranzini

2005-01-01

Full Text Available Changes in cellular functions in response to drug therapy are mediated by specific transcriptional profiles resulting from the induction or repression in the activity of a number of genes, thereby modifying the preexisting gene activity pattern of the drug-targeted cell(s. Recombinant human interferon beta (rIFNbeta is routinely used to control exacerbations in multiple sclerosis patients with only partial success, mainly because of adverse effects and a relatively large proportion of nonresponders. We applied advanced data-mining and predictive modeling tools to a longitudinal 70-gene expression dataset generated by kinetic reverse-transcription PCR from 52 multiple sclerosis patients treated with rIFNbeta to discover higher-order predictive patterns associated with treatment outcome and to define the molecular footprint that rIFNbeta engraves on peripheral blood mononuclear cells. We identified nine sets of gene triplets whose expression, when tested before the initiation of therapy, can predict the response to interferon beta with up to 86% accuracy. In addition, time-series analysis revealed potential key players involved in a good or poor response to interferon beta. Statistical testing of a random outcome class and tolerance to noise was carried out to establish the robustness of the predictive models. Large-scale kinetic reverse-transcription PCR, coupled with advanced data-mining efforts, can effectively reveal preexisting and drug-induced gene expression signatures associated with therapeutic effects.

Predicting response to cognitive therapy and interpersonal therapy, with or without antidepressant medication, for major depression: a pragmatic trial in routine practice

NARCIS (Netherlands)

Huibers, M.J.H.; van Breukelen, G.; Roelofs, J.; Hollon, S.D.; Markowitz, J.C.; van Os, J.; Arntz, A.; Peeters, F.

2014-01-01

Background: Identifying patient characteristics that predict response within treatments (prognostic) or between treatments (prescriptive) can inform clinical decision-making. In this study, we sought to identify predictors of response to evidence-based treatments in a sample of depressed patients

Predicting response to cognitive therapy and interpersonal therapy, with or without antidepressant medication, for major depression: A pragmatic trial in routine practice

NARCIS (Netherlands)

Huibers, M.J.H.; van Breukelen, G.J.; Roelofs, J.T.H.; Hollon, S.D.; Markowitz, J.C.; Os, J. V.; Arntz, A.; Peeters, F.

2014-01-01

Background Identifying patient characteristics that predict response within treatments (prognostic) or between treatments (prescriptive) can inform clinical decision-making. In this study, we sought to identify predictors of response to evidence-based treatments in a sample of depressed patients

Early prediction of response to cetuximab and radiotherapy by FDG-PET/CT for the treatment of a locoregionally advanced squamous cell carcinoma of the hypopharynx

Directory of Open Access Journals (Sweden)

Mindaugas Grybauskas

2014-01-01

Full Text Available Cetuximab (CTX is used for the concurrent treatment with radiotherapy (RT in squamous cell carcinoma of head and neck (HNSCC. There are no reliable clinical predictive markers of effectiveness of CTX at yet. We describe the clinical case of patient who received a CTX/RT to cure locoregionally advanced hypopharyngeal SCC. 2-Deoxy-2-[18F]fluoro-d-glucose positron emission tomography and computed tomography (18FDG-PET/CT was performed before the treatment and repeated 10 days after CTX induction dose. A repeated 18FDG-PET/CT scan showed dramatic decrease of metabolic parameters. Patient had a complete response after treatment and is still alive and cured after 5 years.

Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy.

Science.gov (United States)

Mani, Subramani; Chen, Yukun; Li, Xia; Arlinghaus, Lori; Chakravarthy, A Bapsi; Abramson, Vandana; Bhave, Sandeep R; Levy, Mia A; Xu, Hua; Yankeelov, Thomas E

2013-01-01

To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC). Quantitative dynamic contrast-enhanced MRI and diffusion-weighted MRI data were acquired on 28 patients before and after one cycle of NAC. A total of 118 semiquantitative and quantitative parameters were derived from these data and combined with 11 clinical variables. We used Bayesian logistic regression in combination with feature selection using a machine learning framework for predictive model building. The best predictive models using feature selection obtained an area under the curve of 0.86 and an accuracy of 0.86, with a sensitivity of 0.88 and a specificity of 0.82. With the numerous options for NAC available, development of a method to predict response early in the course of therapy is needed. Unfortunately, by the time most patients are found not to be responding, their disease may no longer be surgically resectable, and this situation could be avoided by the development of techniques to assess response earlier in the treatment regimen. The method outlined here is one possible solution to this important clinical problem. Predictive modeling approaches based on machine learning using readily available clinical and quantitative MRI data show promise in distinguishing breast cancer responders from non-responders after the first cycle of NAC.

Prediction of immunophenotype, treatment response, and relapse in childhood acute lymphoblastic leukemia using DNA microarrays

DEFF Research Database (Denmark)

Willenbrock, Hanni; Juncker, Agnieszka; Schmiegelow, K.

2004-01-01

Gene expression profiling is a promising tool for classification of pediatric acute lymphoblastic leukemia ( ALL). We analyzed the gene expression at the time of diagnosis for 45 Danish children with ALL. The prediction of 5-year event-free survival or relapse after treatment by NOPHO-ALL92 or 2000...

Hypothyroidism as a predictive clinical marker of better treatment response to sunitinib therapy.

Science.gov (United States)

Kust, Davor; Prpić, Marin; Murgić, Jure; Jazvić, Marijana; Jakšić, Blanka; Krilić, Dražena; Bolanča, Ante; Kusić, Zvonko

2014-06-01

Tyrosine kinase inhibitors are standard treatment in patients with metastatic renal cell carcinoma (mRCC). Several studies have indicated that side-effects including hypothyroidism may serve as potential predictive biomarkers of treatment efficacy. All patients with clear cell mRCC treated with sunitinib in the first-line setting in our Center between November 2008 and October 2013 were included. Thyroid function was assessed after every 2 cycles. Prognostic factors were tested using Cox proportional hazards model for univariate analysis. During treatment, 29.3% developed hypothyroidism, with a median of peak TSH values of 34.4 mIU/L. Patients who had both TSH >4 mIU/L and were receiving substitution therapy with levothyroxine had prolonged PFS compared to all other patients (25.3 months vs. 9.0 months; p=0.042). The rate of hypothyroidism as a side-effect of sunitinib in patients with mRCC is significant. Patients with symptomatic hypothyroidism experienced significantly longer PFS, but without difference in OS. Copyright© 2014 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Role of scintigraphy with {sup 99m}Tc-infliximab in predicting the response of intraarticular infliximab treatment in patients with refractory monoarthritis

Energy Technology Data Exchange (ETDEWEB)

Conti, F.; Ceccarelli, F.; Priori, R.; Iagnocco, A.; Valesini, G. [University of Rome, Rheumatology Unit, Faculty of Medicine and Dentistry, Rome (Italy); Malviya, G.; Signore, A. [University of Rome, Nuclear Medicine Unit, Faculty of Medicine and Psychology, Rome (Italy)

2012-08-15

The rationale for the present study was to evaluate the predictive role of {sup 99m}Tc-infliximab scintigraphy in therapy decision-making in patients with refractory monoarthritis and also candidates for intraarticular (IA) infliximab treatment. We studied 12 patients (5 with rheumatoid arthritis and 7 with spondyloarthropathy) with active monoarthritis (11 knees and 1 ankle) that had lasted for at least 3 months. Patients were evaluated clinically and ultrasonographically at baseline and 12 weeks after IA administration of infliximab. At the same time-points, {sup 99m}Tc-infliximab scintigraphy was performed: planar anterior and posterior images of arthritic joints were acquired at 6 and 20 h after injection and target-to-background (T/B) ratios were calculated. After treatment, a significant improvement in clinical and ultrasonographic parameters was recorded in six patients. Three patients had a partial response and three did not respond. Regarding scintigraphic evaluation, the T/B ratio analysis showed a significantly higher uptake in affected than in nonaffected joints before therapy (1.78 {+-} 0.46 vs. 1.29 {+-} 0.27, p = 0.006 at 6 h; 2.05 {+-} 0.50 vs. 1.41 {+-} 0.36 at 20 h, p = 0.002), and mean uptake at 20 h was also significantly higher than at 6 h (p = 0.0004). Scintigraphy showed a significant decrease in posttherapy T/B ratios of the affected joints (p = 0.0001 at 6 h and p = 0.0001 at 20 h), indicating a reduction in TNF into the affected joints. Most importantly, responders showed a significantly higher percentage increase in pretherapy uptake from 6 h to 20 h in the affected joints than nonresponders (p = 0.00001). The results of the present investigation suggest that {sup 99m}Tc-infliximab scintigraphy could be a useful tool to predict the clinical response to IA infliximab treatment in patients with refractory monoarthritis. (orig.)

Using Flow Characteristics in Three-Dimensional Power Doppler Ultrasound Imaging to Predict Complete Responses in Patients Undergoing Neoadjuvant Chemotherapy.

Science.gov (United States)

Shia, Wei-Chung; Huang, Yu-Len; Wu, Hwa-Koon; Chen, Dar-Ren

2017-05-01

Strategies are needed for the identification of a poor response to treatment and determination of appropriate chemotherapy strategies for patients in the early stages of neoadjuvant chemotherapy for breast cancer. We hypothesize that power Doppler ultrasound imaging can provide useful information on predicting response to neoadjuvant chemotherapy. The solid directional flow of vessels in breast tumors was used as a marker of pathologic complete responses (pCR) in patients undergoing neoadjuvant chemotherapy. Thirty-one breast cancer patients who received neoadjuvant chemotherapy and had tumors of 2 to 5 cm were recruited. Three-dimensional power Doppler ultrasound with high-definition flow imaging technology was used to acquire the indices of tumor blood flow/volume, and the chemotherapy response prediction was established, followed by support vector machine classification. The accuracy of pCR prediction before the first chemotherapy treatment was 83.87% (area under the ROC curve [AUC] = 0.6957). After the second chemotherapy treatment, the accuracy of was 87.9% (AUC = 0.756). Trend analysis showed that good and poor responders exhibited different trends in vascular flow during chemotherapy. This preliminary study demonstrates the feasibility of using the vascular flow in breast tumors to predict chemotherapeutic efficacy. © 2017 by the American Institute of Ultrasound in Medicine.

Diffusion-weighted magnetic resonance imaging: biomarker for treatment response in oncology

Directory of Open Access Journals (Sweden)

Maria Luiza Testa

2013-06-01

Full Text Available The authors report a case where a quantitative assessment of the apparent diffusion coefficient (ADC of liver metastasis in a patient undergoing chemotherapy has shown to be an effective early marker for predicting therapeutic response, anticipating changes in tumor size. A lesion with lower initial ADC value and early increase in such value in the course of the treatment tends to present a better therapeutic response.

Prediction of placebo responses: A systematic review of the literature

Directory of Open Access Journals (Sweden)

Bjoern eHoring

2014-10-01

Full Text Available Objective: Predicting who responds to placebo treatment – and under which circumstances – has been a question of interest and investigation for generations. However, the literature is disparate and inconclusive. This review aims to identify publications that provide high quality data on the topic of placebo response (PR prediction. Methods: To identify studies concerned with PR prediction, independent searches were performed in an expert database (for all symptom modalities and in PubMed (for pain only. Articles were selected when a they assessed putative predictors prior to placebo treatment and b an adequate control group was included when the association of predictors and PRs were analyzed. Results: Twenty-one studies were identified, most with pain as dependent variable. Most predictors of PRs were psychological constructs related to actions, expected outcomes and the emotional valence attached to these events (goal-seeking, self-efficacy/-esteem, locus of control, optimism. Other predictors involved behavioural control (desire for control, eating restraint, personality variables (fun seeking, sensation seeking, neuroticism, biological markers (sex, a single nucleotide polymorphism related to dopamine metabolism. Finally, suggestibility and beliefs in expectation biases, body consciousness and baseline symptom severity were found to be predictive. Conclusions: While results are heterogeneous, some congruence of predictors can be identified. PRs mainly appear to be moderated by expectations of how the symptom might change after treatment, or the expectation of how symptom repetition can be coped with. It is suggested to include the listed constructs in future research. Furthermore, a closer look at variables moderating symptom change in control groups seems warranted.

Diffusion-weighted magnetic resonance imaging for pretreatment prediction and monitoring of treatment response of patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy

International Nuclear Information System (INIS)

Nilsen, Line; Olsen, Dag Rune; Seierstad, Therese; Fangberget, Anne; Geier, Oliver

2010-01-01

Background. For patients with locally advanced breast cancer (LABC) undergoing neoadjuvant chemotherapy (NACT), the European Guidelines for Breast Imaging recommends magnetic resonance imaging (MRI) to be performed before start of NACT, when half of the NACT has been administered and prior to surgery. This is the first study addressing the value of flow-insensitive apparent diffusion coefficients (ADCs) obtained from diffusion-weighted (DW) MRI at the recommended time points for pretreatment prediction and monitoring of treatment response. Materials and methods. Twenty-five LABC patients were included in this prospective study. DW MRI was performed using single-shot spin-echo echo-planar imaging with b-values of 100, 250 and 800 s/mm 2 prior to NACT, after four cycles of NACT and at the conclusion of therapy using a 1.5 T MR scanner. ADC in the breast tumor was calculated from each assessment. The strength of correlation between pretreatment ADC, ADC changes and tumor volume changes were examined using Spearman's rho correlation test. Results. Mean pretreatment ADC was 1.11 ± 0.21 x 10 -3 mm 2 /s. After 4 cycles of NACT, ADC was significantly increased (1.39 ± 0.36 x 10 -3 mm 2 /s; p=0.018). There was no correlation between individual pretreatment breast tumor ADC and MR response measured after four cycles of NACT (p=0.816) or prior to surgery (p=0.620). Conclusion. Pretreatment tumor ADC does not predict treatment response for patients with LABC undergoing NACT. Furthermore, ADC increase observed mid-way in the course of NACT does not correlate with tumor volume changes.

Methodology for Designing Models Predicting Success of Infertility Treatment

OpenAIRE

Alireza Zarinara; Mohammad Mahdi Akhondi; Hojjat Zeraati; Koorsh Kamali; Kazem Mohammad

2016-01-01

Abstract Background: The prediction models for infertility treatment success have presented since 25 years ago. There are scientific principles for designing and applying the prediction models that is also used to predict the success rate of infertility treatment. The purpose of this study is to provide basic principles for designing the model to predic infertility treatment success. Materials and Methods: In this paper, the principles for developing predictive models are explained and...

The predictive value of extensor grip test for the effectiveness of treatment for tennis elbow

International Nuclear Information System (INIS)

Zehtab, Mohammad J.; Mirghasemi, A.; Majlesara, A.; Siavashi, B.; Tajik, P.

2008-01-01

Objective was to compare the effectiveness of 5 different modalities and determine the usefulness of recently proposed extensor grip test (EGT) in predicting the response to treatment. In a randomized controlled clinical trial, 92 of 98 tennis elbow patients in Sina Hospital Tehran, Iran between 2006 and 2007 fulfilled the trial entry criteria. Among these patients 56 (60.9%) had positive EGT results, were randomly allocated to 5 treatment groups: brace, physiotherapy, brace plus physiotherapy, injection and injection plus physiotherapy. Patients with a positive EGT result had better response to treatments. Among them, injection plus physiotherapy was the most successful, then brace plus physiotherapy was the worst treatment modality. Response to treatment was comparable in all groups between EGT positive and negative patients except bracing, in which positive EGT was correlated with dramatic response to treatment. In all patients, injection plus physiotherapy and the brace plus physiotherapy is recommended, but in EGT negatives, bracing seems to be of no use. Injection alone is not recommended in either group. (author)

SU-E-J-258: Prediction of Cervical Cancer Treatment Response Using Radiomics Features Based On F18-FDG Uptake in PET Images

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Altazi, B; Fernandez, D; Zhang, G; Biagioli, M; Moros, E; Moffitt, H. Lee [Cancer Center, Tampa, FL, University of South Florida, Tampa, FL (United States)

2015-06-15

Purpose: Radiomics have shown potential for predicting treatment outcomes in several body sites. This study investigated the correlation between PET Radiomics features and treatment response of cervical cancer outcomes. Methods: our dataset consisted of a cohort of 79 patients diagnosed with cervical cancer, FIGO stage IB-IVA, age range 25–86 years, (median age at diagnosis: 50 years) all treated between: 2009–14 with external beam radiation therapy to a dose range between: 45–50.4 Gy (median= 45 Gy), concurrent cisplatin chemotherapy and MRI-based brachytherapy to a dose of 20–30 Gy (median= 28 Gy). Metabolic Tumor Volume (MTV) in patient’s primary site was delineated on pretreatment PET/CT by two board certified Radiation Oncologists. The features extracted from each patient’s volume were: 26 Co-occurrence matrix (COM) Feature, 11 Run-Length Matrix (RLM), 11 Gray Level Size Zone Matrix (GLSZM) and 33 Intensity-based features (IBF). The treatment outcome was divided based on the last follow up status into three classes: No Evidence of Disease (NED), Alive with Disease (AWD) and Dead of Disease (DOD). The ability for the radiomics features to differentiate between the 3 treatments outcome categories were assessed by One-Way ANOVA test with p-value < 0.05 was to be statistically significant. The results from the analysis were compared with the ones obtained previously for standard Uptake Value (SUV). Results: Based on patients last clinical follow-up; 52 showed NED, 17 AWD and 10 DOD. Radiomics Features were able to classify the patients based on their treatment response. A parallel analysis was done for SUV measurements for comparison. Conclusion: Radiomics features were able to differentiate between the three different classes of treatment outcomes. However, most of the features were only able to differentiate between NED and DOD class. Also, The ability or radiomics features to differentiate types of response were more significant than SUV.

Rapid response in psychological treatments for binge eating disorder.

Science.gov (United States)

Hilbert, Anja; Hildebrandt, Thomas; Agras, W Stewart; Wilfley, Denise E; Wilson, G Terence

2015-06-01

Analysis of short- and long-term effects of rapid response across 3 different treatments for binge eating disorder (BED). In a randomized clinical study comparing interpersonal psychotherapy (IPT), cognitive-behavioral therapy guided self-help (CBTgsh), and behavioral weight loss (BWL) treatment in 205 adults meeting Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; APA, 1994) criteria for BED, the predictive value of rapid response, defined as ≥70% reduction in binge eating by Week 4, was determined for remission from binge eating and global eating disorder psychopathology at posttreatment, 6-, 12-, 18-, and 24-month follow-ups. Rapid responders in CBTgsh, but not in IPT or BWL, showed significantly greater rates of remission from binge eating than nonrapid responders, which was sustained over the long term. Rapid and nonrapid responders in IPT and rapid responders in CBTgsh showed a greater remission from binge eating than nonrapid responders in CBTgsh and BWL. Rapid responders in CBTgsh showed greater remission from binge eating than rapid responders in BWL. Although rapid responders in all treatments had lower global eating disorder psychopathology than nonrapid responders in the short term, rapid responders in CBTgsh and IPT were more improved than those in BWL and nonrapid responders in each treatment. Rapid responders in BWL did not differ from nonrapid responders in CBTgsh and IPT. Rapid response is a treatment-specific positive prognostic indicator of sustained remission from binge eating in CBTgsh. Regarding an evidence-based, stepped-care model, IPT, equally efficacious for rapid and nonrapid responders, could be investigated as a second-line treatment in case of nonrapid response to first-line CBTgsh. (c) 2015 APA, all rights reserved).

Rapid Response in Psychological Treatments for Binge-Eating Disorder

Science.gov (United States)

Hilbert, Anja; Hildebrandt, Thomas; Agras, W. Stewart; Wilfley, Denise E.; Wilson, G. Terence

2015-01-01

Objective Analysis of short- and long-term effects of rapid response across three different treatments for binge-eating disorder (BED). Method In a randomized clinical study comparing interpersonal psychotherapy (IPT), cognitive-behavioral guided self-help (CBTgsh), and behavioral weight loss (BWL) treatment in 205 adults meeting DSM-IV criteria for BED, the predictive value of rapid response, defined as ≥ 70% reduction in binge-eating by week four, was determined for remission from binge-eating and global eating disorder psychopathology at posttreatment, 6-, 12-, 18-, and 24-month follow-up. Results Rapid responders in CBTgsh, but not in IPT or BWL, showed significantly greater rates of remission from binge-eating than non-rapid responders, which was sustained over the long term. Rapid and non-rapid responders in IPT and rapid responders in CBTgsh showed a greater remission from binge-eating than non-rapid responders in CBTgsh and BWL. Rapid responders in CBTgsh showed greater remission from binge-eating than rapid responders in BWL. Although rapid responders in all treatments had lower global eating disorder psychopathology than non-rapid responders in the short term, rapid responders in CBTgsh and IPT were more improved than those in BWL and non-rapid responders in each treatment. Rapid responders in BWL did not differ from non-rapid responders in CBTgsh and IPT. Conclusions Rapid response is a treatment-specific positive prognostic indicator of sustained remission from binge-eating in CBTgsh. Regarding an evidence-based stepped care model, IPT, equally efficacious for rapid and non-rapid responders, could be investigated as a second-line treatment in case of non-rapid response to first-line CBTgsh. PMID:25867446

A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI

Science.gov (United States)

Ravichandran, Kavya; Braman, Nathaniel; Janowczyk, Andrew; Madabhushi, Anant

2018-02-01

Neoadjuvant chemotherapy (NAC) is routinely used to treat breast tumors before surgery to reduce tumor size and improve outcome. However, no current clinical or imaging metrics can effectively predict before treatment which NAC recipients will achieve pathological complete response (pCR), the absence of residual invasive disease in the breast or lymph nodes following surgical resection. In this work, we developed and applied a convolu- tional neural network (CNN) to predict pCR from pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans on a per-voxel basis. In this study, DCE-MRI data for a total of 166 breast cancer pa- tients from the ISPY1 Clinical Trial were split into a training set of 133 patients and a testing set of 33 patients. A CNN consisting of 6 convolutional blocks was trained over 30 epochs. The pre-contrast and post-contrast DCE-MRI phases were considered in isolation and conjunction. A CNN utilizing a combination of both pre- and post-contrast images best distinguished responders, with an AUC of 0.77; 82% of the patients in the testing set were correctly classified based on their treatment response. Within the testing set, the CNN was able to produce probability heatmaps that visualized tumor regions that most strongly predicted therapeutic response. Multi- variate analysis with prognostic clinical variables (age, largest diameter, hormone receptor and HER2 status), revealed that the network was an independent predictor of response (p=0.05), and that the inclusion of HER2 status could further improve capability to predict response (AUC = 0.85, accuracy = 85%).

Computer-aided global breast MR image feature analysis for prediction of tumor response to chemotherapy: performance assessment

Science.gov (United States)

Aghaei, Faranak; Tan, Maxine; Hollingsworth, Alan B.; Zheng, Bin; Cheng, Samuel

2016-03-01

Dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) has been used increasingly in breast cancer diagnosis and assessment of cancer treatment efficacy. In this study, we applied a computer-aided detection (CAD) scheme to automatically segment breast regions depicting on MR images and used the kinetic image features computed from the global breast MR images acquired before neoadjuvant chemotherapy to build a new quantitative model to predict response of the breast cancer patients to the chemotherapy. To assess performance and robustness of this new prediction model, an image dataset involving breast MR images acquired from 151 cancer patients before undergoing neoadjuvant chemotherapy was retrospectively assembled and used. Among them, 63 patients had "complete response" (CR) to chemotherapy in which the enhanced contrast levels inside the tumor volume (pre-treatment) was reduced to the level as the normal enhanced background parenchymal tissues (post-treatment), while 88 patients had "partially response" (PR) in which the high contrast enhancement remain in the tumor regions after treatment. We performed the studies to analyze the correlation among the 22 global kinetic image features and then select a set of 4 optimal features. Applying an artificial neural network trained with the fusion of these 4 kinetic image features, the prediction model yielded an area under ROC curve (AUC) of 0.83+/-0.04. This study demonstrated that by avoiding tumor segmentation, which is often difficult and unreliable, fusion of kinetic image features computed from global breast MR images without tumor segmentation can also generate a useful clinical marker in predicting efficacy of chemotherapy.

Collision prediction software for radiotherapy treatments

Energy Technology Data Exchange (ETDEWEB)

Padilla, Laura [Virginia Commonwealth University Medical Center, Richmond, Virginia 23298 (United States); Pearson, Erik A. [Techna Institute and the Princess Margaret Cancer Center, University Health Network, Toronto, Ontario M5G 2M9 (Canada); Pelizzari, Charles A., E-mail: c-pelizzari@uchicago.edu [Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, Illinois 60637 (United States)

2015-11-15

Purpose: This work presents a method of collision predictions for external beam radiotherapy using surface imaging. The present methodology focuses on collision prediction during treatment simulation to evaluate the clearance of a patient’s treatment position and allow for its modification if necessary. Methods: A Kinect camera (Microsoft, Redmond, WA) is used to scan the patient and immobilization devices in the treatment position at the simulator. The surface is reconstructed using the SKANECT software (Occipital, Inc., San Francisco, CA). The treatment isocenter is marked using simulated orthogonal lasers projected on the surface scan. The point cloud of this surface is then shifted to isocenter and converted from Cartesian to cylindrical coordinates. A slab models the treatment couch. A cylinder with a radius equal to the normal distance from isocenter to the collimator plate, and a height defined by the collimator diameter is used to estimate collisions. Points within the cylinder clear through a full gantry rotation with the treatment couch at 0° , while points outside of it collide. The angles of collision are reported. This methodology was experimentally verified using a mannequin positioned in an alpha cradle with both arms up. A planning CT scan of the mannequin was performed, two isocenters were marked in PINNACLE, and this information was exported to AlignRT (VisionRT, London, UK)—a surface imaging system for patient positioning. This was used to ensure accurate positioning of the mannequin in the treatment room, when available. Collision calculations were performed for the two treatment isocenters and the results compared to the collisions detected the room. The accuracy of the Kinect-Skanect surface was evaluated by comparing it to the external surface of the planning CT scan. Results: Experimental verification results showed that the predicted angles of collision matched those recorded in the room within 0.5°, in most cases (largest deviation �

Early spatiotemporal-specific changes in intermediate signals are predictive of cytotoxic sensitivity to TNFα and co-treatments

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Loo, Lit-Hsin; Bougen-Zhukov, Nicola Michelle; Tan, Wei-Ling Cecilia

2017-03-01

Signaling pathways can generate different cellular responses to the same cytotoxic agents. Current quantitative models for predicting these differential responses are usually based on large numbers of intracellular gene products or signals at different levels of signaling cascades. Here, we report a study to predict cellular sensitivity to tumor necrosis factor alpha (TNFα) using high-throughput cellular imaging and machine-learning methods. We measured and compared 1170 protein phosphorylation events in a panel of human lung cancer cell lines based on different signals, subcellular regions, and time points within one hour of TNFα treatment. We found that two spatiotemporal-specific changes in an intermediate signaling protein, p90 ribosomal S6 kinase (RSK), are sufficient to predict the TNFα sensitivity of these cell lines. Our models could also predict the combined effects of TNFα and other kinase inhibitors, many of which are not known to target RSK directly. Therefore, early spatiotemporal-specific changes in intermediate signals are sufficient to represent the complex cellular responses to these perturbations. Our study provides a general framework for the development of rapid, signaling-based cytotoxicity screens that may be used to predict cellular sensitivity to a cytotoxic agent, or identify co-treatments that may sensitize or desensitize cells to the agent.

Accumulating Data to Optimally Predict Obesity Treatment (ADOPT): Recommendations from the Biological Domain.

Science.gov (United States)

Rosenbaum, Michael; Agurs-Collins, Tanya; Bray, Molly S; Hall, Kevin D; Hopkins, Mark; Laughlin, Maren; MacLean, Paul S; Maruvada, Padma; Savage, Cary R; Small, Dana M; Stoeckel, Luke

2018-04-01

The responses to behavioral, pharmacological, or surgical obesity treatments are highly individualized. The Accumulating Data to Optimally Predict obesity Treatment (ADOPT) project provides a framework for how obesity researchers, working collectively, can generate the evidence base needed to guide the development of tailored, and potentially more effective, strategies for obesity treatment. The objective of the ADOPT biological domain subgroup is to create a list of high-priority biological measures for weight-loss studies that will advance the understanding of individual variability in response to adult obesity treatments. This list includes measures of body composition, energy homeostasis (energy intake and output), brain structure and function, and biomarkers, as well as biobanking procedures, which could feasibly be included in most, if not all, studies of obesity treatment. The recommended high-priority measures are selected to balance needs for sensitivity, specificity, and/or comprehensiveness with feasibility to achieve a commonality of usage and increase the breadth and impact of obesity research. The accumulation of data on key biological factors, along with behavioral, psychosocial, and environmental factors, can generate a more precise description of the interplay and synergy among them and their impact on treatment responses, which can ultimately inform the design and delivery of effective, tailored obesity treatments. © 2018 The Obesity Society.

Prediction of response to radiotherapy in the treatment of esophageal cancer using stem cell markers

International Nuclear Information System (INIS)

Smit, Justin K.; Faber, Hette; Niemantsverdriet, Maarten; Baanstra, Mirjam; Bussink, Johan; Hollema, Harry; Os, Ronald P. van; Plukker, John Th. M.; Coppes, Robert P.

2013-01-01

Background and purpose: In this study, we investigated whether cancer stem cell marker expressing cells can be identified that predict for the response of esophageal cancer (EC) to CRT. Materials and methods: EC cell-lines OE-33 and OE-21 were used to assess in vitro, stem cell activity, proliferative capacity and radiation response. Xenograft tumors were generated using NOD/SCID mice to assess in vivo proliferative capacity and tumor hypoxia. Archival and fresh EC biopsy tissue was used to confirm our in vitro and in vivo results. Results: We showed that the CD44+/CD24− subpopulation of EC cells exerts a higher proliferation rate and sphere forming potential and is more radioresistant in vitro, when compared to unselected or CD44+/CD24+ cells. Moreover, CD44+/CD24− cells formed xenograft tumors faster and were often located in hypoxic tumor areas. In a study of archival pre-neoadjuvant CRT biopsy material from EC adenocarcinoma patients (N = 27), this population could only be identified in 50% (9/18) of reduced-responders to neoadjuvant CRT, but never (0/9) in the complete responders (P = 0.009). Conclusion: These results warrant further investigation into the possible clinical benefit of CD44+/CD24− as a predictive marker in EC patients for the response to chemoradiation

The accurate definition of metabolic volumes on {sup 18}F-FDG-PET before treatment allows the response to chemoradiotherapy to be predicted in the case of oesophagus cancers; La definition precise des volumes metaboliques sur TEP au 18F-FDG avant traitement permet la prediction de la reponse a la chimioradiotherapie dans les cancers de l'oesophage

Energy Technology Data Exchange (ETDEWEB)

Hatt, M.; Cheze-Le Rest, C.; Visvikis, D. [Inserm U650, Brest (France); Pradier, O. [Radiotherapie, CHRU Morvan, Brest (France)

2011-10-15

This study aims at assessing the possibility of prediction of the response of locally advanced oesophagus cancers, even before the beginning of treatment, by using metabolic volume measurements performed on {sup 18}F-FDG PET images made before the treatment. Medical files of 50 patients have been analyzed. According to the observed responses, and to metabolic volume and Total Lesion Glycosis (TLG) values, it appears that the images allow the extraction of parameters, such as the TLG, which are criteria for the prediction of the therapeutic response. Short communication

Neural markers of attention to aversive pictures predict response to cognitive behavioral therapy in anxiety and depression.

Science.gov (United States)

Stange, Jonathan P; MacNamara, Annmarie; Barnas, Olga; Kennedy, Amy E; Hajcak, Greg; Phan, K Luan; Klumpp, Heide

2017-02-01

Excessive attention toward aversive information may be a core mechanism underlying emotional disorders, but little is known about whether this is predictive of response to treatments. We evaluated whether enhanced attention toward aversive stimuli, as indexed by an event-related potential component, the late positive potential (LPP), would predict response to cognitive behavioral therapy (CBT) in patients with social anxiety disorder and/or major depressive disorder. Thirty-two patients receiving 12 weeks of CBT responded to briefly-presented pairs of aversive and neutral pictures that served as targets or distracters while electroencephaolography was recorded. Patients with larger pre-treatment LPPs to aversive relative to neutral distracters (when targets were aversive) were more likely to respond to CBT, and demonstrated larger reductions in symptoms of depression and anxiety following treatment. Increased attention toward irrelevant aversive stimuli may signal attenuated top-down control, so treatments like CBT that improve this control could be beneficial for these individuals. Copyright © 2016 Elsevier B.V. All rights reserved.

Prediction of Response to Medication and Cognitive Therapy in the Treatment of Moderate to Severe Depression

Science.gov (United States)

Fournier, Jay C.; DeRubeis, Robert J.; Shelton, Richard C.; Hollon, Steven D.; Amsterdam, Jay D.; Gallop, Robert

2009-01-01

A recent randomized controlled trial found nearly equivalent response rates for antidepressant medications and cognitive therapy in a sample of moderate to severely depressed outpatients. In this article, the authors seek to identify the variables that were associated with response across both treatments as well as variables that predicted…

Discriminate the response of Acute Myeloid Leukemia patients to treatment by using proteomics data and Answer Set Programming.

Science.gov (United States)

Chebouba, Lokmane; Miannay, Bertrand; Boughaci, Dalila; Guziolowski, Carito

2018-03-08

During the last years, several approaches were applied on biomedical data to detect disease specific proteins and genes in order to better target drugs. It was shown that statistical and machine learning based methods use mainly clinical data and improve later their results by adding omics data. This work proposes a new method to discriminate the response of Acute Myeloid Leukemia (AML) patients to treatment. The proposed approach uses proteomics data and prior regulatory knowledge in the form of networks to predict cancer treatment outcomes by finding out the different Boolean networks specific to each type of response to drugs. To show its effectiveness we evaluate our method on a dataset from the DREAM 9 challenge. The results are encouraging and demonstrate the benefit of our approach to distinguish patient groups with different response to treatment. In particular each treatment response group is characterized by a predictive model in the form of a signaling Boolean network. This model describes regulatory mechanisms which are specific to each response group. The proteins in this model were selected from the complete dataset by imposing optimization constraints that maximize the difference in the logical response of the Boolean network associated to each group of patients given the omic dataset. This mechanistic and predictive model also allow us to classify new patients data into the two different patient response groups. We propose a new method to detect the most relevant proteins for understanding different patient responses upon treatments in order to better target drugs using a Prior Knowledge Network and proteomics data. The results are interesting and show the effectiveness of our method.

Enteric microbiome metabolites correlate with response to simvastatin treatment.

Directory of Open Access Journals (Sweden)

Rima Kaddurah-Daouk

Full Text Available Although statins are widely prescribed medications, there remains considerable variability in therapeutic response. Genetics can explain only part of this variability. Metabolomics is a global biochemical approach that provides powerful tools for mapping pathways implicated in disease and in response to treatment. Metabolomics captures net interactions between genome, microbiome and the environment. In this study, we used a targeted GC-MS metabolomics platform to measure a panel of metabolites within cholesterol synthesis, dietary sterol absorption, and bile acid formation to determine metabolite signatures that may predict variation in statin LDL-C lowering efficacy. Measurements were performed in two subsets of the total study population in the Cholesterol and Pharmacogenetics (CAP study: Full Range of Response (FR, and Good and Poor Responders (GPR were 100 individuals randomly selected from across the entire range of LDL-C responses in CAP. GPR were 48 individuals, 24 each from the top and bottom 10% of the LDL-C response distribution matched for body mass index, race, and gender. We identified three secondary, bacterial-derived bile acids that contribute to predicting the magnitude of statin-induced LDL-C lowering in good responders. Bile acids and statins share transporters in the liver and intestine; we observed that increased plasma concentration of simvastatin positively correlates with higher levels of several secondary bile acids. Genetic analysis of these subjects identified associations between levels of seven bile acids and a single nucleotide polymorphism (SNP, rs4149056, in the gene encoding the organic anion transporter SLCO1B1. These findings, along with recently published results that the gut microbiome plays an important role in cardiovascular disease, indicate that interactions between genome, gut microbiome and environmental influences should be considered in the study and management of cardiovascular disease. Metabolic

Predictive value of brain perfusion SPECT for rTMS response in pharmacoresistant depression

Energy Technology Data Exchange (ETDEWEB)

Richieri, Raphaelle; Lancon, Christophe [Sainte-Marguerite University Hospital, Department of Psychiatry, Marseille (France); La Timone University, EA 3279 - Self-perceived Health Assessment Research Unit, School of Medicine, Marseille (France); Boyer, Laurent [La Timone University, EA 3279 - Self-perceived Health Assessment Research Unit, School of Medicine, Marseille (France); La Timone University Hospital, Assistance Publique - Hopitaux de Marseille, Department of Public Health, Marseille (France); Farisse, Jean [Sainte-Marguerite University Hospital, Department of Psychiatry, Marseille (France); Colavolpe, Cecile; Mundler, Olivier [La Timone University Hospital, Assistance Publique - Hopitaux de Marseille, Service Central de Biophysique et Medecine Nucleaire, Marseille (France); Universite de la Mediterranee, Centre Europeen de Recherche en Imagerie Medicale (CERIMED), Marseille (France); Guedj, Eric [La Timone University Hospital, Assistance Publique - Hopitaux de Marseille, Service Central de Biophysique et Medecine Nucleaire, Marseille (France); Universite de la Mediterranee, Centre Europeen de Recherche en Imagerie Medicale (CERIMED), Marseille (France); Hopital de la Timone, Service Central de Biophysique et de Medecine Nucleaire, Marseille Cedex 5 (France)

2011-09-15

The aim of this study was to determine the predictive value of whole-brain voxel-based regional cerebral blood flow (rCBF) for repetitive transcranial magnetic stimulation (rTMS) response in patients with pharmacoresistant depression. Thirty-three right-handed patients who met DSM-IV criteria for major depressive disorder (unipolar or bipolar depression) were included before rTMS. rTMS response was defined as at least 50% reduction in the baseline Beck Depression Inventory scores. The predictive value of {sup 99m}Tc-ethyl cysteinate dimer (ECD) single photon emission computed tomography (SPECT) for rTMS response was studied before treatment by comparing rTMS responders to non-responders at voxel level using Statistical Parametric Mapping (SPM) (p < 0.001, uncorrected). Of the patients, 18 (54.5%) were responders to rTMS and 15 were non-responders (45.5%). There were no statistically significant differences in demographic and clinical characteristics (p > 0.10). In comparison to responders, non-responders showed significant hypoperfusions (p < 0.001, uncorrected) in the left medial and bilateral superior frontal cortices (BA10), the left uncus/parahippocampal cortex (BA20/BA35) and the right thalamus. The area under the curve for the combination of SPECT clusters to predict rTMS response was 0.89 (p < 0.001). Sensitivity, specificity, positive predictive value and negative predictive value for the combination of clusters were: 94, 73, 81 and 92%, respectively. This study shows that, in pharmacoresistant depression, pretreatment rCBF of specific brain regions is a strong predictor for response to rTMS in patients with homogeneous demographic/clinical features. (orig.)

Prediction of residual metabolic activity after treatment in NSCLC patients

International Nuclear Information System (INIS)

Rios Velazquez, Emmanuel; Aerts, Hugo J.W.L.; Oberije, Cary; Ruysscher, Dirk De; Lambin, Philippe

2010-01-01

Purpose. Metabolic response assessment is often used as a surrogate of local failure and survival. Early identification of patients with residual metabolic activity is essential as this enables selection of patients who could potentially benefit from additional therapy. We report on the development of a pre-treatment prediction model for metabolic response using patient, tumor and treatment factors. Methods. One hundred and one patients with inoperable NSCLC (stage I-IV), treated with 3D conformal radical (chemo)-radiotherapy were retrospectively included in this study. All patients received a pre and post-radiotherapy fluorodeoxyglucose positron emission tomography-computed tomography FDG-PET-CT scan. The electronic medical record system and the medical patient charts were reviewed to obtain demographic, clinical, tumor and treatment data. Primary outcome measure was examined using a metabolic response assessment on a post-radiotherapy FDG-PET-CT scan. Radiotherapy was delivered in fractions of 1.8 Gy, twice a day, with a median prescribed dose of 60 Gy. Results. Overall survival was worse in patients with residual metabolic active areas compared with the patients with a complete metabolic response (p=0.0001). In univariate analysis, three variables were significantly associated with residual disease: larger primary gross tumor volume (GTVprimary, p=0.002), higher pre-treatment maximum standardized uptake value (SUV max , p=0.0005) in the primary tumor and shorter overall treatment time (OTT, p=0.046). A multivariate model including GTVprimary, SUV max , equivalent radiation dose at 2 Gy corrected for time (EQD2, T) and OTT yielded an area under the curve assessed by the leave-one-out cross validation of 0.71 (95% CI, 0.65-0.76). Conclusion. Our results confirmed the validity of metabolic response assessment as a surrogate of survival. We developed a multivariate model that is able to identify patients at risk of residual disease. These patients may benefit from

Metformin improves glucose effectiveness, not insulin sensitivity: predicting treatment response in women with polycystic ovary syndrome in an open-label, interventional study.

Science.gov (United States)

Pau, Cindy T; Keefe, Candace; Duran, Jessica; Welt, Corrine K

2014-05-01

Although metformin is widely used to improve insulin resistance in women with polycystic ovary syndrome (PCOS), its mechanism of action is complex, with inconsistent effects on insulin sensitivity and variability in treatment response. The aim of the study was to delineate the effect of metformin on glucose and insulin parameters, determine additional treatment outcomes, and predict patients with PCOS who will respond to treatment. We conducted an open-label, interventional study at an academic medical center. Women with PCOS (n = 36) diagnosed by the National Institutes of Health criteria participated in the study. Subjects underwent fasting blood sampling, an IV glucose tolerance test, dual-energy x-ray absorptiometry scan, transvaginal ultrasound, and measurement of human chorionic gonadotropin-stimulated androgen levels before and after 12 weeks of treatment with metformin extended release 1500 mg/d. Interval visits were performed to monitor anthropometric measurements and menstrual cycle parameters. Changes in glucose and insulin parameters, androgen levels, anthropometric measurements, and ovulatory menstrual cycles were evaluated. Insulin sensitivity did not change despite weight loss. Glucose effectiveness (P = .002) and the acute insulin response to glucose (P = .002) increased, and basal glucose levels (P = .001) decreased after metformin treatment. T levels also decreased. Women with improved ovulatory function (61%) had lower baseline T levels and lower baseline and stimulated T and androstenedione levels after metformin treatment (all P effectiveness and insulin sensitivity, metformin does not improve insulin sensitivity in women with PCOS but does improve glucose effectiveness. The improvement in glucose effectiveness may be partially mediated by decreased glucose levels. T levels also decreased with metformin treatment. Ovulation during metformin treatment was associated with lower baseline T levels and greater T and androstenedione decreases during

Pharmacogenomics of Methotrexate Membrane Transport Pathway: Can Clinical Response to Methotrexate in Rheumatoid Arthritis Be Predicted?

Directory of Open Access Journals (Sweden)

Aurea Lima

2015-06-01

Full Text Available Background: Methotrexate (MTX is widely used for rheumatoid arthritis (RA treatment. Single nucleotide polymorphisms (SNPs could be used as predictors of patients’ therapeutic outcome variability. Therefore, this study aims to evaluate the influence of SNPs in genes encoding for MTX membrane transport proteins in order to predict clinical response to MTX. Methods: Clinicopathological data from 233 RA patients treated with MTX were collected, clinical response defined, and patients genotyped for 23 SNPs. Genotype and haplotype analyses were performed using multivariate methods and a genetic risk index (GRI for non-response was created. Results: Increased risk for non-response was associated to SLC22A11 rs11231809 T carriers; ABCC1 rs246240 G carriers; ABCC1 rs3784864 G carriers; CGG haplotype for ABCC1 rs35592, rs2074087 and rs3784864; and CGG haplotype for ABCC1 rs35592, rs246240 and rs3784864. GRI demonstrated that patients with Index 3 were 16-fold more likely to be non-responders than those with Index 1. Conclusions: This study revealed that SLC22A11 and ABCC1 may be important to identify those patients who will not benefit from MTX treatment, highlighting the relevance in translating these results to clinical practice. However, further validation by independent studies is needed to develop the field of personalized medicine to predict clinical response to MTX treatment.

International Study to Predict Optimized Treatment for Depression (iSPOT-D, a randomized clinical trial: rationale and protocol

Directory of Open Access Journals (Sweden)

Cooper Nicholas J

2011-01-01

Full Text Available Abstract Background Clinically useful treatment moderators of Major Depressive Disorder (MDD have not yet been identified, though some baseline predictors of treatment outcome have been proposed. The aim of iSPOT-D is to identify pretreatment measures that predict or moderate MDD treatment response or remission to escitalopram, sertraline or venlafaxine; and develop a model that incorporates multiple predictors and moderators. Methods/Design The International Study to Predict Optimized Treatment - in Depression (iSPOT-D is a multi-centre, international, randomized, prospective, open-label trial. It is enrolling 2016 MDD outpatients (ages 18-65 from primary or specialty care practices (672 per treatment arm; 672 age-, sex- and education-matched healthy controls. Study-eligible patients are antidepressant medication (ADM naïve or willing to undergo a one-week wash-out of any non-protocol ADM, and cannot have had an inadequate response to protocol ADM. Baseline assessments include symptoms; distress; daily function; cognitive performance; electroencephalogram and event-related potentials; heart rate and genetic measures. A subset of these baseline assessments are repeated after eight weeks of treatment. Outcomes include the 17-item Hamilton Rating Scale for Depression (primary and self-reported depressive symptoms, social functioning, quality of life, emotional regulation, and side-effect burden (secondary. Participants may then enter a naturalistic telephone follow-up at weeks 12, 16, 24 and 52. The first half of the sample will be used to identify potential predictors and moderators, and the second half to replicate and confirm. Discussion First enrolment was in December 2008, and is ongoing. iSPOT-D evaluates clinical and biological predictors of treatment response in the largest known sample of MDD collected worldwide. Trial registration International Study to Predict Optimised Treatment - in Depression (iSPOT-D ClinicalTrials.gov Identifier

The effects of genetic polymorphism on treatment response of recombinant human growth hormone.

Science.gov (United States)

Chen, Shi; You, Hanxiao; Pan, Hui; Zhu, Huijuan; Yang, Hongbo; Gong, Fengying; Wang, Linjie; Jiang, Yu; Yan, Chengsheng

2017-12-06

Recombinant human growth hormone (rhGH) has been widely used in clinical treatment of growth hormone deficiency (GHD) or non GHD since 1985 and technology have achieved a great development in different long-acting formulations. Although the mathematical models for predicting the growth hormone response could help clinicians get to an individual personalized growth dose, many patients just can't reach the target height and the growth hormone responses differed.Genetic polymorphisms may play a role in the varies of individual responses in this treatment process.This article gives an overview of the genetic polymorphisms research of growth hormone in recent years, in order to give some potential suggestion and guide for the dose titration during treatment. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Use of Germline Polymorphisms in Predicting Concurrent Chemoradiotherapy Response in Esophageal Cancer

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Chen, Pei-Chun; Chen, Yen-Ching; Lai, Liang-Chuan; Tsai, Mong-Hsun; Chen, Shin-Kuang; Yang, Pei-Wen; Lee, Yung-Chie; Hsiao, Chuhsing K.; Lee, Jang-Ming; Chuang, Eric Y.

2012-01-01

Purpose: To identify germline polymorphisms to predict concurrent chemoradiation therapy (CCRT) response in esophageal cancer patients. Materials and Methods: A total of 139 esophageal cancer patients treated with CCRT (cisplatin-based chemotherapy combined with 40 Gy of irradiation) and subsequent esophagectomy were recruited at the National Taiwan University Hospital between 1997 and 2008. After excluding confounding factors (i.e., females and patients aged ≥70 years), 116 patients were enrolled to identify single nucleotide polymorphisms (SNPs) associated with specific CCRT responses. Genotyping arrays and mass spectrometry were used sequentially to determine germline polymorphisms from blood samples. These polymorphisms remain stable throughout disease progression, unlike somatic mutations from tumor tissues. Two-stage design and additive genetic models were adopted in this study. Results: From the 26 SNPs identified in the first stage, 2 SNPs were found to be significantly associated with CCRT response in the second stage. Single nucleotide polymorphism rs16863886, located between SGPP2 and FARSB on chromosome 2q36.1, was significantly associated with a 3.93-fold increase in pathologic complete response to CCRT (95% confidence interval 1.62–10.30) under additive models. Single nucleotide polymorphism rs4954256, located in ZRANB3 on chromosome 2q21.3, was associated with a 3.93-fold increase in pathologic complete response to CCRT (95% confidence interval 1.57–10.87). The predictive accuracy for CCRT response was 71.59% with these two SNPs combined. Conclusions: This is the first study to identify germline polymorphisms with a high accuracy for predicting CCRT response in the treatment of esophageal cancer.

Serum albumin level predicts initial intravenous immunoglobulin treatment failure in Kawasaki disease.

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Kuo, Ho-Chang; Liang, Chi-Di; Wang, Chih-Lu; Yu, Hong-Ren; Hwang, Kao-Pin; Yang, Kuender D

2010-10-01

Kawasaki disease (KD) is a systemic vasculitis primarily affecting children who are initial IVIG treatment. This study was conducted to investigate the risk factors for initial IVIG treatment failure in KD. Children who met KD diagnosis criteria and were admitted for IVIG treatment were retrospectively enrolled for analysis. Patients were divided into IVIG-responsive and IVIG-resistant groups. Initial laboratory data before IVIG treatment were collected for analysis. A total of 131 patients were enrolled during the study period. At 48 h after completion of initial IVIG treatment, 20 patients (15.3%) had an elevated body temperature. Univariate analysis showed that patients who had initial findings of high neutrophil count, abnormal liver function, low serum albumin level (≤2.9 g/dL) and pericardial effusion were at risk for IVIG treatment failure. Multivariate analysis with a logistic regression procedure showed that serum albumin level was considered the independent predicting factor of IVIG resistance in patients with KD (p = 0.006, OR = 40, 95% CI: 52.8-562). There was no significant correlation between age, gender, fever duration before IVIG treatment, haemoglobin level, total leucocyte and platelet counts, C-reactive protein level, or sterile pyuria and initial IVIG treatment failure. The specificity and sensitivity for prediction of IVIG treatment failure in this study were 96% and 34%, respectively. Pre-IVIG treatment serum albumin levels are a useful predictor of IVIG resistance in patients with KD. © 2010 The Author(s)/Journal Compilation © 2010 Foundation Acta Paediatrica.

Comparison of continuous versus categorical tumor measurement-based metrics to predict overall survival in cancer treatment trials

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An, Ming-Wen; Mandrekar, Sumithra J.; Branda, Megan E.; Hillman, Shauna L.; Adjei, Alex A.; Pitot, Henry; Goldberg, Richard M.; Sargent, Daniel J.

2011-01-01

Purpose The categorical definition of response assessed via the Response Evaluation Criteria in Solid Tumors has documented limitations. We sought to identify alternative metrics for tumor response that improve prediction of overall survival. Experimental Design Individual patient data from three North Central Cancer Treatment Group trials (N0026, n=117; N9741, n=1109; N9841, n=332) were used. Continuous metrics of tumor size based on longitudinal tumor measurements were considered in addition to a trichotomized response (TriTR: Response vs. Stable vs. Progression). Cox proportional hazards models, adjusted for treatment arm and baseline tumor burden, were used to assess the impact of the metrics on subsequent overall survival, using a landmark analysis approach at 12-, 16- and 24-weeks post baseline. Model discrimination was evaluated using the concordance (c) index. Results The overall best response rates for the three trials were 26%, 45%, and 25% respectively. While nearly all metrics were statistically significantly associated with overall survival at the different landmark time points, the c-indices for the traditional response metrics ranged from 0.59-0.65; for the continuous metrics from 0.60-0.66 and for the TriTR metrics from 0.64-0.69. The c-indices for TriTR at 12-weeks were comparable to those at 16- and 24-weeks. Conclusions Continuous tumor-measurement-based metrics provided no predictive improvement over traditional response based metrics or TriTR; TriTR had better predictive ability than best TriTR or confirmed response. If confirmed, TriTR represents a promising endpoint for future Phase II trials. PMID:21880789

Predicting therapy success for treatment as usual and blended treatment in the domain of depression.

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van Breda, Ward; Bremer, Vincent; Becker, Dennis; Hoogendoorn, Mark; Funk, Burkhardt; Ruwaard, Jeroen; Riper, Heleen

2018-06-01

In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients suffering from depression. We use this data, where 276 patients received treatment as usual and 227 received blended treatment, to investigate to what extent we are able to predict therapy success. We utilize different encoding strategies for preprocessing, varying feature selection techniques, and different statistical procedures for this purpose. Significant predictive power is found with average AUC values up to 0.7628 for treatment as usual and 0.7765 for blended treatment. Adding daily assessment data for blended treatment does currently not add predictive accuracy. Cost effectiveness analysis is needed to determine the added potential for real-world applications.

Dissociation predicts poor response to Dialectial Behavioral Therapy in female patients with Borderline Personality Disorder.

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Kleindienst, Nikolaus; Limberger, Matthias F; Ebner-Priemer, Ulrich W; Keibel-Mauchnik, Jana; Dyer, Anne; Berger, Mathias; Schmahl, Christian; Bohus, Martin

2011-08-01

A substantial proportion of Borderline Personality Disorder (BPD) patients respond by a marked decrease of psychopathology when treated with Dialectical Behavioral Therapy (DBT). To further enhance the rate of DBT-response, it is useful to identify characteristics related to unsatisfactory response. As DBT relies on emotional learning, we explored whether dissociation-which is known to interfere with learning- predicts poor response to DBT. Fifty-seven Borderline Personality Disorder (BPD) patients (DSM-IV) were prospectively observed during a three-month inpatient DBT program. Pre-post improvements in general psychopathology (SCL-90-R) were predicted from baseline scores of the Dissociative Experiences Scale (DES) by regression models accounting for baseline psychopathology. High DES-scores were related to poor pre-post improvement (β = -0.017 ± 0.006, p = 0.008). The data yielded no evidence that some facets of dissociation are more important in predicting DBT-response than others. The results suggest that dissociation in borderline-patients should be closely monitored and targeted during DBT. At this stage, research on treatment of dissociation (e.g., specific skills training) is warranted.

Uncertainties in model-based outcome predictions for treatment planning

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Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry

2001-01-01

Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment

The role of clinical variables, neuropsychological performance and SLC6A4 and COMT gene polymorphisms on the prediction of early response to fluoxetine in major depressive disorder.

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Gudayol-Ferré, Esteve; Herrera-Guzmán, Ixchel; Camarena, Beatriz; Cortés-Penagos, Carlos; Herrera-Abarca, Jorge E; Martínez-Medina, Patricia; Cruz, David; Hernández, Sandra; Genis, Alma; Carrillo-Guerrero, Mariana Y; Avilés Reyes, Rubén; Guàrdia-Olmos, Joan

2010-12-01

Major depressive disorder (MDD) is treated with antidepressants, but only between 50% and 70% of the patients respond to the initial treatment. Several authors suggested different factors that could predict antidepressant response, including clinical, psychophysiological, neuropsychological, neuroimaging, and genetic variables. However, these different predictors present poor prognostic sensitivity and specificity by themselves. The aim of our work is to study the possible role of clinical variables, neuropsychological performance, and the 5HTTLPR, rs25531, and val108/58Met COMT polymorphisms in the prediction of the response to fluoxetine after 4weeks of treatment in a sample of patient with MDD. 64 patients with MDD were genotyped according to the above-mentioned polymorphisms, and were clinically and neuropsychologically assessed before a 4-week fluoxetine treatment. Fluoxetine response was assessed by using the Hamilton Depression Rating Scale. We carried out a binary logistic regression model for the potential predictive variables. Out of the clinical variables studied, only the number of anxiety disorders comorbid with MDD have predicted a poor response to the treatment. A combination of a good performance in variables of attention and low performance in planning could predict a good response to fluoxetine in patients with MDD. None of the genetic variables studied had predictive value in our model. The possible placebo effect has not been controlled. Our study is focused on response prediction but not in remission prediction. Our work suggests that the combination of the number of comorbid anxiety disorders, an attentional variable, and two planning variables makes it possible to correctly classify 82% of the depressed patients who responded to the treatment with fluoxetine, and 74% of the patients who did not respond to that treatment. Copyright © 2010 Elsevier B.V. All rights reserved.

Increased amygdala responses to emotional faces after psilocybin for treatment-resistant depression.

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Roseman, Leor; Demetriou, Lysia; Wall, Matthew B; Nutt, David J; Carhart-Harris, Robin L

2017-12-27

Recent evidence indicates that psilocybin with psychological support may be effective for treating depression. Some studies have found that patients with depression show heightened amygdala responses to fearful faces and there is reliable evidence that treatment with SSRIs attenuates amygdala responses (Ma, 2015). We hypothesised that amygdala responses to emotional faces would be altered post-treatment with psilocybin. In this open-label study, 20 individuals diagnosed with moderate to severe, treatment-resistant depression, underwent two separate dosing sessions with psilocybin. Psychological support was provided before, during and after these sessions and 19 completed fMRI scans one week prior to the first session and one day after the second and last. Neutral, fearful and happy faces were presented in the scanner and analyses focused on the amygdala. Group results revealed rapid and enduring improvements in depressive symptoms post psilocybin. Increased responses to fearful and happy faces were observed in the right amygdala post-treatment, and right amygdala increases to fearful versus neutral faces were predictive of clinical improvements at 1-week. Psilocybin with psychological support was associated with increased amygdala responses to emotional stimuli, an opposite effect to previous findings with SSRIs. This suggests fundamental differences in these treatments' therapeutic actions, with SSRIs mitigating negative emotions and psilocybin allowing patients to confront and work through them. Based on the present results, we propose that psilocybin with psychological support is a treatment approach that potentially revives emotional responsiveness in depression, enabling patients to reconnect with their emotions. ISRCTN, number ISRCTN14426797. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

An IL28B genotype-based clinical prediction model for treatment of chronic hepatitis C.

Directory of Open Access Journals (Sweden)

Thomas R O'Brien

Full Text Available Genetic variation in IL28B and other factors are associated with sustained virological response (SVR after pegylated-interferon/ribavirin treatment for chronic hepatitis C (CHC. Using data from the HALT-C Trial, we developed a model to predict a patient's probability of SVR based on IL28B genotype and clinical variables.HALT-C enrolled patients with advanced CHC who had failed previous interferon-based treatment. Subjects were re-treated with pegylated-interferon/ribavirin during trial lead-in. We used step-wise logistic regression to calculate adjusted odds ratios (aOR and create the predictive model. Leave-one-out cross-validation was used to predict a priori probabilities of SVR and determine area under the receiver operator characteristics curve (AUC.Among 646 HCV genotype 1-infected European American patients, 14.2% achieved SVR. IL28B rs12979860-CC genotype was the strongest predictor of SVR (aOR, 7.56; p10% (43.3% of subjects had an SVR rate of 27.9% and accounted for 84.8% of subjects actually achieving SVR. To verify that consideration of both IL28B genotype and clinical variables is required for treatment decisions, we calculated AUC values from published data for the IDEAL Study.A clinical prediction model based on IL28B genotype and clinical variables can yield useful individualized predictions of the probability of treatment success that could increase SVR rates and decrease the frequency of futile treatment among patients with CHC.

Prediction of response to neoadjuvant chemotherapy in breast cancer: a radiomic study

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Wu, Guolin; Fan, Ming; Zhang, Juan; Zheng, Bin; Li, Lihua

2017-03-01

Breast cancer is one of the most malignancies among women in worldwide. Neoadjuvant Chemotherapy (NACT) has gained interest and is increasingly used in treatment of breast cancer in recent years. Therefore, it is necessary to find a reliable non-invasive assessment and prediction method which can evaluate and predict the response of NACT. Recent studies have highlighted the use of MRI for predicting response to NACT. In addition, molecular subtype could also effectively identify patients who are likely have better prognosis in breast cancer. In this study, a radiomic analysis were performed, by extracting features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and immunohistochemistry (IHC) to determine subtypes. A dataset with fifty-seven breast cancer patients were included, all of them received preoperative MRI examination. Among them, 47 patients had complete response (CR) or partial response (PR) and 10 had stable disease (SD) to chemotherapy based on the RECIST criterion. A total of 216 imaging features including statistical characteristics, morphology, texture and dynamic enhancement were extracted from DCE-MRI. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.923 (P = 0.0002) in leave-one-out crossvalidation. The performance of the classifier increased to 0.960, 0.950 and 0.936 when status of HER2, Luminal A and Luminal B subtypes were added into the statistic model, respectively. The results of this study demonstrated that IHC determined molecular status combined with radiomic features from DCE-MRI could be used as clinical marker that is associated with response to NACT.

Does cognitive flexibility predict treatment gains in Internet-delivered psychological treatment of social anxiety disorder, depression, or tinnitus?

Directory of Open Access Journals (Sweden)

Philip Lindner

2016-04-01

Full Text Available Little is known about the individual factors that predict outcomes in Internet-administered psychological treatments. We hypothesized that greater cognitive flexibility (i.e. the ability to simultaneously consider several concepts and tasks and switch effortlessly between them in response to changes in environmental contingencies would provide a better foundation for learning and employing the cognitive restructuring techniques taught and exercised in therapy, leading to greater treatment gains. Participants in three trials featuring Internet-administered psychological treatments for depression (n = 36, social anxiety disorder (n = 115 and tinnitus (n = 53 completed the 64-card Wisconsin Card Sorting Test (WCST prior to treatment. We found no significant associations between perseverative errors on the WCST and treatment gains in any group. We also found low accuracy in the classification of treatment responders. We conclude that lower cognitive flexibility, as captured by perseverative errors on the WCST, should not impede successful outcomes in Internet-delivered psychological treatments.

Pharmacogenetic markers to predict the clinical response to methotrexate in south Indian Tamil patients with psoriasis.

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Indhumathi, S; Rajappa, Medha; Chandrashekar, Laxmisha; Ananthanarayanan, P H; Thappa, D M; Negi, V S

2017-08-01

Despite the advent of several new systemic therapies, methotrexate remains the gold standard for the treatment of moderate to severe psoriasis. However, there exists a significant heterogeneity in individual response to methotrexate. There are no consistently reliable markers to predict methotrexate treatment response till date. We aimed to demonstrate the association of certain genetic variants in the HLA (HLA-A2, HLA-B17, and HLA-Cw6) and the non-HLA genes including T-helper (Th)-1, Th-2, Th-17 cytokine genes (IFN-γ, IL-2, IL-4, IL-10, IL-12B, and IL-23R), and T-regulatory gene (FOXP3) with the methotrexate treatment response in South Indian Tamil patients with psoriasis. Of the 360 patients recruited, 189 patients with moderate to severe psoriasis were treated with methotrexate. Of the 189 patients, 132 patients responded to methotrexate and the remaining 57 patients were non-responders. We analyzed the association of aforesaid polymorphisms with the methotrexate treatment outcome using binary logistic regression. We observed that there were significant differences between genotype frequencies of HLA-Cw6 and FOXP3 (rs3761548) among the responders compared to non-responders, with conservative estimation. We observed that pro-inflammatory cytokines such as IFN-γ, IL-2, IL-12, and IL-23 were markedly reduced with the use of methotrexate, in comparison to the baseline levels, while the plasma IL-4 levels were increased posttreatment. Our results serve as preliminary evidence for the clinical use of genetic markers as predictors of response to methotrexate in psoriasis. This might aid in the future in the development of a point-of-care testing (POCT) gene chip, to predict optimal treatment response in patients with psoriasis, based on their individual genotypic profile.

The Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures Project: Rationale and Approach.

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MacLean, Paul S; Rothman, Alexander J; Nicastro, Holly L; Czajkowski, Susan M; Agurs-Collins, Tanya; Rice, Elise L; Courcoulas, Anita P; Ryan, Donna H; Bessesen, Daniel H; Loria, Catherine M

2018-04-01

Individual variability in response to multiple modalities of obesity treatment is well documented, yet our understanding of why some individuals respond while others do not is limited. The etiology of this variability is multifactorial; however, at present, we lack a comprehensive evidence base to identify which factors or combination of factors influence treatment response. This paper provides an overview and rationale of the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project, which aims to advance the understanding of individual variability in response to adult obesity treatment. This project provides an integrated model for how factors in the behavioral, biological, environmental, and psychosocial domains may influence obesity treatment responses and identify a core set of measures to be used consistently across adult weight-loss trials. This paper provides the foundation for four companion papers that describe the core measures in detail. The accumulation of data on factors across the four ADOPT domains can inform the design and delivery of effective, tailored obesity treatments. ADOPT provides a framework for how obesity researchers can collectively generate this evidence base and is a first step in an ongoing process that can be refined as the science advances. © 2018 The Obesity Society.

Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma.

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Fowles, Jared S; Brown, Kristen C; Hess, Ann M; Duval, Dawn L; Gustafson, Daniel L

2016-02-19

Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The "COXEN" method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm. The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn't (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124). Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

Response-driven imaging biomarkers for predicting radiation necrosis of the brain

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Nazem-Zadeh, Mohammad-Reza; Chapman, Christopher H; Lawrence, Theodore S; Ten Haken, Randall K; Tsien, Christina I; Cao, Yue; Chenevert, Thomas

2014-01-01

Radiation necrosis is an uncommon but severe adverse effect of brain radiation therapy (RT). Current predictive models based on radiation dose have limited accuracy. We aimed to identify early individual response biomarkers based upon diffusion tensor (DT) imaging and incorporated them into a response model for prediction of radiation necrosis. Twenty-nine patients with glioblastoma received six weeks of intensity modulated RT and concurrent temozolomide. Patients underwent DT-MRI scans before treatment, at three weeks during RT, and one, three, and six months after RT. Cases with radiation necrosis were classified based on generalized equivalent uniform dose (gEUD) of whole brain and DT index early changes in the corpus callosum and its substructures. Significant covariates were used to develop normal tissue complication probability models using binary logistic regression. Seven patients developed radiation necrosis. Percentage changes of radial diffusivity (RD) in the splenium at three weeks during RT and at six months after RT differed significantly between the patients with and without necrosis (p = 0.05 and p = 0.01). Percentage change of RD at three weeks during RT in the 30 Gy dose–volume of the splenium and brain gEUD combined yielded the best-fit logistic regression model. Our findings indicate that early individual response during the course of RT, assessed by radial diffusivity, has the potential to aid the prediction of delayed radiation necrosis, which could provide guidance in dose-escalation trials. (paper)

Texture analysis on MR images helps predicting non-response to NAC in breast cancer

International Nuclear Information System (INIS)

Michoux, N.; Van den Broeck, S.; Lacoste, L.; Fellah, L.; Galant, C.; Berlière, M.; Leconte, I.

2015-01-01

To assess the performance of a predictive model of non-response to neoadjuvant chemotherapy (NAC) in patients with breast cancer based on texture, kinetic, and BI-RADS parameters measured from dynamic MRI. Sixty-nine patients with invasive ductal carcinoma of the breast who underwent pre-treatment MRI were studied. Morphological parameters and biological markers were measured. Pathological complete response was defined as the absence of invasive and in situ cancer in breast and nodes. Pathological non-responders, partial and complete responders were identified. Dynamic imaging was performed at 1.5 T with a 3D axial T1W GRE fat-suppressed sequence. Visual texture, kinetic and BI-RADS parameters were measured in each lesion. ROC analysis and leave-one-out cross-validation were used to assess the performance of individual parameters, then the performance of multi-parametric models in predicting non-response to NAC. A model based on four pre-NAC parameters (inverse difference moment, GLN, LRHGE, wash-in) and k-means clustering as statistical classifier identified non-responders with 84 % sensitivity. BI-RADS mass/non-mass enhancement, biological markers and histological grade did not contribute significantly to the prediction. Pre-NAC texture and kinetic parameters help predicting non-benefit to NAC. Further testing including larger groups of patients with different tumor subtypes is needed to improve the generalization properties and validate the performance of the predictive model

Nomograms Predicting Response to Therapy and Outcomes After Bladder-Preserving Trimodality Therapy for Muscle-Invasive Bladder Cancer

Energy Technology Data Exchange (ETDEWEB)

Coen, John J., E-mail: jcoen@harthosp.org [Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts (United States); Paly, Jonathan J.; Niemierko, Andrzej [Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts (United States); Kaufman, Donald S. [Department of Medical Oncology, Massachusetts General Hospital, Boston, Massachusetts (United States); Heney, Niall M. [Department of Urology, Massachusetts General Hospital, Boston, Massachusetts (United States); Spiegel, Daphne Y.; Efstathiou, Jason A.; Zietman, Anthony L.; Shipley, William U. [Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts (United States)

2013-06-01

Purpose: Selective bladder preservation by use of trimodality therapy is an established management strategy for muscle-invasive bladder cancer. Individual disease features have been associated with response to therapy, likelihood of bladder preservation, and disease-free survival. We developed prognostic nomograms to predict the complete response rate, disease-specific survival, and likelihood of remaining free of recurrent bladder cancer or cystectomy. Methods and Materials: From 1986 to 2009, 325 patients were managed with selective bladder preservation at Massachusetts General Hospital (MGH) and had complete data adequate for nomogram development. Treatment consisted of a transurethral resection of bladder tumor followed by split-course chemoradiation. Patients with a complete response at midtreatment cystoscopic assessment completed radiation, whereas those with a lesser response underwent a prompt cystectomy. Prognostic nomograms were constructed predicting complete response (CR), disease-specific survival (DSS), and bladder-intact disease-free survival (BI-DFS). BI-DFS was defined as the absence of local invasive or regional recurrence, distant metastasis, bladder cancer-related death, or radical cystectomy. Results: The final nomograms included information on clinical T stage, presence of hydronephrosis, whether a visibly complete transurethral resection of bladder tumor was performed, age, sex, and tumor grade. The predictive accuracy of these nomograms was assessed. For complete response, the area under the receiving operating characteristic curve was 0.69. The Harrell concordance index was 0.61 for both DSS and BI-DFS. Conclusions: Our nomograms allow individualized estimates of complete response, DSS, and BI-DFS. They may assist patients and clinicians making important treatment decisions.

Neurofilament light antibodies in serum reflect response to natalizumab treatment in multiple sclerosis.

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Amor, Sandra; van der Star, Baukje J; Bosca, Isabel; Raffel, Joel; Gnanapavan, Sharmilee; Watchorn, Jonathan; Kuhle, Jens; Giovannoni, Gavin; Baker, David; Malaspina, Andrea; Puentes, Fabiola

2014-09-01

Increased levels of antibodies to neurofilament light protein (NF-L) in biological fluids have been found to reflect neuroinflammatory responses and neurodegeneration in multiple sclerosis (MS). To evaluate whether levels of serum antibodies against NF-L correlate with clinical variants and treatment response in MS. The autoantibody reactivity to NF-L protein was tested in serum samples from patients with relapsing-remitting MS (RRMS) (n=22) and secondary progressive MS (SPMS) (n=26). Two other cohorts of RRMS patients under treatment with natalizumab were analysed cross-sectionally (n=16) and longitudinally (n=24). The follow-up samples were taken at 6, 12, 18 and 24 months after treatment, and the NF-L antibody levels were compared against baseline levels. NF-L antibodies were higher in MS clinical groups than healthy controls and in RRMS compared to SPMS patients (ptreatment compared with baseline measurements (p=0.001). Drug efficacy in MS treatment indicates the potential use of monitoring the content of antibodies against the NF-L chain as a predictive biomarker of treatment response in MS. © The Author(s) 2014.

Treatment non-response: Associations with smoking expectancies among treatment-seeking smokers.

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Garey, Lorra; Taha, Samar A; Kauffman, Brooke Y; Manning, Kara F; Neighbors, Clayton; Schmidt, Norman B; Zvolensky, Michael J

2017-10-01

Despite the high rate of smoking cessation treatment non-response, relatively little empirical work has examined predictors of treatment non-response. The present study sought to explore the effect of smoking outcome expectancies on treatment response in a sample of treatment-seeking adult daily smokers (N=182; 53.3% female; M age =40.67; SD=13.63). Results indicated that expectancies for smoking to reduce negative affect were related to an increased likelihood of treatment non-response (OR=0.73, CI: 0.54, 0.98). These findings remained significant after controlling for sex, presence of Axis I disorder, tobacco-related health problems, tobacco dependence, anxiety sensitivity, and condition assignment as well as other smoking expectancy dimensions. Post hoc analyses revealed that this relation was stronger for smokers in the integrated care condition vs. the standard care condition (Interaction: OR=1.69, CI: 1.05, 2.73). Additionally, expectancies for smoking to enhance positive affect and provide sensory satisfaction were associated with an increased likelihood of treatment response in the standard care condition. The current findings suggest expectancies that smoking will alleviate negative affect may be a risk factor of smoking cessation treatment non-response. Additionally, findings provide evidence that the relation between smoking expectancies and treatment non-response may differ by smoking cessation treatment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting albuminuria response to spironolactone treatment with urinary proteomics in patients with type 2 diabetes and hypertension

DEFF Research Database (Denmark)

Lindhardt, Morten; Persson, Frederik; Oxlund, Christina

2018-01-01

to either spironolactone 12.5-50 mg/day (n = 57) or placebo (n = 54) for 16 weeks. Patients were diagnosed with type 2 diabetes and resistant hypertension. Treatment was an adjunct to renin-angiotensin system inhibition. Primary endpoint was the percentage change in urine albumin to creatinine ratio (UACR......BACKGROUND: The mineralocorticoid receptor antagonist spironolactone significantly reduces albuminuria in patients with diabetes. Prior studies have shown large between-patient variability in albuminuria treatment response. We previously developed and validated a urinary proteomic classifier...... be used to identify individuals with type 2 diabetes who are more likely to show an albuminuria-lowering response to spironolactone treatment. These results suggest that urinary proteomics may be a valuable tool to tailor therapy, but confirmation in a larger clinical trial is required....

The utility of PET/CT in staging and assessment of treatment response of naso pharyngeal cancer

International Nuclear Information System (INIS)

Law, Alastair; Peters, L.J.; Dutu, Gaelle; Rischin, Danny; Lau, Eddie; Drummond, Elizabeth; Corry, June

2011-01-01

Full text: The aim of this study was to evaluate the impact of positron emission tomography/computerised tomography (PET/CT) as an adjunct to conventional imaging (CI) in the management of nasopharyngeal cancer (NPC) both for initial staging and assessment of post-treatment response. Methods: All NPC cases referred to the Peter MacCallum Centre for Metabolic Imaging between January 2002 and December 2007 were identified, In patients undergoing initial staging, any differences between the pre PET/CT management plan based on CI and that following performance of the PET/CT scan were noted. Clinical impact was scored using the Centre's published criteria: 'high' if PET /CT changed the primary treatment modality or intent, 'medium' if treatment modality was unchanged but the radiotherapy technique or dose was altered, and 'low' if there was no change in treatment modality or intent. Patients undergoing PET/CT following definitive treatment were scored according to whether or not they achieved a complete metabolic response. Results: Forty-eight patients underwent a staging PET/CT. The clinical impact was high in 8%, medium in 25% and low in 66% of patients. Twenty-one patients were scanned for post-treatment response. PET/CT was less frequently equivocal than MRI (3 vs 8/21). A complete metabolic response on PET /CT was associated with a 93% negative predictive value for subsequent recurrence. Conclusion: PET /CT is a valuable staging tool for the detection of occult metastatic disease and defining the extent of neck nodal disease, Pos treatment, a complete metabolic response on PET /CT has a very high negative predictive value with fewer equivocal results than MRI.

Low-Dose Involved-Field Radiation in the Treatment of Non-Hodgkin Lymphoma: Predictors of Response and Treatment Failure

Energy Technology Data Exchange (ETDEWEB)

Russo, Andrea L., E-mail: alrusso@partners.org [Harvard Radiation Oncology Program, Boston, Massachusetts (United States); Chen, Yu-Hui [Biostatistics Core, Dana Farber Cancer Institute, Boston, Massachusetts (United States); Martin, Neil E.; Vinjamoori, Anant; Luthy, Sarah K. [Department of Radiation Oncology, Brigham and Women' s Hospital and Dana-Farber Cancer Institute, Boston, Massachusetts (United States); Freedman, Arnold [Department of Hematologic Oncology, Brigham and Women' s Hospital and Dana-Farber Cancer Institute, Boston, Massachusetts (United States); Michaelson, Evan M.; Silver, Barbara; Mauch, Peter M.; Ng, Andrea K. [Department of Radiation Oncology, Brigham and Women' s Hospital and Dana-Farber Cancer Institute, Boston, Massachusetts (United States)

2013-05-01

Purpose: To investigate clinical and pathologic factors significant in predicting local response and time to further treatment after low-dose involved-field radiation therapy (LD-IFRT) for non-Hodgkin lymphoma (NHL). Methods and Materials: Records of NHL patients treated at a single institution between April 2004 and September 2011 were retrospectively reviewed. Low-dose involved-field radiation therapy was given as 4 Gy in 2 fractions over 2 consecutive days. Treatment response and disease control were determined by radiographic studies and/or physical examination. A generalized estimating equation model was used to assess the effect of tumor and patient characteristics on disease response. A Cox proportional hazards regression model was used to assess time to further treatment. Results: We treated a total of 187 sites in 127 patients with LD-IFRT. Histologies included 66% follicular, 9% chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma, 10% marginal zone, 6% mantle cell lymphoma (MCL), and 8% other. Median follow-up time was 23.4 months (range, 0.03-92.2 months). The complete response, partial response, and overall response rates were 57%, 25%, and 82%, respectively. A CLL histology was associated with a lower response rate (odds ratio 0.2, 95% confidence interval 0.1-0.5, P=.02). Tumor size, site, age at diagnosis, and prior systemic therapy were not associated with response. The median time to first recurrence was 13.6 months. Those with CLL and age ≤50 years at diagnosis had a shorter time to further treatment for local failures (hazard ratio [HR] 3.63, P=.01 and HR 5.50, P=.02, respectively). Those with CLL and MCL had a shorter time to further treatment for distant failures (HR 11.1 and 16.3, respectively, P<.0001). Conclusions: High local response rates were achieved with LD-IFRT across most histologies. Chronic lymphocytic leukemia and MCL histologies and age ≤50 years at diagnosis had a shorter time to further treatment after LD-IFRT.

Treatment response in child anxiety is differentially related to the form of maternal anxiety disorder

OpenAIRE

Cooper, P. J.; Gallop, C.; Willetts, L.; Creswell, C.

2008-01-01

An examination was made of the extent to which maternal anxiety predicted response to treatment of children presenting with an anxiety disorder. In a sample of 55 children referred to a local NHS CAMH service for treatment of an anxiety disorder, systematic mental state interview assessment was made of both mothers and children, and both completed self-report questionnaires to assess aspects of anxiety, both immediately before the children received treatment and following treatment. Children ...

Predicting variation in subject thermal response during transcranial magnetic resonance guided focused ultrasound surgery: Comparison in seventeen subject datasets

Energy Technology Data Exchange (ETDEWEB)

Vyas, Urvi, E-mail: urvi.vyas@gmail.com; Ghanouni, Pejman; Halpern, Casey H.; Pauly, Kim Butts [Department of Radiology, Stanford University, Stanford, California 94305 (United States); Elias, Jeff [Department of Neurological Surgery, University of Virginia, Charlottesville, Virginia 22908 (United States)

2016-09-15

Purpose: In transcranial magnetic resonance-guided focused ultrasound (tcMRgFUS) treatments, the acoustic and spatial heterogeneity of the skull cause reflection, absorption, and scattering of the acoustic beams. These effects depend on skull-specific parameters and can lead to patient-specific thermal responses to the same transducer power. In this work, the authors develop a simulation tool to help predict these different experimental responses using 3D heterogeneous tissue models based on the subject CT images. The authors then validate and compare the predicted skull efficiencies to an experimental metric based on the subject thermal responses during tcMRgFUS treatments in a dataset of seventeen human subjects. Methods: Seventeen human head CT scans were used to create tissue acoustic models, simulating the effects of reflection, absorption, and scattering of the acoustic beam as it propagates through a heterogeneous skull. The hybrid angular spectrum technique was used to model the acoustic beam propagation of the InSightec ExAblate 4000 head transducer for each subject, yielding maps of the specific absorption rate (SAR). The simulation assumed the transducer was geometrically focused to the thalamus of each subject, and the focal SAR at the target was used as a measure of the simulated skull efficiency. Experimental skull efficiency for each subject was calculated using the thermal temperature maps from the tcMRgFUS treatments. Axial temperature images (with no artifacts) were reconstructed with a single baseline, corrected using a referenceless algorithm. The experimental skull efficiency was calculated by dividing the reconstructed temperature rise 8.8 s after sonication by the applied acoustic power. Results: The simulated skull efficiency using individual-specific heterogeneous models predicts well (R{sup 2} = 0.84) the experimental energy efficiency. Conclusions: This paper presents a simulation model to predict the variation in thermal responses

Predicting response to epigenetic therapy

DEFF Research Database (Denmark)

Treppendahl, Marianne B; Sommer Kristensen, Lasse; Grønbæk, Kirsten

2014-01-01

of good pretreatment predictors of response is of great value. Many clinical parameters and molecular targets have been tested in preclinical and clinical studies with varying results, leaving room for optimization. Here we provide an overview of markers that may predict the efficacy of FDA- and EMA...

A Framework for Prediction of Response to HCV Therapy Using Different Data Mining Techniques

Directory of Open Access Journals (Sweden)

Enas M. F. El Houby

2014-01-01

Full Text Available Hepatitis C which is a widely spread disease all over the world is a fatal liver disease caused by Hepatitis C Virus (HCV. The only approved therapy is interferon plus ribavirin. The number of responders to this treatment is low, while its cost is high and side effects are undesirable. Treatment response prediction will help in reducing the patients who suffer from the side effects and high costs without achieving recovery. The aim of this research is to develop a framework which can select the best model to predict HCV patients’ response to the treatment of HCV from clinical information. The framework contains three phases which are preprocessing phase to prepare the data for applying Data Mining (DM techniques, DM phase to apply different DM techniques, and evaluation phase to evaluate and compare the performance of the built models and select the best model as the recommended one. Different DM techniques had been applied which are associative classification, artificial neural network, and decision tree to evaluate the framework. The experimental results showed the effectiveness of the framework in selecting the best model which is the model built by associative classification using histology activity index, fibrosis stage, and alanine amino transferase.

Predicting response to physiotherapy treatment for musculoskeletal shoulder pain: a systematic review

Science.gov (United States)

2013-01-01

Background People suffering from musculoskeletal shoulder pain are frequently referred to physiotherapy. Physiotherapy generally involves a multimodal approach to management that may include; exercise, manual therapy and techniques to reduce pain. At present it is not possible to predict which patients will respond positively to physiotherapy treatment. The purpose of this systematic review was to identify which prognostic factors are associated with the outcome of physiotherapy in the management of musculoskeletal shoulder pain. Methods A comprehensive search was undertaken of Ovid Medline, EMBASE, CINAHL and AMED (from inception to January 2013). Prospective studies of participants with shoulder pain receiving physiotherapy which investigated the association between baseline prognostic factors and change in pain and function over time were included. Study selection, data extraction and appraisal of study quality were undertaken by two independent assessors. Quality criteria were selected from previously published guidelines to form a checklist of 24 items. The study protocol was prospectively registered onto the International Prospective Register of Systematic Reviews. Results A total of 5023 titles were retrieved and screened for eligibility, 154 articles were assessed as full text and 16 met the inclusion criteria: 11 cohort studies, 3 randomised controlled trials and 2 controlled trials. Results were presented for the 9 studies meeting 13 or more of the 24 quality criteria. Clinical and statistical heterogeneity resulted in qualitative synthesis rather than meta-analysis. Three studies demonstrated that high functional disability at baseline was associated with poor functional outcome (p ≤ 0.05). Four studies demonstrated a significant association (p ≤ 0.05) between longer duration of shoulder pain and poorer outcome. Three studies, demonstrated a significant association (p ≤ 0.05) between increasing age and poorer function; three studies

Is response to anti-hepatitis C virus treatment predictive of mortality in hepatitis C virus/HIV-positive patients?

DEFF Research Database (Denmark)

Peters, Lars; Raben, Dorthe

2017-01-01

BACKGROUND: Long-term clinical outcomes after hepatitis C virus (HCV) treatment of HIV/HCV patients are not well described. We aimed to compare the risk of all-cause and liver-related death (LRD) according to HCV treatment response in HIV/HCV patients in the multicohort study Collaboration...... of Observational HIV Epidemiological Research in Europe. METHODS: All patients who had started pegylated interferon + ribavirin (baseline) and followed for at least 72 weeks after baseline were included. Patients were categorized into three response groups depending on treatment duration and HCV-RNA measured...... in the window 24-72 weeks after baseline. Patients who received at least 24 weeks of therapy were defined as responders if their last HCV-RNA measured between 24 and 72 weeks after baseline was negative, and having 'unknown response' if HCV-RNA was unknown. Nonresponders were treated for less than 24 weeks...

Sputum eosinophilia can predict responsiveness to inhaled corticosteroid treatment in patients with overlap syndrome of COPD and asthma.

Science.gov (United States)

Kitaguchi, Yoshiaki; Komatsu, Yoshimichi; Fujimoto, Keisaku; Hanaoka, Masayuki; Kubo, Keishi

2012-01-01

Chronic obstructive pulmonary disease (COPD) and asthma may overlap and converge in older people (overlap syndrome). It was hypothesized that patients with overlap syndrome may have different clinical characteristics such as sputum eosinophilia, and better responsiveness to treatment with inhaled corticosteroid (ICS). Sixty-three patients with stable COPD (forced expiratory volume in 1 second [FEV(1)] ≤80%) underwent pulmonary function tests, including reversibility of airflow limitation, arterial blood gas analysis, analysis of inflammatory cells in induced sputum, and chest high-resolution computed tomography. The inclusion criteria for COPD patients with asthmatic symptoms included having asthmatic symptoms such as episodic breathlessness, wheezing, cough, and chest tightness worsening at night or in the early morning (COPD with asthma group). The clinical features of COPD patients with asthmatic symptoms were compared with those of COPD patients without asthmatic symptoms (COPD without asthma group). The increases in FEV(1) in response to treatment with ICS were significantly higher in the COPD with asthma group. The peripheral eosinophil counts and sputum eosinophil counts were significantly higher. The prevalence of patients with bronchial wall thickening on chest high-resolution computed tomography was significantly higher. A significant correlation was observed between the increases in FEV(1) in response to treatment with ICS and sputum eosinophil counts, and between the increases in FEV(1) in response to treatment with ICS and the grade of bronchial wall thickening. Receiver operating characteristic curve analysis revealed 82.4% sensitivity and 84.8% specificity of sputum eosinophil count for detecting COPD with asthma, using 2.5% as the cutoff value. COPD patients with asthmatic symptoms had some clinical features. ICS should be considered earlier as a potential treatment in such patients. High sputum eosinophil counts and bronchial wall thickening on

Prediction of outcome of bright light treatment in patients with seasonal affective disorder: Discarding the early response, confirming a higher atypical balance, and uncovering a higher body mass index at baseline as predictors of endpoint outcome.

Science.gov (United States)

Dimitrova, Tzvetelina D; Reeves, Gloria M; Snitker, Soren; Lapidus, Manana; Sleemi, Aamar R; Balis, Theodora G; Manalai, Partam; Tariq, Muhammad M; Cabassa, Johanna A; Karim, Naila N; Johnson, Mary A; Langenberg, Patricia; Rohan, Kelly J; Miller, Michael; Stiller, John W; Postolache, Teodor T

2017-11-01

We tested the hypothesis that the early improvement in mood after the first hour of bright light treatment compared to control dim-red light would predict the outcome at six weeks of bright light treatment for depressed mood in patients with Seasonal Affective Disorder (SAD). We also analyzed the value of Body Mass Index (BMI) and atypical symptoms of depression at baseline in predicting treatment outcome. Seventy-eight adult participants were enrolled. The first treatment was controlled crossover, with randomized order, and included one hour of active bright light treatment and one hour of control dim-red light, with one-hour washout. Depression was measured on the Structured Interview Guide for the Hamilton Rating Scale for Depression-SAD version (SIGH-SAD). The predictive association of depression scores changes after the first session. BMI and atypical score balance with treatment outcomes at endpoint were assessed using multivariable linear and logistic regressions. No significant prediction by changes in depression scores after the first session was found. However, higher atypical balance scores and BMI positively predicted treatment outcome. Absence of a control intervention for the six-weeks of treatment (only the first session in the laboratory was controlled). Exclusion of patients with comorbid substance abuse, suicidality and bipolar I disorder, and patients on antidepressant medications, reducing the generalizability of the study. Prediction of outcome by early response to light treatment was not replicated, and the previously reported prediction of baseline atypical balance was confirmed. BMI, a parameter routinely calculated in primary care, was identified as a novel predictor, and calls for replication and then exploration of possible mediating mechanisms. Published by Elsevier B.V.

Balancing treatment allocations by clinician or center in randomized trials allows unacceptable levels of treatment prediction.

Science.gov (United States)

Hills, Robert K; Gray, Richard; Wheatley, Keith

2009-08-01

Randomized controlled trials are the standard method for comparing treatments because they avoid the selection bias that might arise if clinicians were free to choose which treatment a patient would receive. In practice, allocation of treatments in randomized controlled trials is often not wholly random with various 'pseudo-randomization' methods, such as minimization or balanced blocks, used to ensure good balance between treatments within potentially important prognostic or predictive subgroups. These methods avoid selection bias so long as full concealment of the next treatment allocation is maintained. There is concern, however, that pseudo-random methods may allow clinicians to predict future treatment allocations from previous allocation history, particularly if allocations are balanced by clinician or center. We investigate here to what extent treatment prediction is possible. Using computer simulations of minimization and balanced block randomizations, the success rates of various prediction strategies were investigated for varying numbers of stratification variables, including the patient's clinician. Prediction rates for minimization and balanced block randomization typically exceed 60% when clinician is included as a stratification variable and, under certain circumstances, can exceed 80%. Increasing the number of clinicians and other stratification variables did not greatly reduce the prediction rates. Without clinician as a stratification variable, prediction rates are poor unless few clinicians participate. Prediction rates are unacceptably high when allocations are balanced by clinician or by center. This could easily lead to selection bias that might suggest spurious, or mask real, treatment effects. Unless treatment is blinded, randomization should not be balanced by clinician (or by center), and clinician-center effects should be allowed for instead by retrospectively stratified analyses. © 2009 Blackwell Publishing Asia Pty Ltd and Chinese

Methods to model and predict the ViewRay treatment deliveries to aid patient scheduling and treatment planning.

Science.gov (United States)

Liu, Shi; Wu, Yu; Wooten, H Omar; Green, Olga; Archer, Brent; Li, Harold; Yang, Deshan

2016-03-08

A software tool is developed, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance image-guided radiation therapy (MR-IGRT) delivery system. This tool is necessary for managing patient treatment scheduling in our clinic. The predicted treatment delivery time and the assessment of plan complexities could also be useful to aid treatment planning. A patient's total treatment delivery time, not including time required for localization, is modeled as the sum of four components: 1) the treatment initialization time; 2) the total beam-on time; 3) the gantry rotation time; and 4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam-on time can be calculated using both the planned beam-on time and the decay-corrected dose rate. To predict the remain-ing components, we retrospectively analyzed the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, linear regression is applied to predict the gantry rotation time. The MLC motion time is calculated using the leaves delay modeling method and the leaf motion speed. A quantitative analysis was performed to understand the correlation between the total treatment time and the plan complexity. The proposed algorithm is able to predict the ViewRay treatment delivery time with the average prediction error 0.22min or 1.82%, and the maximal prediction error 0.89 min or 7.88%. The analysis has shown the correlation between the plan modulation (PM) factor and the total treatment delivery time, as well as the treatment delivery duty cycle. A possibility has been identified to significantly reduce MLC motion time by optimizing the positions of closed MLC pairs. The accuracy of

Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks.

Directory of Open Access Journals (Sweden)

Petros-Pavlos Ypsilantis

Full Text Available Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient's response to neoadjuvant chemotherapy from a single 18F-FDG PET scan taken prior to treatment. We take a "radiomics" approach whereby a large amount of quantitative features is automatically extracted from pretherapy PET images in order to build a comprehensive quantification of the tumor phenotype. While the dominant methodology relies on hand-crafted texture features, we explore the potential of automatically learning low- to high-level features directly from PET scans. We report on a study that compares the performance of two competing radiomics strategies: an approach based on state-of-the-art statistical classifiers using over 100 quantitative imaging descriptors, including texture features as well as standardized uptake values, and a convolutional neural network, 3S-CNN, trained directly from PET scans by taking sets of adjacent intra-tumor slices. Our experimental results, based on a sample of 107 patients with esophageal cancer, provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy. On this dataset, 3S-CNN achieves an average 80.7% sensitivity and 81.6% specificity in predicting non-responders, and outperforms other competing predictive models.

Predictive and prognostic factors associated with soft tissue sarcoma response to chemotherapy

DEFF Research Database (Denmark)

Young, Robin J; Litière, Saskia; Lia, Michela

2017-01-01

BACKGROUND: The European Organization for Research and Treatment of Cancer (EORTC) 62012 study was a Phase III trial of doxorubicin versus doxorubicin-ifosfamide chemotherapy in 455 patients with advanced soft tissue sarcoma (STS). Analysis of the main study showed that combination chemotherapy...... improved tumor response and progression-free survival, but differences in overall survival (OS) were not statistically significant. We analyzed factors prognostic for tumor response and OS, and assessed histological subgroup and tumor grade as predictive factors to identify patients more likely to benefit...... patients had improved tumor response compared to other histological subgroups, whilst patients with metastases other than lung, liver or bone had a poorer response [odds ratio (OR) 0.42, 95% confidence interval (CI) 0.23-0.78; p = 0.006]. Patients with bone metastases had reduced OS [hazard ratio (HR) 1...

Combined-modality treatment and organ preservation in bladder cancer. Do molecular markers predict outcome?

International Nuclear Information System (INIS)

Weiss, C.; Roedel, F.; Wolf, I.; Sauer, R.; Roedel, C.; Papadopoulos, T.; Engehausen, D.G.; Schrott, K.M.

2005-01-01

Purpose: in invasive bladder cancer, several groups have reported the value of organ preservation by a combined-treatment approach, including transurethral resection (TUR-BT) and radiochemotherapy (RCT). As more experience is acquired with this organ-sparing treatment, patient selection needs to be optimized. Clinical factors are limited in their potential to identify patients most likely to respond to RCT, thus, additional molecular markers for predicting treatment response of individual lesions are sorely needed. Patients and methods: the apoptotic index (AI) and Ki-67 index were evaluated by immunohistochemistry on pretreatment biopsies of 134 patients treated for bladder cancer by TUR-BT and RCT. Expression of each marker as well as clinicopathologic factors were then correlated with initial response, local control and cancer-specific survival with preserved bladder in univariate and multivariate analysis. Results: the median AI for all patients was 1.5% (range 0.2-7.4%). The percentage of Ki-67-positive cells in the tumors ranged from 0.2% to 85% with a median of 14.2%. A significant correlation was found for AI and tumor differentiation (G1/2: AI = 1.3% vs. G3/4: AI = 1.6%; p = 0.01). A complete response at restaging TUR-BT was achieved in 76% of patients. Factors predictive of complete response included T-category (p < 0.0001), resection status (p = 0.02), lymphovascular invasion (p = 0.01), and Ki-67 index (p = 0.02). For local control, AI (p = 0.04) and Ki-67 index (p = 0.05) as well as T-category (p = 0.005), R-status (p = 0.05), and lymphatic vessel invasion (p = 0.05) reached statistical significance. Out of the molecular markers only high Ki-67 levels were associated to cause-specific survival with preserved bladder. On multivariate analysis, T-category was the strongest independent factor for initial response, local control and cancer-specific survival with preserved bladder. Conclusion: The indices of pretreatment apoptosis and Ki-67 predict a

Prediction Models for Dynamic Demand Response

Energy Technology Data Exchange (ETDEWEB)

Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.

2015-11-02

As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D²R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D²R, which we address in this paper. Our first contribution is the formal definition of D²R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D²R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D²R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D²R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D²R. Also, prediction models require just few days’ worth of data indicating that small amounts of

Individual response to ionising radiation: What predictive assay(s) to choose?

International Nuclear Information System (INIS)

Granzotto, A.; Viau, M.; Devic, C.; Maalouf, M.; Thomas, Ch.; Vogin, G.; Foray, N.; Granzotto, A.; Vogin, G.; Balosso, J.; Joubert, A.; Maalouf, M.; Vogin, G.; Colin, C.; Malek, K.; Balosso, J.; Colin, C.

2011-01-01

Individual response to ionizing radiation is an important information required to apply an efficient radiotherapy treatment against tumour and to avoid any adverse effects in normal tissues. In 1981, Fertil and Malaise have demonstrated that the post-irradiation local tumor control determined in vivo is correlated with clonogenic cell survival assessed in vitro. Furthermore, these authors have reminded the relevance of the concept of intrinsic radiosensitivity that is specific to each individual organ (Fertil and Malaise, 1981) [1]. To date, since clonogenicity assays are too time-consuming and do not provide any other molecular information, a plethora of research groups have attempted to determine the molecular bases of intrinsic radiosensitivity in order to propose reliable and faster predictive assays. To this aim, several approaches have been developed. Notably, the recent revolution in genomic and proteomics technologies is providing a considerable number of data but their link with radiosensitivity still remains to be elucidated. On another hand, the systematic screening of some candidate genes potentially involved in the radiation response is highlighting the complexity of the molecular and cellular mechanisms of DNA damage sensing and signalling and shows that an abnormal radiation response is not necessarily due to the impairment of one single protein. Finally, more modest approaches consisting in focusing some specific functions of DNA repair seem to provide more reliable clues to predict over-acute reactions caused by radiotherapy. In this review, we endeavored to analyse the contributions of these major approaches to predict human radiosensitivity. (authors)

COX-2 verexpression in pretreatment biopsies predicts response of rectal cancers to neoadjuvant radiochemotherapy

International Nuclear Information System (INIS)

Smith, Fraser M.; Reynolds, John V.; Kay, Elaine W.; Crotty, Paul; Murphy, James O.; Hollywood, Donal; Gaffney, Eoin F.; Stephens, Richard B.; Kennedy, M. John

2006-01-01

Purpose: To determine the utility of COX-2 expression as a response predictor for patients with rectal cancer who are undergoing neoadjuvant radiochemotherapy (RCT). Methods and Materials: Pretreatment biopsies (PTB) from 49 patients who underwent RCT were included. COX-2 and proliferation in PTB were assessed by immunohistochemistry (IHC) and apoptosis was detected by TUNEL stain. Response to treatment was assessed by a 5-point tumor-regression grade (TRG) based on the ratio of residual tumor to fibrosis. Results: Good response (TRG 1 + 2), moderate response (TRG 3), and poor response (TRG 4 + 5) were seen in 21 patients (42%), 11 patients (22%), and 17 patients (34%), respectively. Patients with COX-2 overexpression in PTB were more likely to demonstrate moderate or poor response (TRG 3 + 4) to treatment than were those with normal COX-2 expression (p = 0.026, chi-square test). Similarly, poor response was more likely if patients had low levels of spontaneous apoptosis in PTBs (p = 0.0007, chi-square test). Conclusions: COX-2 overexpression and reduced apoptosis in PTB can predict poor response of rectal cancer to RCT. As COX-2 inhibitors are commercially available, their administration to patients who overexpress COX-2 warrants assessment in clinical trials in an attempt to increase overall response rates

Brain-behavioral adaptability predicts response to cognitive behavioral therapy for emotional disorders: A person-centered event-related potential study.

Science.gov (United States)

Stange, Jonathan P; MacNamara, Annmarie; Kennedy, Amy E; Hajcak, Greg; Phan, K Luan; Klumpp, Heide

2017-06-23

Single-trial-level analyses afford the ability to link neural indices of elaborative attention (such as the late positive potential [LPP], an event-related potential) with downstream markers of attentional processing (such as reaction time [RT]). This approach can provide useful information about individual differences in information processing, such as the ability to adapt behavior based on attentional demands ("brain-behavioral adaptability"). Anxiety and depression are associated with maladaptive information processing implicating aberrant cognition-emotion interactions, but whether brain-behavioral adaptability predicts response to psychotherapy is not known. We used a novel person-centered, trial-level analysis approach to link neural indices of stimulus processing to behavioral responses and to predict treatment outcome. Thirty-nine patients with anxiety and/or depression received 12 weeks of cognitive behavioral therapy (CBT). Prior to treatment, patients performed a speeded reaction-time task involving briefly-presented pairs of aversive and neutral pictures while electroencephalography was recorded. Multilevel modeling demonstrated that larger LPPs predicted slower responses on subsequent trials, suggesting that increased attention to the task-irrelevant nature of pictures interfered with reaction time on subsequent trials. Whereas using LPP and RT averages did not distinguish CBT responders from nonresponders, in trial-level analyses individuals who demonstrated greater ability to benefit behaviorally (i.e., faster RT) from smaller LPPs on the previous trial (greater brain-behavioral adaptability) were more likely to respond to treatment and showed greater improvements in depressive symptoms. These results highlight the utility of trial-level analyses to elucidate variability in within-subjects, brain-behavioral attentional coupling in the context of emotion processing, in predicting response to CBT for emotional disorders. Copyright © 2017 Elsevier Ltd

MYC Amplification as a Predictive Factor of Complete Pathologic Response to Docetaxel-based Neoadjuvant Chemotherapy for Breast Cancer.

Science.gov (United States)

Pereira, Cynthia Brito Lins; Leal, Mariana Ferreira; Abdelhay, Eliana Saul Furquim Werneck; Demachki, Sâmia; Assumpção, Paulo Pimentel; de Souza, Mirian Carvalho; Moreira-Nunes, Caroline Aquino; Tanaka, Adriana Michiko da Silva; Smith, Marília Cardoso; Burbano, Rommel Rodríguez

2017-06-01

Neoadjuvant chemotherapy is a standard treatment for stage II and III breast cancer. The identification of biomarkers that may help in the prediction of response to neoadjuvant therapies is necessary for a more precise definition of the best drug or drug combination to induce a better response. We assessed the role of Ki67, hormone receptors expression, HER2, MYC genes and their protein status, and KRAS codon 12 mutations as predictor factors of pathologic response to anthracycline-cyclophosphamide (AC) followed by taxane docetaxel (T) neoadjuvant chemotherapy (AC+T regimen) in 51 patients with invasive ductal breast cancer. After neoadjuvant chemotherapy, 82.4% of patients showed pathologic partial response, with only 9.8% showing pathologic complete response. In multivariate analysis, MYC immunoreactivity and high MYC gain defined as MYC/nucleus ≥ 5 were significant predictor factors for pathologic partial response. Using the receiver operating characteristic curve analysis, the ratio of 2.5 MYC/CEP8 (sensitivity of 80% and specificity of 89.1%) or 7 MYC/nuclei copies (sensitivity of 80% and specificity of 73.9%) as the best cutoff in predicting a pathologic complete response was identified. Thus, MYC may have a role in chemosensitivity to AC and/or docetaxel drugs. Additionally, MYC amplification may be a predictor factor of pathologic response to the AC+T regimen in patients with breast cancer. Moreover, patients with an increased number of MYC copies showed pathologic complete response to this neoadjuvant treatment more frequently. The analysis of MYC amplification may help in the identification of patients that may have a better response to AC+T treatment. Copyright © 2016 Elsevier Inc. All rights reserved.

Prediction of clinical response to drugs in ovarian cancer using the chemotherapy resistance test (CTR-test).

Science.gov (United States)

Kischkel, Frank Christian; Meyer, Carina; Eich, Julia; Nassir, Mani; Mentze, Monika; Braicu, Ioana; Kopp-Schneider, Annette; Sehouli, Jalid

2017-10-27

In order to validate if the test result of the Chemotherapy Resistance Test (CTR-Test) is able to predict the resistances or sensitivities of tumors in ovarian cancer patients to drugs, the CTR-Test result and the corresponding clinical response of individual patients were correlated retrospectively. Results were compared to previous recorded correlations. The CTR-Test was performed on tumor samples from 52 ovarian cancer patients for specific chemotherapeutic drugs. Patients were treated with monotherapies or drug combinations. Resistances were classified as extreme (ER), medium (MR) or slight (SR) resistance in the CTR-Test. Combination treatment resistances were transformed by a scoring system into these classifications. Accurate sensitivity prediction was accomplished in 79% of the cases and accurate prediction of resistance in 100% of the cases in the total data set. The data set of single agent treatment and drug combination treatment were analyzed individually. Single agent treatment lead to an accurate sensitivity in 44% of the cases and the drug combination to 95% accuracy. The detection of resistances was in both cases to 100% correct. ROC curve analysis indicates that the CTR-Test result correlates with the clinical response, at least for the combination chemotherapy. Those values are similar or better than the values from a publication from 1990. Chemotherapy resistance testing in vitro via the CTR-Test is able to accurately detect resistances in ovarian cancer patients. These numbers confirm and even exceed results published in 1990. Better sensitivity detection might be caused by a higher percentage of drug combinations tested in 2012 compared to 1990. Our study confirms the functionality of the CTR-Test to plan an efficient chemotherapeutic treatment for ovarian cancer patients.

Factors predictive of sustained virological response following 72 weeks of combination therapy for genotype 1b hepatitis C.

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Chayama, Kazuaki; Hayes, C Nelson; Yoshioka, Kentaro; Moriwaki, Hisataka; Okanoue, Takashi; Sakisaka, Shotaro; Takehara, Tetsuo; Oketani, Makoto; Toyota, Joji; Izumi, Namiki; Hiasa, Yoichi; Matsumoto, Akihiro; Nomura, Hideyuki; Seike, Masataka; Ueno, Yoshiyuki; Yotsuyanagi, Hiroshi; Kumada, Hiromitsu

2011-04-01

Treatment of genotype 1b chronic hepatitis C virus (HCV) infection has been improved by extending peg-interferon plus ribavirin combination therapy to 72 weeks, but predictive factors are needed to identify those patients who are likely to respond to long-term therapy. We analyzed amino acid (aa) substitutions in the core protein and the interferon sensitivity determining region (ISDR) of nonstructural protein (NS) 5A in 840 genotype 1b chronic hepatitis C patients with high viral load. We used logistic regression and classification and regression tree (CART) analysis to identify predictive factors for sustained virological response (SVR) for patients undergoing 72 weeks of treatment. When patients were separately analyzed by treatment duration using multivariate logistic regression, several factors, including sex, age, viral load, and core aa70 and ISDR substitutions (P = 0.0003, P = 0.02, P = 0.01, P = 0.0001, and P = 0.0004, respectively) were significant predictive factors for SVR with 48 weeks of treatment, whereas age, previous interferon treatment history, and ISDR substitutions (P = 0.03, P = 0.01, and P = 0.02, respectively) were the only significant predictive factors with 72 weeks of treatment. Using CART analysis, a decision tree was generated that identified age, cholesterol, sex, treatment length, and aa70 and ISDR substitutions as the most important predictive factors. The CART model had a sensitivity of 69.2% and specificity of 60%, with a positive predictive value of 68.4%. Complementary statistical and data mining approaches were used to identify a subgroup of patients likely to benefit from 72 weeks of therapy.

GWAS-based machine learning approach to predict duloxetine response in major depressive disorder.

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Maciukiewicz, Malgorzata; Marshe, Victoria S; Hauschild, Anne-Christin; Foster, Jane A; Rotzinger, Susan; Kennedy, James L; Kennedy, Sidney H; Müller, Daniel J; Geraci, Joseph

2018-04-01

Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders and is commonly treated with antidepressant drugs. However, large variability is observed in terms of response to antidepressants. Machine learning (ML) models may be useful to predict treatment outcomes. A sample of 186 MDD patients received treatment with duloxetine for up to 8 weeks were categorized as "responders" based on a MADRS change >50% from baseline; or "remitters" based on a MADRS score ≤10 at end point. The initial dataset (N = 186) was randomly divided into training and test sets in a nested 5-fold cross-validation, where 80% was used as a training set and 20% made up five independent test sets. We performed genome-wide logistic regression to identify potentially significant variants related to duloxetine response/remission and extracted the most promising predictors using LASSO regression. Subsequently, classification-regression trees (CRT) and support vector machines (SVM) were applied to construct models, using ten-fold cross-validation. With regards to response, none of the pairs performed significantly better than chance (accuracy p > .1). For remission, SVM achieved moderate performance with an accuracy = 0.52, a sensitivity = 0.58, and a specificity = 0.46, and 0.51 for all coefficients for CRT. The best performing SVM fold was characterized by an accuracy = 0.66 (p = .071), sensitivity = 0.70 and a sensitivity = 0.61. In this study, the potential of using GWAS data to predict duloxetine outcomes was examined using ML models. The models were characterized by a promising sensitivity, but specificity remained moderate at best. The inclusion of additional non-genetic variables to create integrated models may improve prediction. Copyright © 2017. Published by Elsevier Ltd.

Predicting the response of olfactory sensory neurons to odor mixtures from single odor response

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Marasco, Addolorata; de Paris, Alessandro; Migliore, Michele

2016-04-01

The response of olfactory receptor neurons to odor mixtures is not well understood. Here, using experimental constraints, we investigate the mathematical structure of the odor response space and its consequences. The analysis suggests that the odor response space is 3-dimensional, and predicts that the dose-response curve of an odor receptor can be obtained, in most cases, from three primary components with specific properties. This opens the way to an objective procedure to obtain specific olfactory receptor responses by manipulating mixtures in a mathematically predictable manner. This result is general and applies, independently of the number of odor components, to any olfactory sensory neuron type with a response curve that can be represented as a sigmoidal function of the odor concentration.

Treatment-Related Predictive and Prognostic Factors in Trimodality Approach in Stage IIIA/N2 Non-Small Cell Lung Cancer.

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Jeremić, Branislav; Casas, Francesc; Dubinsky, Pavol; Gomez-Caamano, Antonio; Čihorić, Nikola; Videtic, Gregory; Igrutinovic, Ivan

2018-01-01

While there are no established pretreatment predictive and prognostic factors in patients with stage IIIA/pN2 non-small cell lung cancer (NSCLC) indicating a benefit to surgery as a part of trimodality approach, little is known about treatment-related predictive and prognostic factors in this setting. A literature search was conducted to identify possible treatment-related predictive and prognostic factors for patients for whom trimodality approach was reported on. Overall survival was the primary endpoint of this study. Of 30 identified studies, there were two phase II studies, 5 "prospective" studies, and 23 retrospective studies. No study was found which specifically looked at treatment-related predictive factors of improved outcomes in trimodality treatment. Of potential treatment-related prognostic factors, the least frequently analyzed factors among 30 available studies were overall pathologic stage after preoperative treatment and UICC downstaging. Evaluation of treatment response before surgery and by pathologic tumor stage after induction therapy were analyzed in slightly more than 40% of studies and found not to influence survival. More frequently studied factors-resection status, degree of tumor regression, and pathologic nodal stage after induction therapy as well as the most frequently studied factor, the treatment (in almost 75% studies)-showed no discernible impact on survival, due to conflicting results. Currently, it is impossible to identify any treatment-related predictive or prognostic factors for selecting surgery in the treatment of patients with stage IIIA/pN2 NSCLC.

Assays for predicting and monitoring responses to lung cancer immunotherapy

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Teixidó, Cristina; Karachaliou, Niki; González-Cao, Maria; Morales-Espinosa, Daniela; Rosell, Rafael

2015-01-01

Immunotherapy has become a key strategy for cancer treatment, and two immune checkpoints, namely, programmed cell death 1 (PD-1) and its ligand (PD-L1), have recently emerged as important targets. The interaction blockade of PD-1 and PD-L1 demonstrated promising activity and antitumor efficacy in early phase clinical trials for advanced solid tumors such as non-small cell lung cancer (NSCLC). Many cell types in multiple tissues express PD-L1 as well as several tumor types, thereby suggesting that the ligand may play important roles in inhibiting immune responses throughout the body. Therefore, PD-L1 is a critical immunomodulating component within the lung microenvironment, but the correlation between PD-L1 expression and prognosis is controversial. More evidence is required to support the use of PD-L1 as a potential predictive biomarker. Clinical trials have measured PD-L1 in tumor tissues by immunohistochemistry (IHC) with different antibodies, but the assessment of PD-L1 is not yet standardized. Some commercial antibodies lack specificity and their reproducibility has not been fully evaluated. Further studies are required to clarify the optimal IHC assay as well as to predict and monitor the immune responses of the PD-1/PD-L1 pathway

Dopamine reward prediction error responses reflect marginal utility.

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Stauffer, William R; Lak, Armin; Schultz, Wolfram

2014-11-03

Optimal choices require an accurate neuronal representation of economic value. In economics, utility functions are mathematical representations of subjective value that can be constructed from choices under risk. Utility usually exhibits a nonlinear relationship to physical reward value that corresponds to risk attitudes and reflects the increasing or decreasing marginal utility obtained with each additional unit of reward. Accordingly, neuronal reward responses coding utility should robustly reflect this nonlinearity. In two monkeys, we measured utility as a function of physical reward value from meaningful choices under risk (that adhered to first- and second-order stochastic dominance). The resulting nonlinear utility functions predicted the certainty equivalents for new gambles, indicating that the functions' shapes were meaningful. The monkeys were risk seeking (convex utility function) for low reward and risk avoiding (concave utility function) with higher amounts. Critically, the dopamine prediction error responses at the time of reward itself reflected the nonlinear utility functions measured at the time of choices. In particular, the reward response magnitude depended on the first derivative of the utility function and thus reflected the marginal utility. Furthermore, dopamine responses recorded outside of the task reflected the marginal utility of unpredicted reward. Accordingly, these responses were sufficient to train reinforcement learning models to predict the behaviorally defined expected utility of gambles. These data suggest a neuronal manifestation of marginal utility in dopamine neurons and indicate a common neuronal basis for fundamental explanatory constructs in animal learning theory (prediction error) and economic decision theory (marginal utility). Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Predicting fluid responsiveness with transthoracic echocardiography is not yet evidence based

DEFF Research Database (Denmark)

Wetterslev, M; Haase, N; Johansen, R R

2013-01-01

an integrated tool in the intensive care unit, this systematic review examined studies evaluating the predictive value of TTE for fluid responsiveness. In October 2012, we searched Pubmed, EMBASE and Web of Science for studies evaluating the predictive value of TTE-derived variables for fluid responsiveness...... responsiveness. Of the 4294 evaluated citations, only one study fully met our inclusion criteria. In this study, the predictive value of variations in inferior vena cava diameter (> 16%) for fluid responsiveness was moderate with sensitivity of 71% [95% confidence interval (CI) 44-90], specificity of 100% (95......% CI 73-100) and an area under the receiver operating curve of 0.90 (95% CI 0.73-0.98). Only one study of TTE-based methods fulfilled the criteria for valid assessment of fluid responsiveness. Before recommending the use of TTE in predicting fluid responsiveness, proper evaluation including...

Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures: Behavioral Domain.

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Lytle, Leslie A; Nicastro, Holly L; Roberts, Susan B; Evans, Mary; Jakicic, John M; Laposky, Aaron D; Loria, Catherine M

2018-04-01

The ability to identify and measure behaviors that are related to weight loss and the prevention of weight regain is crucial to understanding the variability in response to obesity treatment and the development of tailored treatments. The overarching goal of the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project is to provide obesity researchers with guidance on a set of constructs and measures that are related to weight control and that span and integrate obesity-related behavioral, biological, environmental, and psychosocial domains. This article describes how the behavioral domain subgroup identified the initial list of high-priority constructs and measures to be included, and it describes practical considerations for assessing the following four behavioral areas: eating, activity, sleep, and self-monitoring of weight. Challenges and considerations for advancing the science related to weight loss and maintenance behaviors are also discussed. Assessing a set of core behavioral measures in combination with those from other ADOPT domains is critical to improve our understanding of individual variability in response to adult obesity treatment. The selection of behavioral measures is based on the current science, although there continues to be much work needed in this field. © 2018 The Obesity Society.

Family Factors Predict Treatment Outcome for Pediatric Obsessive Compulsive Disorder

Science.gov (United States)

Peris, Tara S.; Sugar, Catherine A.; Bergman, R. Lindsey; Chang, Susanna; Langley, Audra; Piacentini, John

2012-01-01

Objective To examine family conflict, parental blame, and poor family cohesion as predictors of treatment outcome for youth receiving family-focused cognitive behavioral therapy (FCBT) for obsessive compulsive disorder (OCD). Methods We analyzed data from a sample of youth who were randomized to FCBT (n = 49; 59% male; mean age = 12.43 years) as part of a larger randomized clinical trial. Youngsters and their families were assessed by an independent evaluator (IE) pre- and post- FCBT using a standardized battery of measures evaluating family functioning and OCD symptom severity. Family conflict and cohesion were measured via parent self-report on the Family Environment Scale (FES; Moos & Moos, 1994) and parental blame was measured using parent self-report on the Parental Attitudes and Behaviors Scale (PABS; Peris, 2008b). Symptom severity was rated by IE’s using the Children’s Yale-Brown Obsessive Compulsive Scale (CY-BOCS; Scahill et al., 1997). Results Families with lower levels of parental blame and family conflict and higher levels of family cohesion at baseline were more likely to have a child who responded to FCBT treatment even after adjusting for baseline symptom severity compared to families who endorsed higher levels of dysfunction prior to treatment. In analyses using both categorical and continuous outcome measures, higher levels of family dysfunction and difficulty in higher number of domains of family functioning were associated with lower rates of treatment response. In addition, changes in family cohesion predicted response to FCBT controlling for baseline symptom severity. Conclusions Findings speak to the role of the family in treatment for childhood OCD and highlight potential targets for future family interventions. PMID:22309471

Pretreatment Growth Rate Predicts Radiation Response in Vestibular Schwannomas

International Nuclear Information System (INIS)

Niu, Nina N.; Niemierko, Andrzej; Larvie, Mykol; Curtin, Hugh; Loeffler, Jay S.; McKenna, Michael J.; Shih, Helen A.

2014-01-01

Purpose: Vestibular schwannomas (VS) are often followed without initial therapeutic intervention because many tumors do not grow and radiation therapy is associated with potential adverse effects. In an effort to determine whether maximizing initial surveillance predicts for later treatment response, the predictive value of preirradiation growth rate of VS on response to radiation therapy was assessed. Methods and Materials: Sixty-four patients with 65 VS were treated with single-fraction stereotactic radiation surgery or fractionated stereotactic radiation therapy. Pre- and postirradiation linear expansion rates were estimated using volumetric measurements on sequential magnetic resonance images (MRIs). In addition, postirradiation tumor volume change was classified as demonstrating shrinkage (ratio of volume on last follow-up MRI to MRI immediately preceding irradiation <80%), stability (ratio 80%-120%), or expansion (ratio >120%). The median pre- and postirradiation follow-up was 20.0 and 27.5 months, respectively. Seven tumors from neurofibromatosis type 2 (NF2) patients were excluded from statistical analyses. Results: In the 58 non-NF2 patients, there was a trend of correlation between pre- and postirradiation volume change rates (slope on linear regression, 0.29; P=.06). Tumors demonstrating postirradiation expansion had a median preirradiation growth rate of 89%/year, and those without postirradiation expansion had a median preirradiation growth rate of 41%/year (P=.02). As the preirradiation growth rate increased, the probability of postirradiation expansion also increased. Overall, 24.1% of tumors were stable, 53.4% experienced shrinkage, and 22.5% experienced expansion. Predictors of no postirradiation tumor expansion included no prior surgery (P=.01) and slower tumor growth rate (P=.02). The control of tumors in NF2 patients was only 43%. Conclusions: Radiation therapy is an effective treatment for VS, but tumors that grow quickly preirradiation may be

Pretreatment Growth Rate Predicts Radiation Response in Vestibular Schwannomas

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Niu, Nina N. [Department of Radiation Oncology, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts (United States); Harvard Medical School, Department of Medicine, Brigham and Women' s Hospital, Boston, Massachusetts (United States); Niemierko, Andrzej [Department of Radiation Oncology, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts (United States); Larvie, Mykol [Harvard Medical School, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts (United States); Curtin, Hugh [Harvard Medical School, Department of Radiology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts (United States); Loeffler, Jay S. [Department of Radiation Oncology, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts (United States); McKenna, Michael J. [Harvard Medical School, Department of Otolaryngology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts (United States); Shih, Helen A., E-mail: hshih@partners.org [Department of Radiation Oncology, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts (United States)

2014-05-01

Purpose: Vestibular schwannomas (VS) are often followed without initial therapeutic intervention because many tumors do not grow and radiation therapy is associated with potential adverse effects. In an effort to determine whether maximizing initial surveillance predicts for later treatment response, the predictive value of preirradiation growth rate of VS on response to radiation therapy was assessed. Methods and Materials: Sixty-four patients with 65 VS were treated with single-fraction stereotactic radiation surgery or fractionated stereotactic radiation therapy. Pre- and postirradiation linear expansion rates were estimated using volumetric measurements on sequential magnetic resonance images (MRIs). In addition, postirradiation tumor volume change was classified as demonstrating shrinkage (ratio of volume on last follow-up MRI to MRI immediately preceding irradiation <80%), stability (ratio 80%-120%), or expansion (ratio >120%). The median pre- and postirradiation follow-up was 20.0 and 27.5 months, respectively. Seven tumors from neurofibromatosis type 2 (NF2) patients were excluded from statistical analyses. Results: In the 58 non-NF2 patients, there was a trend of correlation between pre- and postirradiation volume change rates (slope on linear regression, 0.29; P=.06). Tumors demonstrating postirradiation expansion had a median preirradiation growth rate of 89%/year, and those without postirradiation expansion had a median preirradiation growth rate of 41%/year (P=.02). As the preirradiation growth rate increased, the probability of postirradiation expansion also increased. Overall, 24.1% of tumors were stable, 53.4% experienced shrinkage, and 22.5% experienced expansion. Predictors of no postirradiation tumor expansion included no prior surgery (P=.01) and slower tumor growth rate (P=.02). The control of tumors in NF2 patients was only 43%. Conclusions: Radiation therapy is an effective treatment for VS, but tumors that grow quickly preirradiation may be

An Evidence-Based Approach to the Use of Predictive Biomarkers in the Treatment of Non- Small Cell Lung Cancer (NSCLC)

International Nuclear Information System (INIS)

Quinton, Cindy; Ellis, Peter M.

2011-01-01

Recent advances in the treatment of non-small cell lung cancer (NSCLC) have led to improvements in patient survival and quality of life. It is unclear whether molecular abnormalities associated with NSCLC cell survival, growth and proliferation are useful in predicting treatment benefit. We conducted a systematic review to establish which biomarkers contribute meaningfully to the management of NSCLC. A team of researchers searched PubMed and conference proceedings (ASCO, ESMO, IASLC, USCAP) using MESH terms for NSCLC and randomized trials (RCT), plus keywords for variables of interest. Evidence from multiple RCTs confirmed that histologic subtype is prognostic for survival and predictive of treatment efficacy and/or toxicity in NSCLC. Likewise, activating mutations of the epidermal growth factor receptor (EGFR) are associated with benefit from EGFR tyrosine kinase inhibitors in patients with advanced non-squamous NSCLC and should be assessed routinely. No biomarkers to date reliably predict response to anti-Vascular Endothelial Growth Factor (VEGF) therapies. There are inconsistent data on the role of ERCC1, BRCA, Beta tubulin III, RRM1, K-RAS, or TP-53 in treatment decisions. These tests should not be routinely used in selecting treatment at this time, whereas EML4/ALK translocations predict responses to specific targeted agents, the optimal assessment of this molecular abnormality has yet to be established. Personalized care of patients with NSCLC based on biomarkers is increasingly important to both clinical practice and research

Net root growth and nutrient acquisition in response to predicted climate change in two contrasting heathland species

DEFF Research Database (Denmark)

Arndal, M.F.; Merrild, M.P.; Michelsen, A.

2013-01-01

Accurate predictions of nutrient acquisition by plant roots and mycorrhizas are critical in modelling plant responses to climate change.We conducted a field experiment with the aim to investigate root nutrient uptake in a future climate and studied root production by ingrowth cores, mycorrhizal...... to elevated CO2. The species-specific response to the treatments suggests different sensitivity to global change factors, which could result in changed plant competitive interactions and belowground nutrient pool sizes in response to future climate change....

Treatment response in HCV related chronic hepatitis

International Nuclear Information System (INIS)

Hussain, A.B.; Hussain, T.; Hussain, S.; Masood, A.; Kazmi, Y.; Tariq, W.Z.; Karamat, K.A.

2004-01-01

Objective: To evaluate the virological response to treatment with interferon and ribavirin in-patients with hepatitis C related liver disease. Material and Methods: Two hundred seventy-nine patients were included in the study. These patients had taken interferon and ribavirin treatment for HCV related chronic hepatitis, and were referred to AFIP for HCV RNA testing by polymerase chain reaction (PCR) between January 2002 and September 2002. Out of 279 cases, 229 had taken the treatment for 06 or 12 months and were tested for end-of-treatment response (ETR). Fifty patients had completed there treatment regimens of 6 or 12 months treatment, at least 24 weeks before their PCR test and were having follow-up testing for sustained viral response (SVR). The sera of these patients were tested for HCV RNA by PCR, using a commercial kit of Amplicor (Roche) for qualitative detection of HCV RNA. Results: Out of 229 cases tested for end-of-treatment response, 198 (86.5%) had no detectable HCV RNA (responders) and 31 (13.50%) were PCR positive (non-responders). Thirty-eight out of 50 cases, tested for a sustained viral response, had a negative result for HCV PCR thus showing sustained response rate of 76%. Conclusion: The viral remission/response to interferon and ribavirin combination therapy in our patients was better than that quoted in other regions. (author)

Does a family history of RA influence the clinical presentation and treatment response in RA?

Science.gov (United States)

Frisell, Thomas; Saevarsdottir, Saedis; Askling, Johan

2016-06-01

To assess whether family history of rheumatoid arthritis (RA), among the strongest risk factors for developing RA, also carries information on the clinical presentation and treatment response. The prospective Swedish Rheumatology register was linked to family history of RA, defined as diagnosed RA in any first-degree relative, ascertained through the Swedish Multi-Generation and Patient registers. Clinical presentation was examined among patients with early RA 2000-2011 (symptom onset clinical characteristics, drug survival, European League Against Rheumatism (EULAR) response and change in disease activity at 3 and 6 months was estimated using linear and generalised logistic regression models. Correlation in relatives' response measures was also assessed. Patients with early RA with family history of RA were more often rheumatoid factor positive, but with no other clinically meaningful differences in their clinical presentation. Family history of RA did not predict response to MTX or TNFi, with the possible exception of no versus good EULAR response to TNFi at 6 months (OR=1.4, 95% CI 1.1 to 1.7). Having a relative who discontinued TNFi within a year increased the odds of doing the same (OR=3.7, 95% CI 1.8 to 7.5), although we found no significant familial correlations in change in disease activity measures. Family history of RA did not modify the clinical presentation of RA or predict response to standard treatment with MTX or TNFi. Treatment response, particularly drug survival, may itself be familial. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression.

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Carrillo, Facundo; Sigman, Mariano; Fernández Slezak, Diego; Ashton, Philip; Fitzgerald, Lily; Stroud, Jack; Nutt, David J; Carhart-Harris, Robin L

2018-04-01

Natural speech analytics has seen some improvements over recent years, and this has opened a window for objective and quantitative diagnosis in psychiatry. Here, we used a machine learning algorithm applied to natural speech to ask whether language properties measured before psilocybin for treatment-resistant can predict for which patients it will be effective and for which it will not. A baseline autobiographical memory interview was conducted and transcribed. Patients with treatment-resistant depression received 2 doses of psilocybin, 10 mg and 25 mg, 7 days apart. Psychological support was provided before, during and after all dosing sessions. Quantitative speech measures were applied to the interview data from 17 patients and 18 untreated age-matched healthy control subjects. A machine learning algorithm was used to classify between controls and patients and predict treatment response. Speech analytics and machine learning successfully differentiated depressed patients from healthy controls and identified treatment responders from non-responders with a significant level of 85% of accuracy (75% precision). Automatic natural language analysis was used to predict effective response to treatment with psilocybin, suggesting that these tools offer a highly cost-effective facility for screening individuals for treatment suitability and sensitivity. The sample size was small and replication is required to strengthen inferences on these results. Copyright © 2018 Elsevier B.V. All rights reserved.

WE-AB-202-11: Radiobiological Modeling of Tumor Response During Radiotherapy Based On Pre-Treatment Dynamic PET Imaging Data

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Crispin-Ortuzar, M; Grkovski, M; Beattie, B; Lee, N; Riaz, N; Humm, J; Jeong, J; Fontanella, A; Deasy, J [Memorial Sloan Kettering Cancer Center, New York, NY (United States)

2016-06-15

Purpose: To evaluate the ability of a multiscale radiobiological model of tumor response to predict mid-treatment hypoxia images, based on pretreatment imaging of perfusion and hypoxia with [18-F]FMISO dynamic PET and glucose metabolism with [18-F]FDG PET. Methods: A mechanistic tumor control probability (TCP) radiobiological model describing the interplay between tumor cell proliferation and hypoxia (Jeong et al., PMB 2013) was extended to account for intra-tumor nutrient heterogeneity, dynamic cell migration due to nutrient gradients, and stromal cells. This extended model was tested on 10 head and neck cancer patients treated with chemoradiotherapy, randomly drawn from a larger MSKCC protocol involving baseline and mid-therapy dynamic PET scans. For each voxel, initial fractions of proliferative and hypoxic tumor cells were obtained by finding an approximate solution to a system of linear equations relating cell fractions to voxel-level FDG uptake, perfusion (FMISO K{sub 1}) and hypoxia (FMISO k{sub 3}). The TCP model then predicted their evolution over time up until the mid treatment scan. Finally, the linear model was reapplied to predict each lesion’s median hypoxia level (k{sub 3}[med,sim]) which in turn was compared to the FMISO k{sub 3}[med] measured at mid-therapy. Results: The average k3[med] of the tumors in pre-treatment scans was 0.0035 min{sup −1}, with an inter-tumor standard deviation of σ[pre]=0.0034 min{sup −1}. The initial simulated k{sub 3}[med,sim] of each tumor agreed with the corresponding measurements within 0.1σ[pre]. In 7 out of 10 lesions, the mid-treatment k{sub 3}[med,sim] prediction agreed with the data within 0.3σ[pre]. The remaining cases corresponded to the most extreme relative changes in k{sub 3}[med]. Conclusion: This work presents a method to personalize the prediction of a TCP model using pre-treatment kinetic imaging data, and validates the modeling of radiotherapy response by predicting changes in median hypoxia

IgG Responses to Tissue-Associated Antigens as Biomarkers of Immunological Treatment Efficacy

Directory of Open Access Journals (Sweden)

Heath A. Smith

2011-01-01

Full Text Available We previously demonstrated that IgG responses to a panel of 126 prostate tissue-associated antigens are common in patients with prostate cancer. In the current report we questioned whether changes in IgG responses to this panel might be used as a measure of immune response, and potentially antigen spread, following prostate cancer-directed immune-active therapies. Sera were obtained from prostate cancer patients prior to and three months following treatment with androgen deprivation therapy (=34, a poxviral vaccine (=31, and a DNA vaccine (=21. Changes in IgG responses to individual antigens were identified by phage immunoblot. Patterns of IgG recognition following three months of treatment were evaluated using a machine-learned Bayesian Belief Network (ML-BBN. We found that different antigens were recognized following androgen deprivation compared with vaccine therapies. While the number of clinical responders was low in the vaccine-treated populations, we demonstrate that ML-BBN can be used to develop potentially predictive models.

Heterogeneity of HVR-1 quasispecies is predictive of early but not sustained virological response in genotype 1b-infected patients undergoing combined treatment with PEG- or STD-IFN plus RBV.

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Abbate, I; Cappiello, G; Lo Iacono, O; Longo, R; Ferraro, D; Antonucci, G; Di Marco, V; Di Stefano, R; Craxì, A; Solmone, M C; Spanò, A; Ippolito, G; Capobianchi, M R

2003-01-01

ISDR mutation pattern and HVR-1 quasispecies were analyzed in HCV genotype 1b-infected patients treated with either PEG- or STD-IFN plus ribavirin, in order to find virological correlates of therapy outcome. ISDR region analysis, performed at baseline (T0) and at 4 weeks of therapy (T1), indicated that ISDR mutation pattern was not predictive of response to treatment. Moreover, no selection of putative resistant strains in the first month of therapy was observed. Viral load was not correlated with any parameter of HVR-1 heterogeneity. Among the HVR-1 heterogeneity parameters considered, complexity was inversely correlated to viral load decline at T1. In univariate analysis, complexity, proportion of non synonymous substitutions (NS) and NS/S ratio were lower in patients showing virological response at 6 months of treatment. Complexity was the only parameter independently associated with both decline of viral load at T1 and virological response after 6 months, even after adjustment for confounding variables. At the end of treatment or later, these correlations were lost. Evolution pattern of the HVR-1 quasispecies indicated a strong selective pressure in sustained responders, with complete substitution of pre-existing quasispecies, while minor changes occured in non responders. In relapsers both patterns were present at a similar rate. In conclusion, this study shows that HVR-1 heterogeneity may be involved in the early response to combined IFN-RBV therapy. The loss of correlation between viral heterogeneity and therapy outcome at 6 months of therapy, or later, suggests that other factors may play a role in maintaining sustained response to treatment.

Prediction of therapy response to interferon-alpha in chronic viral hepatitis-B by liver and hepatobiliary scintigraphy

International Nuclear Information System (INIS)

Caglar, M.; Sari, O.; Akcan, Y.

2002-01-01

Interferon (IFN) provides effective treatment in some patients with chronic hepatitis. The clarification of factors predictive of therapy response would be helpful in identifying patients who would benefit from treatment. In this study, we evaluated the potential utility of Tc-99m sulfur colloid liver/spleen and Tc-99m-disofenin hepatobiliary scintigraphy to predict therapy response to IFN in patients with chronic active hepatitis. The study group consisted of ten patients with chronic viral hepatitis B who were treated with 4.5 units of interferon alpha for 12 months. Prior to the start of the therapy, sulfur colloid scintigraphy was obtained by which the liver/spleen ratios were derived. Hepatobiliary scintigraphy was performed on a separate day and time-activity curves were generated from regions of interest drawn over the liver, heart and gall-bladder. The index of blood and liver clearance time was calculated. Histological grading and laboratory values were obtained for clinical correlation. Responders (n=6) to IFN were defined as those who improved clinically with normalized transaminase levels and had hepatitis B envelope antigen (HBeAg) seroconversion. On sulfur colloid (SC) scintigraphy, the liver/spleen ratio of non-responders was significantly lower than responders (median values: 0.69 vs. 1.16, p=0.01) but on hepatobiliary scintigraphy no statistically significant parameters were found to predict response to interferon therapy. (author)

Role of MRI in osteosarcoma for evaluation and prediction of chemotherapy response: correlation with histological necrosis

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Bajpai, Jyoti; Bakhshi, Sameer [Dr. B. R. A. Institute Rotary Cancer Hospital, Department of Medical Oncology, All India Institute of Medical Sciences, New Delhi (India); Gamnagatti, Shivanand [All India Institute of Medical Sciences, Department of Radiodiagnosis, New Delhi (India); Kumar, Rakesh; Malhotra, Arun [All India Institute of Medical Sciences, Department of Nuclear Medicine, New Delhi (India); Sreenivas, Vishnubhatla [All India Institute of Medical Sciences, Department of Biostatistics, New Delhi (India); Sharma, Mehar Chand; Safaya, Rajni [All India Institute of Medical Sciences, Department of Pathology, New Delhi (India); Khan, Shah Alam; Rastogi, Shishir [All India Institute of Medical Sciences, Department of Orthopedics, New Delhi (India)

2011-04-15

Histological necrosis, the current standard for response evaluation in osteosarcoma, is attainable after neoadjuvant chemotherapy. To establish the role of surrogate markers of response prediction and evaluation using MRI in the early phases of the disease. Thirty-one treatment-naive osteosarcoma patients received three cycles of neoadjuvant chemotherapy followed by surgery during 2006-2008. All patients underwent baseline and post-chemotherapy conventional, diffusion-weighted and dynamic contrast-enhanced MRI. Taking histological response (good response {>=}90% necrosis) as the reference standard, various parameters of MRI were compared to it. A tumor was considered ellipsoidal; volume, average tumor plane and its relative value (average tumor plane relative/body surface area) was calculated using the standard formula for ellipse. Receiver operating characteristic curves were generated to assess best threshold and predictability. After deriving thresholds for each parameter in univariable analysis, multivariable analysis was carried out. Both pre-and post-chemotherapy absolute and relative-size parameters correlated well with necrosis. Apparent diffusion coefficient did not correlate with necrosis; however, on adjusting for volume, significant correlation was found. Thus, we could derive a new parameter: diffusion per unit volume. In osteosarcoma, chemotherapy response can be predicted and evaluated by conventional and diffusion-weighted MRI early in the disease course and it correlates well with necrosis. Further, newly derived parameter diffusion per unit volume appears to be a sensitive substitute for response evaluation in osteosarcoma. (orig.)

Role of MRI in osteosarcoma for evaluation and prediction of chemotherapy response: correlation with histological necrosis

International Nuclear Information System (INIS)

Bajpai, Jyoti; Bakhshi, Sameer; Gamnagatti, Shivanand; Kumar, Rakesh; Malhotra, Arun; Sreenivas, Vishnubhatla; Sharma, Mehar Chand; Safaya, Rajni; Khan, Shah Alam; Rastogi, Shishir

2011-01-01

Histological necrosis, the current standard for response evaluation in osteosarcoma, is attainable after neoadjuvant chemotherapy. To establish the role of surrogate markers of response prediction and evaluation using MRI in the early phases of the disease. Thirty-one treatment-naive osteosarcoma patients received three cycles of neoadjuvant chemotherapy followed by surgery during 2006-2008. All patients underwent baseline and post-chemotherapy conventional, diffusion-weighted and dynamic contrast-enhanced MRI. Taking histological response (good response ≥90% necrosis) as the reference standard, various parameters of MRI were compared to it. A tumor was considered ellipsoidal; volume, average tumor plane and its relative value (average tumor plane relative/body surface area) was calculated using the standard formula for ellipse. Receiver operating characteristic curves were generated to assess best threshold and predictability. After deriving thresholds for each parameter in univariable analysis, multivariable analysis was carried out. Both pre-and post-chemotherapy absolute and relative-size parameters correlated well with necrosis. Apparent diffusion coefficient did not correlate with necrosis; however, on adjusting for volume, significant correlation was found. Thus, we could derive a new parameter: diffusion per unit volume. In osteosarcoma, chemotherapy response can be predicted and evaluated by conventional and diffusion-weighted MRI early in the disease course and it correlates well with necrosis. Further, newly derived parameter diffusion per unit volume appears to be a sensitive substitute for response evaluation in osteosarcoma. (orig.)

Immunological tumor status may predict response to neoadjuvant chemotherapy and outcome after radical cystectomy in bladder cancer.

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Tervahartiala, Minna; Taimen, Pekka; Mirtti, Tuomas; Koskinen, Ilmari; Ecke, Thorsten; Jalkanen, Sirpa; Boström, Peter J

2017-10-04

Bladder cancer (BC) is the ninth most common cancer worldwide. Radical cystectomy (RC) with neoadjuvant chemotherapy (NAC) is recommended for muscle-invasive BC. The challenge of the neoadjuvant approach relates to challenges in selection of patients to chemotherapy that are likely to respond to the treatment. To date, there are no validated molecular markers or baseline clinical characteristics to identify these patients. Different inflammatory markers, including tumor associated macrophages with their plastic pro-tumorigenic and anti-tumorigenic functions, have extensively been under interests as potential prognostic and predictive biomarkers in different cancer types. In this immunohistochemical study we evaluated the predictive roles of three immunological markers, CD68, MAC387, and CLEVER-1, in response to NAC and outcome of BC. 41% of the patients had a complete response (pT0N0) to NAC. Basic clinicopathological variables did not predict response to NAC. In contrast, MAC387 + cells and CLEVER-1 + macrophages associated with poor NAC response, while CLEVER-1 + vessels associated with more favourable response to NAC. Higher counts of CLEVER-1 + macrophages associated with poorer overall survival and CD68 + macrophages seem to have an independent prognostic value in BC patients treated with NAC. Our findings point out that CD68, MAC387, and CLEVER-1 may be useful prognostic and predictive markers in BC.

Sputum eosinophilia can predict responsiveness to inhaled corticosteroid treatment in patients with overlap syndrome of COPD and asthma

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Kubo K

2012-04-01

Full Text Available Yoshiaki Kitaguchi1,*, Yoshimichi Komatsu1,*, Keisaku Fujimoto2, Masayuki Hanaoka1, Keishi Kubo1 1First Department of Internal Medicine, Shinshu University School of Medicine, 2Department of Biomedical Laboratory Sciences, Shinshu University School of Health Sciences, Matsumoto, Japan *These authors contributed equally to this workBackground: Chronic obstructive pulmonary disease (COPD and asthma may overlap and converge in older people (overlap syndrome. It was hypothesized that patients with overlap syndrome may have different clinical characteristics such as sputum eosinophilia, and better responsiveness to treatment with inhaled corticosteroid (ICS.Methods: Sixty-three patients with stable COPD (forced expiratory volume in 1 second [FEV1] ≤80% underwent pulmonary function tests, including reversibility of airflow limitation, arterial blood gas analysis, analysis of inflammatory cells in induced sputum, and chest high-resolution computed tomography. The inclusion criteria for COPD patients with asthmatic symptoms included having asthmatic symptoms such as episodic breathlessness, wheezing, cough, and chest tightness worsening at night or in the early morning (COPD with asthma group. The clinical features of COPD patients with asthmatic symptoms were compared with those of COPD patients without asthmatic symptoms (COPD without asthma group.Results: The increases in FEV1 in response to treatment with ICS were significantly higher in the COPD with asthma group. The peripheral eosinophil counts and sputum eosinophil counts were significantly higher. The prevalence of patients with bronchial wall thickening on chest high-resolution computed tomography was significantly higher. A significant correlation was observed between the increases in FEV1 in response to treatment with ICS and sputum eosinophil counts, and between the increases in FEV1 in response to treatment with ICS and the grade of bronchial wall thickening. Receiver operating

Predictive effects of bilirubin on response of colorectal cancer to irinotecan-based chemotherapy.

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Yu, Qian-Qian; Qiu, Hong; Zhang, Ming-Sheng; Hu, Guang-Yuan; Liu, Bo; Huang, Liu; Liao, Xin; Li, Qian-Xia; Li, Zhi-Huan; Yuan, Xiang-Lin

2016-04-28

To examine the predictive effects of baseline serum bilirubin levels and UDP-glucuronosyltransferase (UGT) 1A1*28 polymorphism on response of colorectal cancer to irinotecan-based chemotherapy. The present study was based on a prospective multicenter longitudinal trial of Chinese metastatic colorectal cancer (mCRC) patients treated with irinotecan-based chemotherapy (NCT01282658). Baseline serum bilirubin levels, including total bilirubin (TBil) and unconjugated bilirubin (UBil), were measured, and genotyping of UGT1A1*28 polymorphism was performed. Receiver operating characteristic curve (ROC) analysis was used to determine cutoff values of TBil and UBil. The TBil values were categorized into > 13.0 or ≤ 13.0 groups; the UBil values were categorized into > 4.1 or ≤ 4.1 groups. Combining the cutoff values of TBil and UBil, which was recorded as CoBil, patients were classified into three groups. The classifier's performance of UGT1A1*28 and CoBil for predicting treatment response was evaluated by ROC analysis. Associations between response and CoBil or UGT1A1*28 polymorphism were estimated using simple and multiple logistic regression models. Among the 120 mCRC patients, the serum bilirubin level was significantly different between the UGT1A1*28 wild-type and mutant genotypes. Patients with the mutant genotype had an increased likelihood of a higher TBil (P = 0.018) and a higher UBil (P = 0.014) level compared with the wild-type genotype. Patients were stratified into three groups based on CoBil. Group 1 was patients with TBil > 13.0 and UBil > 4.1; Group 2 was patients with TBil ≤ 13.0 and UBil > 4.1; and Group 3 was patients with TBil ≤ 13.0 and UBil ≤ 4.1. Patients in Group 3 had more than a 10-fold higher likelihood of having a response in the simple (OR = 11.250; 95%CI: 2.286-55.367; P = 0.003) and multiple (OR = 16.001; 95%CI: 2.802 -91.371; P = 0.002) analyses compared with the Group 1 individuals. Patients carrying the UGT1A1*28 (TA)7 allele were 4

Serum peptide expression and treatment responses in patients with advanced non-small-cell lung cancer

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An, Juan; Tang, Chuan-Hao; Wang, Na; Liu, Yi; Lv, Jin; Xu, Bin; Li, Xiao-Yan; Guo, Wan-Feng; Gao, Hong-Jun; He, Kun; Liu, Xiao-Qing

2018-01-01

Epidermal growth factor receptor (EGFR) mutation is an important predictor for response to personalized treatments of patients with advanced non-small-cell lung cancer (NSCLC). However its usage is limited due to the difficult of obtaining tissue specimens. A novel prediction system using matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been reported to be a perspective tool in European countries to identify patients who are likely to benefit from EGFR tyrosine kinase inhibitor (TKI) treatment. In the present study, MALDI-TOF MS was used on pretreatment serum samples of patients with advanced non-small-cell lung cancer to discriminate the spectra between disease control and disease progression groups in one cohort of Chinese patients. The candidate features for classification were subsequently validated in a blinded fashion in another set of patients. The correlation between plasma EGFR mutation status and the intensities of representative spectra for classification was evaluated. A total of 103 patients that were treated with EGFR-TKIs were included. It was determined that 8 polypeptides peaks were significant different between the disease control and disease progression group. A total of 6 polypeptides were established in the classification algorithm. The sensitivity of the algorithm to predict treatment responses was 76.2% (16/21) and the specificity was 81.8% (18/22). The accuracy rate of the algorithm was 79.1% (34/43). A total of 3 polypeptides were significantly correlated with EGFR mutations (P=0.04, P=0.03 and P=0.04, respectively). The present study confirmed that MALDI-TOF MS analysis can be used to predict responses to EGFR-TKI treatment of the Asian population where the EGFR mutation status differs from the European population. Furthermore, the expression intensities of the three polypeptides in the classification model were associated with EGFR mutation. PMID:29844828

Motivation and treatment credibility predict alliance in cognitive behavioral treatment for youth with anxiety disorders in community clinics.

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Fjermestad, K W; Lerner, M D; McLeod, B D; Wergeland, G J H; Haugland, B S M; Havik, O E; Öst, L-G; Silverman, W K

2017-11-16

We examined whether motivation and treatment credibility predicted alliance in a 10-session cognitive behavioral treatment delivered in community clinics for youth anxiety disorders. Ninety-one clinic-referred youths (mean age  = 11.4 years, standard deviation = 2.1, range 8-15 years, 49.5% boys) with anxiety disorders-rated treatment motivation at pretreatment and perceived treatment credibility after session 1. Youths and therapists (YT) rated alliance after session 3 (early) and session 7 (late). Hierarchical linear models were applied to examine whether motivation and treatment credibility predicted YT early alliance, YT alliance change, and YT alliance agreement. Motivation predicted high early YT alliance, but not YT alliance change or alliance agreement. Youth-rated treatment credibility predicted high early youth alliance and high YT positive alliance change, but not early therapist alliance or alliance agreement. Conclusion Efforts to enhance youth motivation and treatment credibility early in treatment could facilitate the formation of a strong YT alliance. © 2017 Wiley Periodicals, Inc.

An observation on the variance of a predicted response in ...

African Journals Online (AJOL)

... these properties and computational simplicity. To avoid over fitting, along with the obvious advantage of having a simpler equation, it is shown that the addition of a variable to a regression equation does not reduce the variance of a predicted response. Key words: Linear regression; Partitioned matrix; Predicted response ...

Refining Prediction in Treatment-Resistant Depression: Results of Machine Learning Analyses in the TRD III Sample.

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Kautzky, Alexander; Dold, Markus; Bartova, Lucie; Spies, Marie; Vanicek, Thomas; Souery, Daniel; Montgomery, Stuart; Mendlewicz, Julien; Zohar, Joseph; Fabbri, Chiara; Serretti, Alessandro; Lanzenberger, Rupert; Kasper, Siegfried

The study objective was to generate a prediction model for treatment-resistant depression (TRD) using machine learning featuring a large set of 47 clinical and sociodemographic predictors of treatment outcome. 552 Patients diagnosed with major depressive disorder (MDD) according to DSM-IV criteria were enrolled between 2011 and 2016. TRD was defined as failure to reach response to antidepressant treatment, characterized by a Montgomery-Asberg Depression Rating Scale (MADRS) score below 22 after at least 2 antidepressant trials of adequate length and dosage were administered. RandomForest (RF) was used for predicting treatment outcome phenotypes in a 10-fold cross-validation. The full model with 47 predictors yielded an accuracy of 75.0%. When the number of predictors was reduced to 15, accuracies between 67.6% and 71.0% were attained for different test sets. The most informative predictors of treatment outcome were baseline MADRS score for the current episode; impairment of family, social, and work life; the timespan between first and last depressive episode; severity; suicidal risk; age; body mass index; and the number of lifetime depressive episodes as well as lifetime duration of hospitalization. With the application of the machine learning algorithm RF, an efficient prediction model with an accuracy of 75.0% for forecasting treatment outcome could be generated, thus surpassing the predictive capabilities of clinical evaluation. We also supply a simplified algorithm of 15 easily collected clinical and sociodemographic predictors that can be obtained within approximately 10 minutes, which reached an accuracy of 70.6%. Thus, we are confident that our model will be validated within other samples to advance an accurate prediction model fit for clinical usage in TRD. © Copyright 2017 Physicians Postgraduate Press, Inc.

Emotional Responses to Suicidal Patients: Factor Structure, Construct, and Predictive Validity of the Therapist Response Questionnaire-Suicide Form

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Shira Barzilay

2018-04-01

3-factor structure. It demonstrates construct validity for assessing distinct suicide-related countertransference to psychiatric outpatients. Mental health professionals’ emotional responses to their patients are concurrently indicative and prospectively predictive of suicidal thoughts and behaviors. Thus, the TRQ-SF is a useful tool for the study of countertransference in the treatment of suicidal patients and may help clinicians make diagnostic and therapeutic use of their own responses to improve assessment and intervention for individual suicidal patients.

Emotional Responses to Suicidal Patients: Factor Structure, Construct, and Predictive Validity of the Therapist Response Questionnaire-Suicide Form.

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Barzilay, Shira; Yaseen, Zimri S; Hawes, Mariah; Gorman, Bernard; Altman, Rachel; Foster, Adriana; Apter, Alan; Rosenfield, Paul; Galynker, Igor

2018-01-01

construct validity for assessing distinct suicide-related countertransference to psychiatric outpatients. Mental health professionals' emotional responses to their patients are concurrently indicative and prospectively predictive of suicidal thoughts and behaviors. Thus, the TRQ-SF is a useful tool for the study of countertransference in the treatment of suicidal patients and may help clinicians make diagnostic and therapeutic use of their own responses to improve assessment and intervention for individual suicidal patients.

Influential Factors for Accurate Load Prediction in a Demand Response Context

DEFF Research Database (Denmark)

Wollsen, Morten Gill; Kjærgaard, Mikkel Baun; Jørgensen, Bo Nørregaard

2016-01-01

Accurate prediction of a buildings electricity load is crucial to respond to Demand Response events with an assessable load change. However, previous work on load prediction lacks to consider a wider set of possible data sources. In this paper we study different data scenarios to map the influence....... Next, the time of day that is being predicted greatly influence the prediction which is related to the weather pattern. By presenting these results we hope to improve the modeling of building loads and algorithms for Demand Response planning.......Accurate prediction of a buildings electricity load is crucial to respond to Demand Response events with an assessable load change. However, previous work on load prediction lacks to consider a wider set of possible data sources. In this paper we study different data scenarios to map the influence...

Can cell kinetic parameters predict the response of tumours to radiotherapy?

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McNally, N J

1989-11-01

Three potential predictive assays of the repopulation component in tumour response to therapy are considered. (1) The DNA index can easily be measured. It is of prognostic value for cancers of certain sites, aneuploidy being a bad prognostic indicator. It is not strictly an indicator of cell proliferation. (2) The in vitro labelling index is of predictive value in early stage operable breast cancer and in head and neck cancer. In the former a high pretreatment labelling index can identify patients who could benefit from adjuvant chemotherapy. (3) The tumour potential doubling time (Tpot) can be measured rapidly following in vivo labelling with bromodeoxyuridine or iododeoxyuridine. We have measured Tpot in over 100 solid tumours with a success rate of about 75 per cent. Nearly 50 per cent of the tumours have a pre-treatment potential doubling time of 5 days or less. These would be suitable candidates for accelerated fractionation.

Can cell kinetic parameters predict the response of tumours to radiotherapy?

International Nuclear Information System (INIS)

McNally, N.J.

1989-01-01

Three potential predictive assays of the repopulation component in tumour response to therapy are considered. (1) The DNA index can easily be measured. It is of prognostic value for cancers of certain sites, aneuploidy being a bad prognostic indicator. It is not strictly an indicator of cell proliferation. (2) The in vitro labelling index is of predictive value in early stage operable breast cancer and in head and neck cancer. In the former a high pretreatment labelling index can identify patients who could benefit from adjuvant chemotherapy. (3) The tumour potential doubling time can be measured rapidly following in vivo labelling with bromodeoxyuridine or iododeoxyuridine. The authors measured T pot in over 100 solid tumours with a success rate of about 75%. Nearly 50% of the tumours have a pre-treatment potential doubling time of 5 days or less. These would be suitable candidates for accelerated fractionation. (author)

A simple risk scoring system for prediction of relapse after inpatient alcohol treatment.

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Pedersen, Mads Uffe; Hesse, Morten

2009-01-01

Predicting relapse after alcoholism treatment can be useful in targeting patients for aftercare services. However, a valid and practical instrument for predicting relapse risk does not exist. Based on a prospective study of alcoholism treatment, we developed the Risk of Alcoholic Relapse Scale (RARS) using items taken from the Addiction Severity Index and some basic demographic information. The RARS was cross-validated using two non-overlapping samples, and tested for its ability to predict relapse across different models of treatment. The RARS predicted relapse to drinking within 6 months after alcoholism treatment in both the original and the validation sample, and in a second validation sample it predicted admission to new treatment 3 years after treatment. The RARS can identify patients at high risk of relapse who need extra aftercare and support after treatment.

Response predictions using the observed autocorrelation function

DEFF Research Database (Denmark)

Nielsen, Ulrik Dam; H. Brodtkorb, Astrid; Jensen, Jørgen Juncher

2018-01-01

This article studies a procedure that facilitates short-time, deterministic predictions of the wave-induced motion of a marine vessel, where it is understood that the future motion of the vessel is calculated ahead of time. Such predictions are valuable to assist in the execution of many marine......-induced response in study. Thus, predicted (future) values ahead of time for a given time history recording are computed through a mathematical combination of the sample autocorrelation function and previous measurements recorded just prior to the moment of action. Importantly, the procedure does not need input...... show that predictions can be successfully made in a time horizon corresponding to about 8-9 wave periods ahead of current time (the moment of action)....

Breast Cancer Spatial Heterogeneity in Near-Infrared Spectra and the Prediction of Neoadjuvant Chemotherapy Response

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Santoro, Ylenia

Breast cancer accounts for more than 20% of all female cancers. Many of these patients receive neoadjuvant chemotherapy (NAC) to reduce the size of the tumor before surgery and to anticipate the efficacy of treatments for after the procedure. Breast cancer is a heterogeneous disease that comes in several clinical and histological forms. The prediction of the efficacy of chemotherapy would potentially select good candidates who would respond while excluding poor candidates who would not benefit from treatment. In this work we investigate the possibility of noninvasively predicting chemotherapy response prior to treatment based on optical biomarkers obtained from tumor spatial heterogeneities of spectral features measured using Diffuse Optical Spectroscopy. We describe an algorithm to calculate an index that characterizes spatial differences in broadband near-infrared absorption spectra of tumor-containing breast tissue. Patient-specific tumor spatial heterogeneities are visualized through a Heterogeneity Spectrum (HS). HS is a biomarker that can be attributed to different molecular distributions within the tumor. To classify lesion heterogeneities, we built a Heterogeneity Index (HI) from the HS by weighing specific absorption bands. It has been shown that NAC response is potentially related to tumor heterogeneity. Therefore, we correlate the HI obtained prior to treatment with the final response to NAC. In this thesis we also present a novel digital parallel frequency domain system for tissue imaging. The systems employs a supercontinuum laser with high brightness, and a photomultiplier with a large detection area, both allowing a deep penetration with extremely low power on the sample. The digital parallel acquisition is performed through the use of the Flimbox and it decreases the time required for standard serial systems that need to scan through all modulation frequencies. The all-digital acquisition removes analog noise, avoids the analog mixer and it does not

Dynamic contrast-enhanced MR imaging pharmacokinetic parameters as predictors of treatment response of brain metastases in patients with lung cancer

Energy Technology Data Exchange (ETDEWEB)

Kuchcinski, Gregory; Duhal, Romain; Lalisse, Maxime; Dumont, Julien; Lopes, Renaud; Pruvo, Jean-Pierre; Leclerc, Xavier; Delmaire, Christine [University of Lille, CHU Lille, Department of Neuroradiology, Lille (France); Le Rhun, Emilie [University of Lille, CHU Lille, Department of Neurosurgery, Lille (France); Oscar Lambret Center, Department of Medical Oncology, Lille (France); Inserm U1192-PRISM-Laboratoire de Proteomique, Reponse Inflammatoire, Spectrometrie de Masse, Lille (France); Cortot, Alexis B. [University of Lille, CHU Lille, Department of Thoracic Oncology, Lille (France); Drumez, Elodie [University of Lille, CHU Lille, Department of Biostatistics, Lille (France)

2017-09-15

To determine the diagnostic accuracy of pharmacokinetic parameters measured by dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in predicting the response of brain metastases to antineoplastic therapy in patients with lung cancer. Forty-four consecutive patients with lung cancer, harbouring 123 newly diagnosed brain metastases prospectively underwent conventional 3-T MRI at baseline (within 1 month before treatment), during the early (7-10 weeks) and midterm (5-7 months) post-treatment period. An additional DCE MRI sequence was performed during baseline and early post-treatment MRI to evaluate baseline pharmacokinetic parameters (K{sup trans}, k{sub ep}, v{sub e}, v{sub p}) and their early variation (∇K{sup trans}, ∇k{sub ep}, ∇v{sub e}, ∇v{sub p}). The objective response was judged by the volume variation of each metastasis from baseline to midterm MRI. ROC curve analysis determined the best DCE MRI parameter to predict the objective response. Baseline DCE MRI parameters were not associated with the objective response. Early ∇K{sup trans}, ∇v{sub e} and ∇v{sub p} were significantly associated with the objective response (p = 0.02, p = 0.001 and p = 0.02, respectively). The best predictor of objective response was ∇v{sub e} with an area under the curve of 0.93 [95% CI = 0.87, 0.99]. DCE MRI and early ∇v{sub e} may be a useful tool to predict the objective response of brain metastases in patients with lung cancer. (orig.)

Mood color choice helps to predict response to hypnotherapy in patients with irritable bowel syndrome

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Tarrier Nicholas

2010-12-01

Full Text Available Abstract Background Approximately two thirds of patients with irritable bowel syndrome (IBS respond well to hypnotherapy. However, it is time consuming as well as expensive to provide and therefore a way of predicting outcome would be extremely useful. The use of imagery and color form an integral part of the hypnotherapeutic process and we have hypothesised that investigating color and how it relates to mood might help to predict response to treatment. In order to undertake this study we have previously developed and validated a method of presenting colors to individuals for research purposes called the Manchester Color Wheel (MCW. Using this instrument we have been able to classify colors into positive, neutral and negative shades and this study aimed to assess their predictive role in hypnotherapy. Methods 156 consecutive IBS patients (aged 14-74, mean 42.0 years, 127 (81% females, 29 (19% males were studied. Before treatment, each patient was asked to relate their mood to a color on the MCW as well as completing the IBS Symptom Severity Score, the Hospital Anxiety and Depression (HAD Scale, the Non-colonic Symptom Scale, the Quality of Life Scale and the Tellegen Absorption Scale (TAS which is a measure of hypnotisability. Following hypnotherapy all these measures were repeated with the exception of the TAS. Results For patients with a positive mood color the odds of responding to hypnotherapy were nine times higher than that of those choosing either a neutral or negative color or no color at all (odds ratio: 8.889; p = 0.042. Furthermore, a high TAS score and the presence of HAD anxiety also had good predictive value (odds ratio: 4.024; p = 0.092, 3.917; p Conclusion A positive mood color, especially when combined with HAD anxiety and a high TAS score, predict a good response to hypnotherapy.

Elevation in inflammatory serum biomarkers predicts response to trastuzumab-containing therapy.

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Ahmed A Alkhateeb

Full Text Available Approximately half of all HER2/neu-overexpressing breast cancer patients do not respond to trastuzumab-containing therapy. Therefore, there remains an urgent and unmet clinical need for the development of predictive biomarkers for trastuzumab response. Recently, several lines of evidence have demonstrated that the inflammatory tumor microenvironment is a major contributor to therapy resistance in breast cancer. In order to explore the predictive value of inflammation in breast cancer patients, we measured the inflammatory biomarkers serum ferritin and C-reactive protein (CRP in 66 patients immediately before undergoing trastuzumab-containing therapy and evaluated their progression-free and overall survival. The elevation in pre-treatment serum ferritin (>250 ng/ml or CRP (>7.25 mg/l was a significant predictor of reduced progression-free survival and shorter overall survival. When patients were stratified based on their serum ferritin and CRP levels, patients with elevation in both inflammatory biomarkers had a markedly poorer response to trastuzumab-containing therapy. Therefore, the elevation in inflammatory serum biomarkers may reflect a pathological state that decreases the clinical efficacy of this therapy. Anti-inflammatory drugs and life-style changes to decrease inflammation in cancer patients should be explored as possible strategies to sensitize patients to anti-cancer therapeutics.

The potential biomarkers in predicting pathologic response of breast cancer to three different chemotherapy regimens: a case control study

Directory of Open Access Journals (Sweden)

Xu Chaoyang

2009-07-01

Full Text Available Abstract Background Preoperative chemotherapy (PCT has become the standard of care in locally advanced breast cancer. The identification of patient-specific tumor characteristics that can improve the ability to predict response to therapy would help optimize treatment, improve treatment outcomes, and avoid unnecessary exposure to potential toxicities. This study is to determine whether selected biomarkers could predict pathologic response (PR of breast tumors to three different PCT regimens, and to identify a subset of patients who would benefit from a given type of treatment. Methods 118 patients with primary breast tumor were identified and three PCT regimens including DEC (docetaxel+epirubicin+cyclophosphamide, VFC (vinorelbine/vincristine+5-fluorouracil+cyclophosphamide and EFC (epirubicin+5-fluorouracil+cyclophosphamide were investigated. Expression of steroid receptors, HER2, P-gp, MRP, GST-pi and Topo-II was evaluated by immunohistochemical scoring on tumor tissues obtained before and after PCT. The PR of breast carcinoma was graded according to Sataloff's classification. Chi square test, logistic regression and Cochran-Mantel-Haenszel assay were performed to determine the association between biomarkers and PR, as well as the effectiveness of each regimen on induction of PR. Results There was a clear-cut correlation between the expression of ER and decreased PR to PCT in all three different regimens (p p Conclusion ER is an independent predictive factor for PR to PCT regimens including DEC, VFC and EFC in primary breast tumors, while HER2 is only predictive for DEC regimen. Expression of PgR, Topo-II, P-gp, MRP and GST-pi are not predictive for PR to any PCT regimens investigated. Results obtained in this clinical study may be helpful for the selection of appropriate treatments for breast cancer patients.

Predicting and measuring fluid responsiveness with echocardiography

Directory of Open Access Journals (Sweden)

Ashley Miller

2016-06-01

Full Text Available Echocardiography is ideally suited to guide fluid resuscitation in critically ill patients. It can be used to assess fluid responsiveness by looking at the left ventricle, aortic outflow, inferior vena cava and right ventricle. Static measurements and dynamic variables based on heart–lung interactions all combine to predict and measure fluid responsiveness and assess response to intravenous fluid esuscitation. Thorough knowledge of these variables, the physiology behind them and the pitfalls in their use allows the echocardiographer to confidently assess these patients and in combination with clinical judgement manage them appropriately.

Use of PET to monitor the response of lung cancer to radiation treatment

International Nuclear Information System (INIS)

Erdi, Y.E.; Humm, J.L.; Erdi, A.K.; Yorke, E.D.; Macapinlac, H.; Larson, S.M.; Rosenzweig, K.E.

2000-01-01

Approximately 170,000 people are diagnosed with lung cancer in the United States each year. Many of these patients receive external beam radiation for treatment. Fluorine-18 2-fluoro-2-deoxy-d-glucose positron emission tomography (FDG PET) is increasingly being used in evaluating non-small cell lung cancer and may be of clinical utility in assessing response to treatment. In this report, we present FDG PET images and data from two patients who were followed with a total of eight and seven serial FDG PET scans, respectively, through the entire course of their radiation therapy. Changes in several potential response parameters are shown versus time, including lesion volume (V FDG ) by PET, SUV av , SUV max , and total lesion glycolysis (TLG) during the course of radiotherapy. The response parameters for patient 1 demonstrated a progressive decrease; however, the response parameters for patient 2 showed an initial decrease followed by an increase. The data presented here may suggest that the outcome of radiation therapy can be predicted by PET imaging, but this observation requires a study of additional patients. (orig.)

Model Predictive Control based on Finite Impulse Response Models

DEFF Research Database (Denmark)

Prasath, Guru; Jørgensen, John Bagterp

2008-01-01

We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...... and related to the uncertainty of the impulse response coefficients. The simulations can be used to benchmark l2 MPC against FIR based robust MPC as well as to estimate the maximum performance improvements by robust MPC....

Low pre-treatment end-tidal CO2 predicts dropout from cognitive-behavioral therapy for anxiety and related disorders.

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Tolin, David F; Billingsley, Amber L; Hallion, Lauren S; Diefenbach, Gretchen J

2017-03-01

Recent clinical trial research suggests that baseline low end-tidal CO 2 (ETCO 2 , the biological marker of hyperventilation) may predict poorer response to cognitive-behavioral therapy (CBT) for anxiety-related disorders. The present study examined the predictive value of baseline ETCO 2 among patients treated for such disorders in a naturalistic clinical setting. Sixty-nine adults with a primary diagnosis of a DSM-5 anxiety disorder, obsessive-compulsive disorder, or posttraumatic stress disorder completed a 4-min assessment of resting ETCO 2 , and respiration rate (the first minute was analyzed). Lower ETCO 2 was not associated with a diagnosis of panic disorder, and was associated with lower subjective distress ratings on certain measures. Baseline ETCO 2 significantly predicted treatment dropout: those meeting cutoff criteria for hypocapnia were more than twice as likely to drop out of treatment, and ETCO 2 significantly predicted dropout beyond other pre-treatment variables. Weekly measurement suggested that the lower-ETCO 2 patients who dropped out were not responding well to treatment prior to dropout. The present results, along with previous clinical trial data, suggest that lower pre-treatment ETCO 2 is a negative prognostic indicator for CBT for anxiety-related disorders. It is suggested that patients with lower ETCO 2 might benefit from additional intervention that targets respiratory abnormality. Copyright © 2016 Elsevier Ltd. All rights reserved.

Texture analysis of advanced non-small cell lung cancer (NSCLC) on contrast-enhanced computed tomography: prediction of the response to the first-line chemotherapy

International Nuclear Information System (INIS)

Farina, Davide; Morassi, Mauro; Maroldi, Roberto; Roca, Elisa; Tassi, Gianfranco; Cavalleri, Giuseppe

2013-01-01

To assess whether tumour heterogeneity, quantified by texture analysis (TA) on contrast-enhanced computed tomography (CECT), can predict response to chemotherapy in advanced non-small cell lung cancer (NSCLC). Fifty-three CECT studies of patients with advanced NSCLC who had undergone first-line chemotherapy were retrospectively reviewed. Response to chemotherapy was evaluated according to RECIST1.1. Tumour uniformity was assessed by a TA method based on Laplacian of Gaussian filtering. The resulting parameters were correlated with treatment response and overall survival by multivariate analysis. Thirty-one out of 53 patients were non-responders and 22 were responders. Average overall survival was 13 months (4-35), minimum follow-up was 12 months. In the adenocarcinoma group (n = 31), the product of tumour uniformity and grey level (GL*U) was the unique independent variable correlating with treatment response. Dividing the GL*U (range 8.5-46.6) into tertiles, lesions belonging to the second and the third tertiles had an 8.3-fold higher probability of treatment response compared with those in the first tertile. No association between texture features and response to treatment was observed in the non-adenocarcinoma group (n = 22). GL*U did not correlate with overall survival. TA on CECT images in advanced lung adenocarcinoma provides an independent predictive indicator of response to first-line chemotherapy. (orig.)

FDG-PET for prediction of tumour aggressiveness and response to intra-arterial chemotherapy and radiotherapy in head and neck cancer

International Nuclear Information System (INIS)

Kitagawa, Yoshimasa; Sano, Kazuo; Nakamura, Mikiko; Ogasawara, Toshiyuki; Nishizawa, Sadahiko; Sadato, Norihiro; Yonekura, Yoshiharu

2003-01-01

The aim of this study was to evaluate the possible usefulness of fluorine-18 fluorodeoxyglucose positron emission tomography (FDG-PET) for predicting tumour aggressiveness and response to intra-arterial chemotherapy (THP-ADM + 5-FU + carboplatin) and radiotherapy in head and neck carcinomas. Twenty patients with squamous cell carcinoma (SCC) of the head and neck were included in the study. All patients completed the treatment regimen, and each patient underwent two FDG-PET studies, one prior to and one at 4 weeks after the chemoradiotherapy. For the quantitative evaluation of regional FDG uptake in the tumour, standardised uptake values (SUVs) with an uptake period of 50 min were used. The pre-treatment SUV (pre-SUV) and post-treatment SUV (post-SUV) were compared with immunohistologically evaluated tumour proliferative potential (MIB-1 and PCNA), tumour cellularity and other parameters including histological grade, tumour size and stage, clinical response and histological evaluation after therapy. All neoplastic lesions showed high SUVs (mean, 9.75 mg/ml) prior to the treatment, which decreased significantly after the therapy (3.41 mg/ml, P 7 mg/ml) showed residual tumour cells after treatment in 4 out of 15 patients, whereas patients whose lesions showed a low pre-SUV (<7 mg/ml, five patients) were successfully treated. Four out of six tumours with a post-SUV higher than 4 mg/ml had viable tumour cells, whereas all tumours (14/14) with a post-SUV lower than 4 mg/ml showed no viable tumour cells. Computational multivariate analysis using multiple regression revealed four factors (MIB-1 labelling index, cellularity, the number of MIB-1 labelled tumour cells and tumour size grade) contributing to pre-SUV and pre-post SUV (difference between pre-treatment SUV and post-treatment SUV in each patient) with statistical significance. FDG uptake in the tumour might reflect tumour aggressiveness, which is closely related to the proliferative activity and cellularity. Pre-treatment

Genetic polymorphisms in 5-Fluorouracil-related enzymes predict pathologic response after neoadjuvant chemoradiation for rectal cancer.

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Nelson, Bailey; Carter, Jane V; Eichenberger, Maurice R; Netz, Uri; Galandiuk, Susan

2016-11-01

Many patients with rectal cancer undergo preoperative neoadjuvant chemoradiation, with approximately 70% exhibiting pathologic downstaging in response to treatment. Currently, there is no accurate test to predict patients who are likely to be complete responders to therapy. 5-Fluorouracil is used regularly in the neoadjuvant treatment of rectal cancer. Genetic polymorphisms affect the activity of thymidylate synthase, an enzyme involved in 5-Fluorouracil metabolism, which may account for observed differences in response to neoadjuvant treatment between patients. Detection of genetic polymorphisms might identify patients who are likely to have a complete response to neoadjuvant therapy and perhaps allow them to avoid operation. DNA was isolated from whole blood taken from patients with newly diagnosed rectal cancer who received neoadjuvant therapy (n = 50). Response to therapy was calculated with a tumor regression score based on histology from the time of operation. Polymerase chain reaction was performed targeting the promoter region of thymidylate synthase. Polymerase chain reaction products were separated using electrophoresis to determine whether patients were homozygous for a double-tandem repeat (2R), a triple-tandem repeat (3R), or were heterozygous (2R/3R). A single nucleotide polymorphism, 3G or 3C, also may be present in the second repeat unit of the triple-tandem repeat allele. Restriction fragment length polymorphism assays were performed in patients with at least one 3R allele using HaeIII. Patients with at least 1 thymidylate synthase 3G allele were more likely to have a complete or partial pathologic response to 5-Fluorouracil neoadjuvant therapy (odds ratio 10.4; 95% confidence interval, 1.3-81.6; P = .01) than those without at least one 3G allele. Identification of rectal cancer patients with specific genetic polymorphisms in enzymes involved in 5-Fluorouracil metabolism seems to predict the likelihood of complete or partial pathologic response

Advanced Computational Modeling Approaches for Shock Response Prediction

Science.gov (United States)

Derkevorkian, Armen; Kolaini, Ali R.; Peterson, Lee

2015-01-01

Motivation: (1) The activation of pyroshock devices such as explosives, separation nuts, pin-pullers, etc. produces high frequency transient structural response, typically from few tens of Hz to several hundreds of kHz. (2) Lack of reliable analytical tools makes the prediction of appropriate design and qualification test levels a challenge. (3) In the past few decades, several attempts have been made to develop methodologies that predict the structural responses to shock environments. (4) Currently, there is no validated approach that is viable to predict shock environments overt the full frequency range (i.e., 100 Hz to 10 kHz). Scope: (1) Model, analyze, and interpret space structural systems with complex interfaces and discontinuities, subjected to shock loads. (2) Assess the viability of a suite of numerical tools to simulate transient, non-linear solid mechanics and structural dynamics problems, such as shock wave propagation.

Use of reflectance spectrophotometry to predict the response of port wine stains to pulsed dye laser.

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Halachmi, Shlomit; Azaria, Ron; Inbar, Roy; Ad-El, Dean; Lapidoth, Moshe

2014-01-01

Reflectance spectroscopy can be used to quantitate subtle differences in color. We applied a portable reflectance spectrometer to determine its utility in the evaluation of pulsed dye laser treatment of port wine stains (PWS) and in prediction of clinical outcome, in a prospective study. Forty-eight patients with PWS underwent one to nine pulsed dye laser treatments. Patient age and skin color as well as PWS surface area, anatomic location, and color were recorded. Pretreatment spectrophotometric measurements were performed. The subjective clinical results of treatment and the quantitative spectrophotometry results were evaluated by two independent teams, and the findings were correlated. The impact of the clinical characteristics on the response to treatment was assessed as well. Patients with excellent to good clinical results of laser treatments had pretreatment spectrophotometric measurements which differed by more than 10%, whereas patients with fair to poor results had spectrophotometric measurements with a difference of of less than 10%. The correlation between the spectrophotometric results and the clinical outcome was 73% (p Spectrophotometry has a higher correlation with clinical outcome and a better predictive value than other nonmeasurable, nonquantitative, dependent variables.

The potential of predictive analytics to provide clinical decision support in depression treatment planning.

Science.gov (United States)

Kessler, Ronald C

2018-01-01

To review progress developing clinical decision support tools for personalized treatment of major depressive disorder (MDD). Over the years, a variety of individual indicators ranging from biomarkers to clinical observations and self-report scales have been used to predict various aspects of differential MDD treatment response. Most of this work focused on predicting remission either with antidepressant medications versus psychotherapy, some antidepressant medications versus others, some psychotherapies versus others, and combination therapies versus monotherapies. However, to date, none of the individual predictors in these studies has been strong enough to guide optimal treatment selection for most patients. Interest consequently turned to decision support tools made up of multiple predictors, but the development of such tools has been hampered by small study sample sizes. Design recommendations are made here for future studies to address this problem. Recommendations include using large prospective observational studies followed by pragmatic trials rather than smaller, expensive controlled treatment trials for preliminary development of decision support tools; basing these tools on comprehensive batteries of inexpensive self-report and clinical predictors (e.g., self-administered performance-based neurocognitive tests) versus expensive biomarkers; and reserving biomarker assessments for targeted studies of patients not well classified by inexpensive predictor batteries.

Prediction of week 4 virological response in hepatitis C for making decision on triple therapy: the Optim study.

Directory of Open Access Journals (Sweden)

Manuel Romero-Gómez

Full Text Available Virological response to peginterferon + ribavirin (P+R at week 4 can predict sustained virological response (SVR. While patients with rapid virological response (RVR do not require triple therapy, patients with a decline <1 log10 IU/ml HCVRNA (D1L should have treatment discontinued due to low SVR rate.To develop a tool to predict first 4 weeks' viral response in patients with hepatitis C genotype 1&4 treated with P+R.In this prospective and multicenter study, HCV mono-infected (n=538 and HCV/HIV co-infected (n=186 patients were included. To develop and validate a prognostic tool to detect RVR and D1L, we segregated the patients as an estimation cohort (to construct the model and a validation cohort (to validate the model.D1L was reached in 509 (80.2% and RVR in 148 (22.5% patients. Multivariate analyses demonstrated that HIV co-infection, Forns' index, LVL, IL28B-CC and Genotype-1 were independently related to RVR as well as D1L. Diagnostic accuracy (AUROC for D1L was: 0.81 (95%CI: 0.76 ̶ 0.86 in the estimation cohort and 0.71 (95%CI: 0.62 ̶ 0.79 in the validation cohort; RVR prediction: AUROC 0.83 (95%CI: 0.78 ̶ 0.88 in the estimation cohort and 0.82 (95%CI: 0.76 ̶ 0.88 in the validation cohort. Cost-analysis of standard 48-week treatment indicated a saving of 30.3% if the prognostic tool is implemented.The combination of genetic (IL28B polymorphism and viral genotype together with viral load, HIV co-infection and fibrosis stage defined a tool able to predict RVR and D1L at week 4. Using this tool would be a cost-saving strategy compared to universal triple therapy for hepatitis C.

Predictors of treatment response to an adjunctive emotion regulation group therapy for deliberate self-harm among women with borderline personality disorder.

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Gratz, Kim L; Dixon-Gordon, Katherine L; Tull, Matthew T

2014-01-01

Despite evidence for the efficacy of several treatments for deliberate self-harm (DSH) within borderline personality disorder (BPD), predictors of response to these treatments remain unknown. This study examined baseline demographic, clinical, and diagnostic predictors of treatment response to an adjunctive emotion regulation group therapy (ERGT) for DSH among women with BPD. A recent RCT provided evidence for the efficacy of this ERGT (relative to a treatment-as-usual only waitlist condition). Participants in this study include the full intent-to-treat sample who began ERGT (across treatment and waitlist conditions; n = 51). Baseline diagnostic and clinical data were collected at the initial assessment, and outcome measures of DSH and self-destructive behaviors, emotion dysregulation/avoidance, and BPD symptoms (among others) were administered at pretreatment, posttreatment, and 3- and 9-months posttreatment. Notably, both demographic variables and characteristics of participants' ongoing therapy in the community had minimal impact on treatment response. However, several indicators of greater severity in domains relevant to this ERGT (i.e., baseline emotion dysregulation and BPD criteria, lifetime and recent DSH, and past-year hospitalization and suicide attempts) predicted better responses during treatment and follow-up across the primary targets of treatment. Likewise, several co-occurring disorders (i.e., social phobia, panic disorder, and a cluster B personality disorder) predicted greater improvements in BPD symptoms during treatment or follow-up. Finally, although co-occurring generalized anxiety disorder, posttraumatic stress disorder, and cluster A and C personality disorders were associated with poorer treatment response during follow-up, most of these effects reflected a lack of continued improvements during this period (vs. worsening of symptoms).

Cytotoxic T lymphocyte response to peptide vaccination predicts survival in stage III colorectal cancer.

Science.gov (United States)

Kawamura, Junichiro; Sugiura, Fumiaki; Sukegawa, Yasushi; Yoshioka, Yasumasa; Hida, Jin-Ichi; Hazama, Shoichi; Okuno, Kiyotaka

2018-02-23

We previously reported a phase I clinical trial of a peptide vaccine ring finger protein 43 (RNF43) and 34-kDa translocase of the outer mitochondrial membrane (TOMM34) combined with uracil-tegafur (UFT)/LV for patients with metastatic colorectal cancer (CRC), and demonstrated the safety and immunological responsiveness of this combination therapy. In this study, we evaluated vaccination-induced immune responses to clarify the survival benefit of the combination therapy as adjuvant treatment. We enrolled 44 patients initially in an HLA-masked fashion. After the disclosure of HLA, 28 patients were in the HLA-A*2402-matched and 16 were in the unmatched group. In the HLA-matched group, 14 patients had positive CTL responses specific for the RNF43 and/or TOMM34 peptides after 2 cycles of treatment and 9 had negative responses; in the HLA-unmatched group, 10 CTL responses were positive and 2 negative. In the HLA-matched group, 3-year relapse-free survival (RFS) was significantly better in the positive CTL subgroup than in the negative-response subgroup. Patients with negative vaccination-induced CTL responses showed a significant trend towards shorter RFS than those with positive responses. Moreover, in the HLA-unmatched group, the positive CTL response subgroup showed an equally good 3-year RFS as in the HLA-matched group. In conclusion, vaccination-induced CTL response to peptide vaccination could predict survival in the adjuvant setting for stage III CRC. © 2018 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.

Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis.

Directory of Open Access Journals (Sweden)

Bart V J Cuppen

Full Text Available In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA respond insufficiently to TNF-α inhibitors (TNFis. The aim of the study was to explore the use of a metabolomics to identify predictors for the outcome of TNFi therapy, and study the metabolomic fingerprint in active RA irrespective of patients' response. In the metabolomic profiling, lipids, oxylipins, and amines were measured in serum samples of RA patients from the observational BiOCURA cohort, before start of biological treatment. Multivariable logistic regression models were established to identify predictors for good- and non-response in patients receiving TNFi (n = 124. The added value of metabolites over prediction using clinical parameters only was determined by comparing the area under receiver operating characteristic curve (AUC-ROC, sensitivity, specificity, positive- and negative predictive value and by the net reclassification index (NRI. The models were further validated by 10-fold cross validation and tested on the complete TNFi treatment cohort including moderate responders. Additionally, metabolites were identified that cross-sectionally associated with the RA disease activity score based on a 28-joint count (DAS28, erythrocyte sedimentation rate (ESR or C-reactive protein (CRP. Out of 139 metabolites, the best-performing predictors were sn1-LPC(18:3-ω3/ω6, sn1-LPC(15:0, ethanolamine, and lysine. The model that combined the selected metabolites with clinical parameters showed a significant larger AUC-ROC than that of the model containing only clinical parameters (p = 0.01. The combined model was able to discriminate good- and non-responders with good accuracy and to reclassify non-responders with an improvement of 30% (total NRI = 0.23 and showed a prediction error of 0.27. For the complete TNFi cohort, the NRI was 0.22. In addition, 88 metabolites were associated with DAS28, ESR or CRP (p<0.05. Our study established an accurate

Pharmacokinetic-Pharmacodynamic modelling of intracellular Mycobacterium tuberculosis growth and kill rates is predictive of clinical treatment duration.

Science.gov (United States)

Aljayyoussi, Ghaith; Jenkins, Victoria A; Sharma, Raman; Ardrey, Alison; Donnellan, Samantha; Ward, Stephen A; Biagini, Giancarlo A

2017-03-29

Tuberculosis (TB) treatment is long and complex, typically involving a combination of drugs taken for 6 months. Improved drug regimens to shorten and simplify treatment are urgently required, however a major challenge to TB drug development is the lack of predictive pre-clinical tools. To address this deficiency, we have adopted a new high-content imaging-based approach capable of defining the killing kinetics of first line anti-TB drugs against intracellular Mycobacterium tuberculosis (Mtb) residing inside macrophages. Through use of this pharmacokinetic-pharmacodynamic (PK-PD) approach we demonstrate that the killing dynamics of the intracellular Mtb sub-population is critical to predicting clinical TB treatment duration. Integrated modelling of intracellular Mtb killing alongside conventional extracellular Mtb killing data, generates the biphasic responses typical of those described clinically. Our model supports the hypothesis that the use of higher doses of rifampicin (35 mg/kg) will significantly reduce treatment duration. Our described PK-PD approach offers a much needed decision making tool for the identification and prioritisation of new therapies which have the potential to reduce TB treatment duration.

Resting state functional connectivity predicts neurofeedback response

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Dustin eScheinost

2014-09-01

Full Text Available Tailoring treatments to the specific needs and biology of individual patients – personalized medicine – requires delineation of reliable predictors of response. Unfortunately, these have been slow to emerge, especially in neuropsychiatric disorders. We have recently described a real-time functional magnetic resonance imaging (rt-fMRI neurofeedback protocol that can reduce contamination-related anxiety, a prominent symptom of many cases of obsessive-compulsive disorder (OCD. Individual response to this intervention is variable. Here we used patterns of brain functional connectivity, as measured by baseline resting-state fMRI (rs-fMRI, to predict improvements in contamination anxiety after neurofeedback training. Activity of a region of the orbitofrontal cortex (OFC and anterior prefrontal cortex, Brodmann area (BA 10, associated with contamination anxiety in each subject was measured in real time and presented as a neurofeedback signal, permitting subjects to learn to modulate this target brain region. We have previously reported both enhanced OFC/BA 10 control and improved anxiety in a group of subclinically anxious subjects after neurofeedback. Five individuals with contamination-related OCD who underwent the same protocol also showed improved clinical symptomatology. In both groups, these behavioral improvements were strongly correlated with baseline whole-brain connectivity in the OFC/BA 10, computed from rs-fMRI collected several days prior to neurofeedback training. These pilot data suggest that rs-fMRI can be used to identify individuals likely to benefit from rt-fMRI neurofeedback training to control contamination anxiety.

Evolution of nodule stiffness might predict response to local ablative therapy: A series of patients with hepatocellular carcinoma.

Directory of Open Access Journals (Sweden)

Michael Praktiknjo

Full Text Available Early information on treatment response of HCC to local ablative therapy is crucial. Elastography as a non-invasive method has recently been shown to play a potential role in distinguishing between benign and malignant liver lesions. Elastography of hepatocellular carcinoma (HCC in early response to local ablative therapy has not been studied to date.We prospectively included a cohort of 14 patients with diagnosis of HCC who were treated with local ablative therapy (transarterial chemoembolization, TACE and/or radiofrequency ablation, RFA. We used 2D shear-wave elastography (RT 2D-SWE to examine stiffness of HCC lesion before and 3, 30 and 90 days after local ablative therapy. Contrast-enhanced imaging after 90 days was performed to evaluate treatment response. Primary endpoint was stiffness of HCC in response to local ablative therapy. Secondary end point was tumor recurrence.Stiffness of HCC nodules and liver showed no significant difference prior to local ablative therapy. As early as three days after treatment, stiffness of responding HCC was significantly higher compared to non-responding. Higher stiffness before treatment was significantly associated with tumor recurrence.Nodule stiffness in general and RT 2D-SWE in particular could provide a useful tool for early prediction of HCC response to local ablative therapy.

SU-E-J-04: A Data-Driven, Response-Based, Multi-Criteria Decision Support System for Personalized Lung Radiation Treatment Planning

International Nuclear Information System (INIS)

Luo, Y; McShan, D; Schipper, M; Matuszak, M; Ten Haken, R; Kong, F

2014-01-01

Purpose: To develop a decision support tool to predict a patient's potential overall survival (OS) and radiation induced toxicity (RIT) based on clinical factors and responses during the course of radiotherapy, and suggest appropriate radiation dose adjustments to improve therapeutic effect. Methods: Important relationships between a patient's basic information and their clinical features before and during the radiation treatment are identified from historical clinical data by using statistical learning and data mining approaches. During each treatment period, a data analysis (DA) module predicts radiotherapy features such as time to local progression (TTLP), time to distant metastases (TTDM), radiation toxicity to different organs, etc., under possible future treatment plans based on patient specifics or responses. An information fusion (IF) module estimates intervals for a patient's OS and the probabilities of RIT from a treatment plan by integrating the outcomes of module DA. A decision making (DM) module calculates “satisfaction” with the predicted radiation outcome based on trade-offs between OS and RIT, and finds the best treatment plan for the next time period via multi-criteria optimization. Results: Using physical and biological data from 130 lung cancer patients as our test bed, we were able to train and implement the 3 modules of our decision support tool. Examples demonstrate how it can help predict a new patient's potential OS and RIT with different radiation dose plans along with how these combinations change with dose, thus presenting a range of satisfaction/utility for use in individualized decision support. Conclusion: Although the decision support tool is currently developed from a small patient sample size, it shows the potential for the improvement of each patient's satisfaction in personalized radiation therapy. The radiation treatment outcome prediction and decision making model needs to be evaluated with more patients and demonstrated for

Personality and Differential Treatment Response in Major Depression: A Randomized Controlled Trial Comparing Cognitive-Behavioural Therapy and Pharmacotherapy

Science.gov (United States)

Bagby, R Michael; Quilty, Lena C; Segal, Zindel V; McBride, Carolina C; Kennedy, Sidney H; Costa, Paul T

2008-01-01

Objective Effective treatments for major depressive disorder exist, yet some patients fail to respond, or achieve only partial response. One approach to optimizing treatment success is to identify which patients are more likely to respond best to which treatments. The objective of this investigation was to determine if patient personality characteristics are predictive of response to either cognitive-behavioural therapy (CBT) or pharmacotherapy (PHT). Method Depressed patients completed the Revised NEO Personality Inventory, which measures the higher-order domain and lower-order facet traits of the Five-Factor Model of Personality, and were randomized to receive either CBT or PHT. Result Four personality traits—the higher-order domain neuroticism and 3 lower-order facet traits: trust, straightforwardness, and tendermindedness—were able to distinguish a differential response rate to CBT, compared with PHT. Conclusion The assessment of patient dimensional personality traits can assist in the selection and optimization of treatment response for depressed patients. PMID:18616856

Response prediction of long flexible risers subject to forced harmonic vibration

OpenAIRE

Riveros, Carlos Alberto; Utsunomiya, Tomoaki; Maeda, Katsuya; Itoh, Kazuaki

2010-01-01

Several research efforts have been directed toward the development of models for response prediction of flexible risers. The main difficulties arise from the fact that the dynamic response of flexible risers involves highly nonlinear behavior and a self-regulated process. This article presents a quasi-steady approach for response prediction of oscillating flexible risers. Amplitude-dependent lift coefficients are considered, as is an increased mean drag coefficient model during synchronizatio...

Confirmation of an IRAK3 polymorphism as a genetic marker predicting response to anti-TNF treatment in rheumatoid arthritis

DEFF Research Database (Denmark)

Sode, Jacob; Vogel, U; Bank, Steffen

2018-01-01

the IRAK3 rs11541076 variant as associated (odds ratio (OR)=1.33, 95% confidence interval (CI): 1.00-1.77, P-value=0.047) with a positive treatment response (EULAR (European League Against Rheumatism) good/moderate vs none response at 4±2 months), and found the NLRP3 rs461266 variant associated (OR=0...

Deterministic Predictions of Vessel Responses Based on Past Measurements

DEFF Research Database (Denmark)

Nielsen, Ulrik Dam; Jensen, Jørgen Juncher

2017-01-01

The paper deals with a prediction procedure from which global wave-induced responses can be deterministically predicted a short time, 10-50 s, ahead of current time. The procedure relies on the autocorrelation function and takes into account prior measurements only; i.e. knowledge about wave...

3P: Personalized Pregnancy Prediction in IVF Treatment Process

Science.gov (United States)

Uyar, Asli; Ciray, H. Nadir; Bener, Ayse; Bahceci, Mustafa

We present an intelligent learning system for improving pregnancy success rate of IVF treatment. Our proposed model uses an SVM based classification system for training a model from past data and making predictions on implantation outcome of new embryos. This study employs an embryo-centered approach. Each embryo is represented with a data feature vector including 17 features related to patient characteristics, clinical diagnosis, treatment method and embryo morphological parameters. Our experimental results demonstrate a prediction accuracy of 82.7%. We have obtained the IVF dataset from Bahceci Women Health, Care Centre, in Istanbul, Turkey.

PREDICTION OF SURGICAL TREATMENT WITH POUR PERITONITIS QUANTIFYING RISK FACTORS

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І. К. Churpiy

2012-11-01

Full Text Available Explored the possibility of quantitative assessment of risk factors of complications in the treatment of diffuse peritonitis. Highlighted 53 groups of features that are important in predicting the course of diffuse peritonitis. The proposed scheme of defining the risk of clinical course of diffuse peritonitis can quantify the severity of the source of patients and in most cases correctly predict the results of treatment of disease.

A new patient classification for laser resurfacing and peels: predicting responses, risks, and results.

Science.gov (United States)

Fanous, Nabil

2002-01-01

Traditional classifications for skin treatment modalities are based on skin characteristics, the most important being skin color. Other factors are considered as well, such as oiliness, thickness, pathology, and sensitivity. While useful, these classifications are occasionally inadequate in predicting and explaining the outcome of some peels, dermabrasions, or laser resurfacing procedures. Why, for example, would a Korean patient with a light white skin inadvertently develop more hyperpigmentation than his darker skinned French counterpart? The new classification introduced here is based on the racial and genetic origins of patients. It suggests that racial genetic predisposition is the determining factor in human response to skin injury, including skin treatments. This classification takes into account both skin and features, rather than skin alone. It offers a new approach in evaluating patients scheduled for skin peels or laser resurfacing, in the hope of helping physicians to better predict reactions, select the appropriate type and intensity of the skin treatment and, ultimately, better control the outcome. Six categories (sub-races) are described: Nordics, Europeans, Mediterraneans, Indo-Pakistanis, Africans, and Asians. The reaction of each sub-race to peels, laser resurfacing, or dermabrasion is analyzed. The risks associated with each group are noted. This new classification provides physicians with a practical way to evaluate patients prior to treatment, with a view to determining each patient's suitability, postoperative reaction, the likelihood of complications, and likely result.

Predicting Social Anxiety Treatment Outcome Based on Therapeutic Email Conversations.

Science.gov (United States)

Hoogendoorn, Mark; Berger, Thomas; Schulz, Ava; Stolz, Timo; Szolovits, Peter

2017-09-01

Predicting therapeutic outcome in the mental health domain is of utmost importance to enable therapists to provide the most effective treatment to a patient. Using information from the writings of a patient can potentially be a valuable source of information, especially now that more and more treatments involve computer-based exercises or electronic conversations between patient and therapist. In this paper, we study predictive modeling using writings of patients under treatment for a social anxiety disorder. We extract a wealth of information from the text written by patients including their usage of words, the topics they talk about, the sentiment of the messages, and the style of writing. In addition, we study trends over time with respect to those measures. We then apply machine learning algorithms to generate the predictive models. Based on a dataset of 69 patients, we are able to show that we can predict therapy outcome with an area under the curve of 0.83 halfway through the therapy and with a precision of 0.78 when using the full data (i.e., the entire treatment period). Due to the limited number of participants, it is hard to generalize the results, but they do show great potential in this type of information.

Exposure Plus Response-Prevention Treatment of Bulimia Nervosa.

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Leitenberg, Harold; And Others

1988-01-01

Evaluated exposure plus response-prevention treatment of bulimia nervosa among 47 women. Subjects were assigned to either exposure plus response-prevention in one setting, exposure plus response-prevention in multiple settings, cognitive-behavioral therapy, or waiting-list control conditions. Found three treatment groups improved significantly on…

Probabilistic application of a fugacity model to predict triclosan fate during wastewater treatment.

Science.gov (United States)

Bock, Michael; Lyndall, Jennifer; Barber, Timothy; Fuchsman, Phyllis; Perruchon, Elyse; Capdevielle, Marie

2010-07-01

The fate and partitioning of the antimicrobial compound, triclosan, in wastewater treatment plants (WWTPs) is evaluated using a probabilistic fugacity model to predict the range of triclosan concentrations in effluent and secondary biosolids. The WWTP model predicts 84% to 92% triclosan removal, which is within the range of measured removal efficiencies (typically 70% to 98%). Triclosan is predominantly removed by sorption and subsequent settling of organic particulates during primary treatment and by aerobic biodegradation during secondary treatment. Median modeled removal efficiency due to sorption is 40% for all treatment phases and 31% in the primary treatment phase. Median modeled removal efficiency due to biodegradation is 48% for all treatment phases and 44% in the secondary treatment phase. Important factors contributing to variation in predicted triclosan concentrations in effluent and biosolids include influent concentrations, solids concentrations in settling tanks, and factors related to solids retention time. Measured triclosan concentrations in biosolids and non-United States (US) effluent are consistent with model predictions. However, median concentrations in US effluent are over-predicted with this model, suggesting that differences in some aspect of treatment practices not incorporated in the model (e.g., disinfection methods) may affect triclosan removal from effluent. Model applications include predicting changes in environmental loadings associated with new triclosan applications and supporting risk analyses for biosolids-amended land and effluent receiving waters. (c) 2010 SETAC.

Predicting the effect of psychoeducational group treatment for hypochondriasis.

NARCIS (Netherlands)

Buwalda, F.M.; Bouman, T.K.

2008-01-01

Both individual cognitive-behavioural therapy and short-term psychoeducational courses have shown to be effective in reducing hypochondriacal complaints. However, it is unknown which patients benefit from treatment. The aim of the present study is to explore which variables predict treatment outcome

Selection of appropriate end-points (pCR vs 2yDFS) for tailoring treatments with prediction models in locally advanced rectal cancer

International Nuclear Information System (INIS)

Valentini, Vincenzo; Stiphout, Ruud G.P.M. van; Lammering, Guido; Gambacorta, Maria A.; Barba, Maria C.; Bebenek, Marek; Bonnetain, Franck; Bosset, Jean F.; Bujko, Krzysztof; Cionini, Luca; Gerard, Jean P.; Rödel, Claus; Sainato, Aldo; Sauer, Rolf; Minsky, Bruce D.; Collette, Laurence; Lambin, Philippe

2015-01-01

Purpose: Personalized treatments based on predictions for patient outcome require early characterization of a rectal cancer patient’s sensitivity to treatment. This study has two aims: (1) identify the main patterns of recurrence and response to the treatments (2) evaluate pathologic complete response (pCR) and two-year disease-free survival (2yDFS) for overall survival (OS) and their potential to be relevant intermediate endpoints to predict. Methods: Pooled and treatment subgroup analyses were performed on five large European rectal cancer trials (2795 patients), who all received long-course radiotherapy with or without concomitant and/or adjuvant chemotherapy. The ratio of distant metastasis (DM) and local recurrence (LR) rates was used to identify patient characteristics that increase the risk of recurrences. Findings: The DM/LR ratio decreased to a plateau in the first 2 years, revealing it to be a critical follow-up period. According to the patterns of recurrences, three patient groups were identified: 5–15% had pCR and were disease free after 2 years (excellent prognosis), 65–75% had no pCR but were disease free (good prognosis) and 15–30% had neither pCR nor 2yDFS (poor prognosis). Interpretation: Compared with pCR, 2yDFS is a stronger predictor of OS. To adapt treatment most efficiently, accurate prediction models should be developed for pCR to select patients for organ preservation and for 2yDFS to select patients for more intensified treatment strategies

Correlation of uptake patterns on single-photon emission computed tomography/computed tomography (SPECT/CT)and treatment response in patients with knee pain

International Nuclear Information System (INIS)

Koh, Geon; Hwang, Kyung Hoon; Lee, Hae Jin; Kim, Seog Gyun; Lee, Beom Koo

2016-01-01

To determine whether treatment response in patients with knee pain could be predicted using uptake patterns on single-photon emission computed tomography/computed tomography (SPECT/CT) images. Ninety-five patients with knee pain who had undergone SPECT/CT were included in this retrospective study. Subjects were divided into three groups: increased focal uptake (FTU), increased irregular tracer uptake (ITU), and no tracer uptake (NTU). A numeric rating scale (NRS-11) assessed pain intensity. We analyzed the association between uptake patterns and treatment response using Pearson's chi-square test and Fisher's exact test. Uptake was quantified from SPECT/CT with region of interest (ROI) counting, and an intraclass correlation coefficient (ICC) calculated agreement. We used Student' t-test to calculate statistically significant differences of counts between groups and the Pearson correlation to measure the relationship between counts and initial NRS-1k1. Multivariate logistic regression analysis determined which variables were significantly associated with uptake. The FTU group included 32 patients; ITU, 39; and NTU, 24. With conservative management, 64 % of patients with increased tracer uptake (TU, both focal and irregular) and 36 % with NTU showed positive response. Conservative treatment response of FTU was better than NTU, but did not differ from that of ITU. Conservative treatment response of TU was significantly different from that of NTU (OR 3.1; p 0.036). Moderate positive correlation was observed between ITU and initial NRS-11. Age and initial NRS-11 significantly predicted uptake. Patients with uptake in their knee(s) on SPECT/CT showed positive treatment response under conservative treatment

Correlation of uptake patterns on single-photon emission computed tomography/computed tomography (SPECT/CT)and treatment response in patients with knee pain

Energy Technology Data Exchange (ETDEWEB)

Koh, Geon; Hwang, Kyung Hoon; Lee, Hae Jin; Kim, Seog Gyun; Lee, Beom Koo [Gachon University Gil Hospital, Incheon (Korea, Republic of)

2016-06-15

To determine whether treatment response in patients with knee pain could be predicted using uptake patterns on single-photon emission computed tomography/computed tomography (SPECT/CT) images. Ninety-five patients with knee pain who had undergone SPECT/CT were included in this retrospective study. Subjects were divided into three groups: increased focal uptake (FTU), increased irregular tracer uptake (ITU), and no tracer uptake (NTU). A numeric rating scale (NRS-11) assessed pain intensity. We analyzed the association between uptake patterns and treatment response using Pearson's chi-square test and Fisher's exact test. Uptake was quantified from SPECT/CT with region of interest (ROI) counting, and an intraclass correlation coefficient (ICC) calculated agreement. We used Student' t-test to calculate statistically significant differences of counts between groups and the Pearson correlation to measure the relationship between counts and initial NRS-1k1. Multivariate logistic regression analysis determined which variables were significantly associated with uptake. The FTU group included 32 patients; ITU, 39; and NTU, 24. With conservative management, 64 % of patients with increased tracer uptake (TU, both focal and irregular) and 36 % with NTU showed positive response. Conservative treatment response of FTU was better than NTU, but did not differ from that of ITU. Conservative treatment response of TU was significantly different from that of NTU (OR 3.1; p 0.036). Moderate positive correlation was observed between ITU and initial NRS-11. Age and initial NRS-11 significantly predicted uptake. Patients with uptake in their knee(s) on SPECT/CT showed positive treatment response under conservative treatment.

The thyroid-related quality of life measure ThyPRO has good responsiveness and ability to detect relevant treatment effects

DEFF Research Database (Denmark)

Watt, Torquil; Cramon, Per; Hegedüs, Laszlo

2014-01-01

responsiveness of the thyroid-related quality of life (QoL) instrument ThyPRO in patients undergoing relevant clinical treatments for benign thyroid diseases and to compare it with responsiveness of the generic SF-36 Health Survey. METHODS: A sample of 435 patients undergoing treatment completed the ThyPRO...... nontoxic goiter treated with surgery or radioactive iodine and remaining euthyroid (n = 62). Changes in QoL were evaluated in terms of effect size and compared to the changes predicted by clinical experts. The responsiveness of equivalent scales from ThyPRO and SF-36 Health Survey were compared...... with the relative validity index. RESULTS: The ThyPRO demonstrated good responsiveness across the whole range of QoL aspects in patients with hyper- and hypothyroidism. Responsiveness to treatment of nontoxic goiter was also demonstrated for physical and mental symptoms and overall QoL, but not for impact on social...

Predicting the response of olfactory sensory neurons to odor mixtures from single odor response

OpenAIRE

Marasco, Addolorata; De Paris, Alessandro; Migliore, Michele

2016-01-01

The response of olfactory receptor neurons to odor mixtures is not well understood. Here, using experimental constraints, we investigate the mathematical structure of the odor response space and its consequences. The analysis suggests that the odor response space is 3-dimensional, and predicts that the dose-response curve of an odor receptor can be obtained, in most cases, from three primary components with specific properties. This opens the way to an objective procedure to obtain specific o...

A novel approach to assess the treatment response using Gaussian random field in PET

Energy Technology Data Exchange (ETDEWEB)

Wang, Mengdie [Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China and Center for Advanced Medical Imaging Science, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114 (United States); Guo, Ning [Center for Advanced Medical Imaging Science, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114 (United States); Hu, Guangshu; Zhang, Hui, E-mail: hzhang@mail.tsinghua.edu.cn, E-mail: li.quanzheng@mgh.harvard.edu [Department of Biomedical Engineering, Tsinghua University, Beijing 100084 (China); El Fakhri, Georges; Li, Quanzheng, E-mail: hzhang@mail.tsinghua.edu.cn, E-mail: li.quanzheng@mgh.harvard.edu [Center for Advanced Medical Imaging Science, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114 and Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115 (United States)

2016-02-15

assessment utilizing local average method based on random field. The accuracy of the estimation was validated in terms of Euler distance and correlation coefficient. Results: It is shown that the performance of therapy response assessment is significantly improved by the introduction of variance with a higher area under the curve (97.3%) than SUVmean (91.4%) and SUVmax (82.0%). In addition, the FPR estimation serves as a good prediction for the specificity of the proposed method, consistent with simulation outcome with ∼1 correlation coefficient. Conclusions: In this work, the authors developed a method to evaluate therapy response from PET images, which were modeled as Gaussian random field. The digital phantom simulations demonstrated that the proposed method achieved a large reduction in statistical variability through incorporating knowledge of the variance of the original Gaussian random field. The proposed method has the potential to enable prediction of early treatment response and shows promise for application to clinical practice. In future work, the authors will report on the robustness of the estimation theory for application to clinical practice of therapy response evaluation, which pertains to binary discrimination tasks at a fixed location in the image such as detection of small and weak lesion.

Mothers' labeling responses to infants' gestures predict vocabulary outcomes.

Science.gov (United States)

Olson, Janet; Masur, Elise Frank

2015-11-01

Twenty-nine infants aged 1;1 and their mothers were videotaped while interacting with toys for 18 minutes. Six experimental stimuli were presented to elicit infant communicative bids in two communicative intent contexts - proto-declarative and proto-imperative. Mothers' verbal responses to infants' gestural and non-gestural communicative bids were coded for object and action labels. Relations between maternal labeling responses and infants' vocabularies at 1;1 and 1;5 were examined. Mothers' labeling responses to infants' gestural communicative bids were concurrently and predictively related to infants' vocabularies, whereas responses to non-gestural communicative bids were not. Mothers' object labeling following gestures in the proto-declarative context mediated the association from infants' gesturing in the proto-declarative context to concurrent noun lexicons and was the strongest predictor of subsequent noun lexicons. Mothers' action labeling after infants' gestural bids in the proto-imperative context predicted infants' acquisition of action words at 1;5. Findings show that mothers' responsive labeling explain specific relations between infants' gestures and their vocabulary development.

Prediction of permeability changes in an excavation response zone

International Nuclear Information System (INIS)

Kinoshita, Naoto; Ishii, Takashi; Kuroda, Hidetaka; Tada, Hiroyuki

1992-01-01

In geologic disposal of radioactive wastes, stress changes due to cavern excavation may expand the existing fractures and create possible bypasses for groundwater. This paper proposes a simple method for predicting permeability changes in the excavation response zones. Numerical analyses using this method predict that the response zones created by cavern excavation would differ greatly in thickness and permeability depending on the depth of the cavern site and the initial in-situ stress, that when the cavern site is deeper, response zones would expand more and permeability would increases more, and that if the ratio of horizontal to vertical in-situ stress is small, extensive permeable zones at the crown and the bottom would occur, whereas if the ratio is large, extensive permeable zones would occur in the side walls. (orig.)

Predicting Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer with Textural Features Derived from Pretreatment 18F-FDG PET/CT Imaging.

Science.gov (United States)

Beukinga, Roelof J; Hulshoff, Jan B; van Dijk, Lisanne V; Muijs, Christina T; Burgerhof, Johannes G M; Kats-Ugurlu, Gursah; Slart, Riemer H J A; Slump, Cornelis H; Mul, Véronique E M; Plukker, John Th M

2017-05-01

Adequate prediction of tumor response to neoadjuvant chemoradiotherapy (nCRT) in esophageal cancer (EC) patients is important in a more personalized treatment. The current best clinical method to predict pathologic complete response is SUV max in 18 F-FDG PET/CT imaging. To improve the prediction of response, we constructed a model to predict complete response to nCRT in EC based on pretreatment clinical parameters and 18 F-FDG PET/CT-derived textural features. Methods: From a prospectively maintained single-institution database, we reviewed 97 consecutive patients with locally advanced EC and a pretreatment 18 F-FDG PET/CT scan between 2009 and 2015. All patients were treated with nCRT (carboplatin/paclitaxel/41.4 Gy) followed by esophagectomy. We analyzed clinical, geometric, and pretreatment textural features extracted from both 18 F-FDG PET and CT. The current most accurate prediction model with SUV max as a predictor variable was compared with 6 different response prediction models constructed using least absolute shrinkage and selection operator regularized logistic regression. Internal validation was performed to estimate the model's performances. Pathologic response was defined as complete versus incomplete response (Mandard tumor regression grade system 1 vs. 2-5). Results: Pathologic examination revealed 19 (19.6%) complete and 78 (80.4%) incomplete responders. Least absolute shrinkage and selection operator regularization selected the clinical parameters: histologic type and clinical T stage, the 18 F-FDG PET-derived textural feature long run low gray level emphasis, and the CT-derived textural feature run percentage. Introducing these variables to a logistic regression analysis showed areas under the receiver-operating-characteristic curve (AUCs) of 0.78 compared with 0.58 in the SUV max model. The discrimination slopes were 0.17 compared with 0.01, respectively. After internal validation, the AUCs decreased to 0.74 and 0.54, respectively. Conclusion

Evaluation of candidate biomarkers to predict cancer cell sensitivity or resistance to PARP-1 inhibitor treatment

DEFF Research Database (Denmark)

Oplustilova, L.; Wolanin, K.; Bartkova, J.

2012-01-01

combinations with camptothecin or ionizing radiation. Furthermore, monitoring pARsylation and Rad51 foci formation as surrogate markers for PARP activity and HR, respectively, supported their candidacy for biomarkers of PARP-1i responses. As to resistance mechanisms, we confrmed the role of the multidrug......(ADp-ribose) polymerase-1 (PARP-1), an enzyme critical for repair pathways alternative to HR. While promising, treatment with PARP-1 inhibitors (PARP-1i) faces some hurdles, including (1) acquired resistance, (2) search for other sensitizing, non-BRCA1/2 cancer defects and (3) lack of biomarkers to predict response...

Novel Biomarker for Prognosis, Treatment Response

Science.gov (United States)

An NCI Cancer Currents blog about a study of a new type of cancer biomarker that measures the extent of chromosomal instability as a way to potentially predict patient prognosis and help guide cancer treatment choices.

Time to treatment as an important factor for the response to methotrexate in juvenile idiopathic arthritis.

Science.gov (United States)

Albers, H M; Wessels, J A M; van der Straaten, R J H M; Brinkman, D M C; Suijlekom-Smit, L W A; Kamphuis, S S M; Girschick, H J; Wouters, C; Schilham, M W; le Cessie, S; Huizinga, T W J; Ten Cate, R; Guchelaar, H J

2009-01-15

Methotrexate (MTX) is the most commonly used disease-modifying antirheumatic drug in juvenile idiopathic arthritis (JIA). Currently, individual response to MTX cannot be reliably predicted. Identification of clinical and genetic factors that influence the response to MTX could be helpful in realizing the optimal treatment for individual patients. A cohort of 128 JIA patients treated with MTX were studied retrospectively. Eleven clinical parameters and genotypes of 6 single nucleotide polymorphisms in 5 genes related to the mechanism of action of MTX were compared between MTX responders and nonresponders using a multivariate regression analysis. The time from diagnosis to start of MTX treatment, physician's global assessment at baseline, and the starting dose were significantly associated with the response to MTX at 6 months after initiation. Patients with a shorter time from diagnosis to start of MTX and a higher disease activity according to the physician but with a lower MTX dose showed an increased response. The effect of the starting dose on MTX response seemed to be mainly due to the influence of the systemic JIA subtype. The time from diagnosis to start of MTX treatment and physician's global assessment at baseline were highly correlated. Therefore, the precise effect size of each independent variable could not be determined. In children with JIA, the time from diagnosis to start of MTX appears to be an important factor for MTX response. Our results suggest that an earlier start of MTX treatment will lead to an increased response.

Lateral prefrontal cortex activity during cognitive control of emotion predicts response to social stress in schizophrenia

Directory of Open Access Journals (Sweden)

Laura M. Tully, PhD

2014-01-01

Full Text Available LPFC dysfunction is a well-established neural impairment in schizophrenia and is associated with worse symptoms. However, how LPFC activation influences symptoms is unclear. Previous findings in healthy individuals demonstrate that lateral prefrontal cortex (LPFC activation during cognitive control of emotional information predicts mood and behavior in response to interpersonal conflict, thus impairments in these processes may contribute to symptom exacerbation in schizophrenia. We investigated whether schizophrenia participants show LPFC deficits during cognitive control of emotional information, and whether these LPFC deficits prospectively predict changes in mood and symptoms following real-world interpersonal conflict. During fMRI, 23 individuals with schizophrenia or schizoaffective disorder and 24 healthy controls completed the Multi-Source Interference Task superimposed on neutral and negative pictures. Afterwards, schizophrenia participants completed a 21-day online daily-diary in which they rated the extent to which they experienced mood and schizophrenia-spectrum symptoms, as well as the occurrence and response to interpersonal conflict. Schizophrenia participants had lower dorsal LPFC activity (BA9 during cognitive control of task-irrelevant negative emotional information. Within schizophrenia participants, DLPFC activity during cognitive control of emotional information predicted changes in positive and negative mood on days following highly distressing interpersonal conflicts. Results have implications for understanding the specific role of LPFC in response to social stress in schizophrenia, and suggest that treatments targeting LPFC-mediated cognitive control of emotion could promote adaptive response to social stress in schizophrenia.

A Model for Spheroid versus Monolayer Response of SK-N-SH Neuroblastoma Cells to Treatment with 15-Deoxy-PGJ2

Directory of Open Access Journals (Sweden)

Dorothy I. Wallace

2016-01-01

Full Text Available Researchers have observed that response of tumor cells to treatment varies depending on whether the cells are grown in monolayer, as in vitro spheroids or in vivo. This study uses data from the literature on monolayer treatment of SK-N-SH neuroblastoma cells with 15-deoxy-PGJ2 and couples it with data on growth rates for untreated SK-N-SH neuroblastoma cells grown as multicellular spheroids. A linear model is constructed for untreated and treated monolayer data sets, which is tuned to growth, death, and cell cycle data for the monolayer case for both control and treatment with 15-deoxy-PGJ2. The monolayer model is extended to a five-dimensional nonlinear model of in vitro tumor spheroid growth and treatment that includes compartments of the cell cycle (G1,S,G2/M as well as quiescent (Q and necrotic (N cells. Monolayer treatment data for 15-deoxy-PGJ2 is used to derive a prediction of spheroid response under similar treatments. For short periods of treatment, spheroid response is less pronounced than monolayer response. The simulations suggest that the difference in response to treatment of monolayer versus spheroid cultures observed in laboratory studies is a natural consequence of tumor spheroid physiology rather than any special resistance to treatment.

SU-E-T-629: Prediction of the ViewRay Radiotherapy Treatment Time for Clinical Logistics

Energy Technology Data Exchange (ETDEWEB)

Liu, S; Wooten, H; Wu, Y; Yang, D [Washington University in St Louis, St Louis, MO (United States)

2015-06-15

Purpose: An algorithm is developed in our clinic, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance-image guided radiation therapy (MR-IGRT) delivery system. This algorithm is necessary for managing patient treatment appointments, and is useful as an indicator to assess the treatment plan complexity. Methods: A patient’s total treatment delivery time, not including time required for localization, may be described as the sum of four components: (1) the treatment initialization time; (2) the total beam-on time; (3) the gantry rotation time; and (4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam-on time can be calculated using both the planned beam-on time and the decay-corrected delivery dose rate. To predict the remaining components, we quantitatively analyze the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle and MLC leaf positions of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, and between the furthest MLC leaf moving distance and the corresponding MLC motion time, the total delivery time is predicted using linear regression. Results: The proposed algorithm has demonstrated the feasibility of predicting the ViewRay treatment delivery time for any treatment plan of any patient. The average prediction error is 0.89 minutes or 5.34%, and the maximal prediction error is 2.09 minutes or 13.87%. Conclusion: We have developed a treatment delivery time prediction algorithm based on the analysis of previous patients’ treatment delivery records. The accuracy of our prediction is sufficient for guiding and arranging patient treatment appointments on a daily basis. The predicted delivery time could also be used as an indicator to assess the

SU-E-T-629: Prediction of the ViewRay Radiotherapy Treatment Time for Clinical Logistics

International Nuclear Information System (INIS)

Liu, S; Wooten, H; Wu, Y; Yang, D

2015-01-01

Purpose: An algorithm is developed in our clinic, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance-image guided radiation therapy (MR-IGRT) delivery system. This algorithm is necessary for managing patient treatment appointments, and is useful as an indicator to assess the treatment plan complexity. Methods: A patient’s total treatment delivery time, not including time required for localization, may be described as the sum of four components: (1) the treatment initialization time; (2) the total beam-on time; (3) the gantry rotation time; and (4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam-on time can be calculated using both the planned beam-on time and the decay-corrected delivery dose rate. To predict the remaining components, we quantitatively analyze the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle and MLC leaf positions of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, and between the furthest MLC leaf moving distance and the corresponding MLC motion time, the total delivery time is predicted using linear regression. Results: The proposed algorithm has demonstrated the feasibility of predicting the ViewRay treatment delivery time for any treatment plan of any patient. The average prediction error is 0.89 minutes or 5.34%, and the maximal prediction error is 2.09 minutes or 13.87%. Conclusion: We have developed a treatment delivery time prediction algorithm based on the analysis of previous patients’ treatment delivery records. The accuracy of our prediction is sufficient for guiding and arranging patient treatment appointments on a daily basis. The predicted delivery time could also be used as an indicator to assess the

Treatment of landfill leachate by irrigation of willow coppice - Plant response and treatment efficiency

International Nuclear Information System (INIS)

Aronsson, Paer; Dahlin, Torleif; Dimitriou, Ioannis

2010-01-01

Landfill leachates usually need to be treated before discharged, and using soil-plant systems for this has gained substantial interest in Sweden and in the UK. A three-year field study was conducted in central Sweden to quantify plant response, treatment efficiency and impact on groundwater quality of landfill leachate irrigation of short-rotation willow coppice (Salix). Two willow varieties were tested and four irrigation regimes in sixteen 400-m 2 plots. The willow plants did not react negatively, despite very high annual loads of nitrogen (≤2160 kg N/ha), chloride (≤8600 kg Cl/ha) and other elements. Mean annual growth was 1.5, 9.8 and 12.6 tonnes DM/ha during years 1-3. For one of two willow varieties tested, relative leaf length accurately predicted growth rate. Irrigation resulted in elevated groundwater concentrations of all elements applied. Treatment efficiency varied considerably for different elements, but was adequate when moderate loads were applied. - Short-rotation willow coppice was successfully used for treating a strong landfill leachate in central Sweden over three years.

Outcome Prediction in Mathematical Models of Immune Response to Infection.

Directory of Open Access Journals (Sweden)

Manuel Mai

Full Text Available Clinicians need to predict patient outcomes with high accuracy as early as possible after disease inception. In this manuscript, we show that patient-to-patient variability sets a fundamental limit on outcome prediction accuracy for a general class of mathematical models for the immune response to infection. However, accuracy can be increased at the expense of delayed prognosis. We investigate several systems of ordinary differential equations (ODEs that model the host immune response to a pathogen load. Advantages of systems of ODEs for investigating the immune response to infection include the ability to collect data on large numbers of 'virtual patients', each with a given set of model parameters, and obtain many time points during the course of the infection. We implement patient-to-patient variability v in the ODE models by randomly selecting the model parameters from distributions with coefficients of variation v that are centered on physiological values. We use logistic regression with one-versus-all classification to predict the discrete steady-state outcomes of the system. We find that the prediction algorithm achieves near 100% accuracy for v = 0, and the accuracy decreases with increasing v for all ODE models studied. The fact that multiple steady-state outcomes can be obtained for a given initial condition, i.e. the basins of attraction overlap in the space of initial conditions, limits the prediction accuracy for v > 0. Increasing the elapsed time of the variables used to train and test the classifier, increases the prediction accuracy, while adding explicit external noise to the ODE models decreases the prediction accuracy. Our results quantify the competition between early prognosis and high prediction accuracy that is frequently encountered by clinicians.

The Pupillary Orienting Response Predicts Adaptive Behavioral Adjustment after Errors.

Directory of Open Access Journals (Sweden)

Peter R Murphy

Full Text Available Reaction time (RT is commonly observed to slow down after an error. This post-error slowing (PES has been thought to arise from the strategic adoption of a more cautious response mode following deployment of cognitive control. Recently, an alternative account has suggested that PES results from interference due to an error-evoked orienting response. We investigated whether error-related orienting may in fact be a pre-cursor to adaptive post-error behavioral adjustment when the orienting response resolves before subsequent trial onset. We measured pupil dilation, a prototypical measure of autonomic orienting, during performance of a choice RT task with long inter-stimulus intervals, and found that the trial-by-trial magnitude of the error-evoked pupil response positively predicted both PES magnitude and the likelihood that the following response would be correct. These combined findings suggest that the magnitude of the error-related orienting response predicts an adaptive change of response strategy following errors, and thereby promote a reconciliation of the orienting and adaptive control accounts of PES.

Depression and anxiety predict sex-specific cortisol responses to interpersonal stress.

Science.gov (United States)

Powers, Sally I; Laurent, Heidemarie K; Gunlicks-Stoessel, Meredith; Balaban, Susan; Bent, Eileen

2016-07-01

Clinical theories posit interpersonal stress as an important factor in the emergence and exacerbation of depression and anxiety, while neuroendocrine research confirms the association of these syndromes with dysregulation in a major stress response system, the hypothalamic-pituitary-adrenal (HPA) axis. However, the proposal that depression and anxiety symptoms and diagnoses are associated with problematic HPA responses to close relationship stress has not been directly tested. We examined 196 heterosexual dating couples' depression and anxiety symptoms and diagnoses, assessed with questionnaires and diagnostic interviews, in relation to cortisol responses to discussion of an unresolved relationship conflict. Participants provided seven salivary samples in anticipation of and directly following the discussion, and throughout an hour-long recovery period, which were assayed for cortisol. Multilevel models of the HPA response predicted by symptoms or diagnoses showed that women's depressive symptoms predicted attenuated cortisol levels, with a flatter response curve. In contrast, men's depression symptoms and women's anxiety symptoms and diagnoses predicted higher cortisol levels. These findings highlight the importance of examining sex differences in responses to interpersonal stressors for understanding HPA dysregulation in internalizing psychopathology. Copyright © 2016 Elsevier Ltd. All rights reserved.

SU-E-J-04: A Data-Driven, Response-Based, Multi-Criteria Decision Support System for Personalized Lung Radiation Treatment Planning

Energy Technology Data Exchange (ETDEWEB)

Luo, Y; McShan, D; Schipper, M; Matuszak, M; Ten Haken, R [University of Michigan, Ann Arbor, MI (United States); Kong, F [Georgia Regents University, Augusta, GA (Georgia)

2014-06-01

Purpose: To develop a decision support tool to predict a patient's potential overall survival (OS) and radiation induced toxicity (RIT) based on clinical factors and responses during the course of radiotherapy, and suggest appropriate radiation dose adjustments to improve therapeutic effect. Methods: Important relationships between a patient's basic information and their clinical features before and during the radiation treatment are identified from historical clinical data by using statistical learning and data mining approaches. During each treatment period, a data analysis (DA) module predicts radiotherapy features such as time to local progression (TTLP), time to distant metastases (TTDM), radiation toxicity to different organs, etc., under possible future treatment plans based on patient specifics or responses. An information fusion (IF) module estimates intervals for a patient's OS and the probabilities of RIT from a treatment plan by integrating the outcomes of module DA. A decision making (DM) module calculates “satisfaction” with the predicted radiation outcome based on trade-offs between OS and RIT, and finds the best treatment plan for the next time period via multi-criteria optimization. Results: Using physical and biological data from 130 lung cancer patients as our test bed, we were able to train and implement the 3 modules of our decision support tool. Examples demonstrate how it can help predict a new patient's potential OS and RIT with different radiation dose plans along with how these combinations change with dose, thus presenting a range of satisfaction/utility for use in individualized decision support. Conclusion: Although the decision support tool is currently developed from a small patient sample size, it shows the potential for the improvement of each patient's satisfaction in personalized radiation therapy. The radiation treatment outcome prediction and decision making model needs to be evaluated with more

Highly polygenic architecture of antidepressant treatment response: Comparative analysis of SSRI and NRI treatment in an animal model of depression.

Science.gov (United States)

Malki, Karim; Tosto, Maria Grazia; Mouriño-Talín, Héctor; Rodríguez-Lorenzo, Sabela; Pain, Oliver; Jumhaboy, Irfan; Liu, Tina; Parpas, Panos; Newman, Stuart; Malykh, Artem; Carboni, Lucia; Uher, Rudolf; McGuffin, Peter; Schalkwyk, Leonard C; Bryson, Kevin; Herbster, Mark

2017-04-01

Response to antidepressant (AD) treatment may be a more polygenic trait than previously hypothesized, with many genetic variants interacting in yet unclear ways. In this study we used methods that can automatically learn to detect patterns of statistical regularity from a sparsely distributed signal across hippocampal transcriptome measurements in a large-scale animal pharmacogenomic study to uncover genomic variations associated with AD. The study used four inbred mouse strains of both sexes, two drug treatments, and a control group (escitalopram, nortriptyline, and saline). Multi-class and binary classification using Machine Learning (ML) and regularization algorithms using iterative and univariate feature selection methods, including InfoGain, mRMR, ANOVA, and Chi Square, were used to uncover genomic markers associated with AD response. Relevant genes were selected based on Jaccard distance and carried forward for gene-network analysis. Linear association methods uncovered only one gene associated with drug treatment response. The implementation of ML algorithms, together with feature reduction methods, revealed a set of 204 genes associated with SSRI and 241 genes associated with NRI response. Although only 10% of genes overlapped across the two drugs, network analysis shows that both drugs modulated the CREB pathway, through different molecular mechanisms. Through careful implementation and optimisations, the algorithms detected a weak signal used to predict whether an animal was treated with nortriptyline (77%) or escitalopram (67%) on an independent testing set. The results from this study indicate that the molecular signature of AD treatment may include a much broader range of genomic markers than previously hypothesized, suggesting that response to medication may be as complex as the pathology. The search for biomarkers of antidepressant treatment response could therefore consider a higher number of genetic markers and their interactions. Through

Can current moisture responses predict soil CO2 efflux under altered precipitation regimes? A synthesis of manipulation experiments

DEFF Research Database (Denmark)

Vicca, S.; Bahn, M.; Estiarte, M.

2014-01-01

to fluctuations in soil temperature and soil water content can be used to predict SCE under altered rainfall patterns. Of the 58 experiments for which we gathered SCE data, 20 were discarded because either too few data were available or inconsistencies precluded their incorporation in the analyses. The 38...... remaining experiments were used to test the hypothesis that a model parameterized with data from the control plots (using soil temperature and water content as predictor variables) could adequately predict SCE measured in the manipulated treatment. Only for 7 of these 38 experiments was this hypothesis...... strongly on establishing response functions across a broader range of precipitation regimes and soil moisture conditions. Such experiments should make accurate measurements of water availability, should conduct high-frequency SCE measurements, and should consider both instantaneous responses...

Comparison of quantitative methods on FDG PET/CT for treatment response evaluation of metastatic colorectal cancer

Energy Technology Data Exchange (ETDEWEB)

Bang, Ji In; Paeng, Jin Chul; Park, So Hyun [Dept. of Nuclear Medicine, Seoul National University Hospital, Seoul (Korea, Republic of); and others

2017-06-15

FDG PET is effective in treatment response evaluation of cancer. However, there is no standard method for quantitative evaluation of FDG PET, particularly regarding cytostatic drugs. We compared various FDG PET quantitative methods in terms of response determination. A total of 39 refractory metastatic colorectal cancer patients who received a multikinase inhibitor treatment were included. Baseline and posttreatment FDG PET/CT scans were performed before and two cycles after treatment. Standardized uptake value (SUV) and total lesion glycolysis (TLG) values using various margin thresholds (30–70 % of maximum SUV with increment 10 %, twice mean SUV of blood pool, SUV 3.0, and SUV 4.0) were measured, with measurement target of the hottest lesion or a maximum of five hottest lesions. Treatment response by the PERCIST criteria was also determined. Predictive values of the PET indexes were evaluated in terms of the treatment response determined by the RECIST 1.1 criteria. The agreement rate was 38 % between response determined by the PERCIST and the RECIST criteria (κ = 0.381). When patients were classified into disease control group (PR, SD) and non-control group (PD) by the RECIST criteria, percent changes of TLG with various margin thresholds (particularly, 30–50 % of maximum SUV) exhibited significant differences between the two groups, and high diagnostic power for the response by the RECIST criteria. TLG-based criteria, which used a margin threshold of 50 % of maximum SUV, exhibited a high agreement with the RECIST criteria compared with the PERCIST criteria (κ = 0.606). In metastatic colorectal cancer, FDG PET/CT could be effective for treatment response evaluation by using TLG measured by margin thresholds of 30–50 % of maximum SUV. Further studies are warranted regarding the optimal cutoff values for this method.

Prediction of chemotherapeutic response of colorectal liver metastases with dynamic gadolinium-DTPA-enhanced MRI and localized 19F MRS pharmacokinetic studies of 5-fluorouracil.

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van Laarhoven, H W M; Klomp, D W J; Rijpkema, M; Kamm, Y L M; Wagener, D J Th; Barentsz, J O; Punt, C J A; Heerschap, A

2007-04-01

Systemic chemotherapy is effective in only a subset of patients with metastasized colorectal cancer. Therefore, early selection of patients who are most likely to benefit from chemotherapy is desirable. Response to treatment may be determined by the delivery of the drug to the tumor, retention of the drug in the tumor and by the amount of intracellular uptake, metabolic activation and catabolism, as well as other factors. The first aim of this study was to investigate the predictive value of DCE-MRI with the contrast agent Gd-DTPA for tumor response to first-line chemotherapy in patients with liver metastases of colorectal cancer. The second aim was to investigate the predictive value of 5-fluorouracil (FU) uptake, retention and catabolism as measured by localized (19)F MRS for tumor response to FU therapy. Since FU uptake, retention and metabolism may depend on tumor vascularization, the relationship between (19)F MRS and the DCE-MRI parameters k(ep), K(trans) and v(e) was also examined (1). In this study, 37 patients were included. The kinetic parameters of DCE-MRI, k(ep), K(trans) and v(e), before start of treatment did not predict tumor response after 2 months, suggesting that the delivery of chemotherapy by tumor vasculature is not a major factor determining response in first-line treatment. No evident correlations between (19)F MRS parameters and tumor response were found. This suggests that in liver metastases that are not selected on the basis of their tumor diameter, FU uptake and catabolism are not limiting factors for response. The transfer constant K(trans), as measured by DCE-MRI before start of treatment, was negatively correlated with FU half-life in the liver metastases, which suggests that, in metastases with a larger tumor blood flow or permeability surface area product, FU is rapidly washed out from the tumor. c 2006 John Wiley & Sons, Ltd.

Predictors and moderators of response to enhanced cognitive behaviour therapy and interpersonal psychotherapy for the treatment of eating disorders.

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Cooper, Zafra; Allen, Elizabeth; Bailey-Straebler, Suzanne; Basden, Shawnee; Murphy, Rebecca; O'Connor, Marianne E; Fairburn, Christopher G

2016-09-01

Consistent predictors, and more especially moderators, of response to psychological treatments for eating disorders have not been identified. The present exploratory study examined predictors and moderators of outcome in adult patients who took part in a randomised clinical trial comparing two leading treatments for these disorders, enhanced cognitive behavioural therapy (CBT-E) and interpersonal psychotherapy (IPT). Four potentially important findings emerged. Firstly, patients with a longer duration of disorder were less likely to benefit from either treatment. Second, across the two treatments the presence, at baseline, of higher levels of over-evaluation of the importance of shape predicted a less good treatment outcome. Third DSM-IV diagnosis did not predict treatment outcome. Fourth, with the exception of patients with baseline low self-esteem who achieved a better outcome with CBT-E, it was generally not possible to identify a subgroup of patients who would differentially benefit from one or other treatment. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study.

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Nie, Zhi; Vairavan, Srinivasan; Narayan, Vaibhav A; Ye, Jieping; Li, Qingqin S

2018-01-01

Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) care. We extended the work in predictive modeling of treatment resistant depression (TRD) via partition of the data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) cohort into a training and a testing dataset. We also included data from a small yet completely independent cohort RIS-INT-93 as an external test dataset. We used features from enrollment and level 1 treatment (up to week 2 response only) of STAR*D to explore the feature space comprehensively and applied machine learning methods to model TRD outcome at level 2. For TRD defined using QIDS-C16 remission criteria, multiple machine learning models were internally cross-validated in the STAR*D training dataset and externally validated in both the STAR*D testing dataset and RIS-INT-93 independent dataset with an area under the receiver operating characteristic curve (AUC) of 0.70-0.78 and 0.72-0.77, respectively. The upper bound for the AUC achievable with the full set of features could be as high as 0.78 in the STAR*D testing dataset. Model developed using top 30 features identified using feature selection technique (k-means clustering followed by χ2 test) achieved an AUC of 0.77 in the STAR*D testing dataset. In addition, the model developed using overlapping features between STAR*D and RIS-INT-93, achieved an AUC of > 0.70 in both the STAR*D testing and RIS-INT-93 datasets. Among all the features explored in STAR*D and RIS-INT-93 datasets, the most important feature was early or initial treatment response or symptom severity at week 2. These results indicate that prediction of TRD prior to undergoing a second round of antidepressant treatment could be feasible even in the absence of biomarker data.

Predictive value of lidocaine for treatment success of oxcarbazepine in patients with neuropathic pain syndrome.

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Schipper, Sivan; Gantenbein, Andreas R; Maurer, Konrad; Alon, Eli; Sándor, Peter S

2013-06-01

Pharmacotherapy in patients with neuropathic pain syndromes (NPS) can be associated with long periods of trial and error before reaching satisfactory analgesia. The aim of this study was to investigate whether a short intravenous (i.v.) infusion of lidocaine may have a predictive value for the efficacy of oxcarbazepine. In total, 16 consecutive patients with NPS were studied in a prospective, uncontrolled, open-label study design. Each patient received i.v. lidocaine (5 mg/kg) within 30 min followed by a long-term oral oxcarbazepine treatment (900-1,500 mg/day). During an observation period of 28 days, treatment response was documented by a questionnaire including the average daily pain score documented on a numeric rating scale (NRS). A total of 6 out of 16 patients (38%) were lidocaine responders (defined as pain reduction >50% during the infusion), and 4 of 16 (25%) were oxcarbazepine responders. In total, 6 out of 16 participants (38%) discontinued oxcarbazepine treatment due to side effects. In an interim analysis predictive value of the lidocaine infusion was low with a Kendall's tau correlation coefficient of 0.29 and coefficient of determination R(2) of 0.119 (95% confidence interval -0.29 to 0.72). As a consequence of this low correlation, the study was discontinued for ethical reasons. In conclusion, lidocaine infusion has a low predictive value for effectiveness of oxcarbazepine-if at all.

The value of 18F-FDG PET before and after induction chemotherapy for the early prediction of a poor pathologic response to subsequent preoperative chemoradiotherapy in oesophageal adenocarcinoma

International Nuclear Information System (INIS)

Rossum, Peter S.N. van; Fried, David V.; Zhang, Lifei; Court, Laurence E.; Hofstetter, Wayne L.; Ho, Linus; Meijer, Gert J.; Carter, Brett W.; Lin, Steven H.

2017-01-01

The purpose of our study was to determine the value of 18 F-FDG PET before and after induction chemotherapy in patients with oesophageal adenocarcinoma for the early prediction of a poor pathologic response to subsequent preoperative chemoradiotherapy (CRT). In 70 consecutive patients receiving a three-step treatment strategy of induction chemotherapy and preoperative chemoradiotherapy for oesophageal adenocarcinoma, 18 F-FDG PET scans were performed before and after induction chemotherapy (before preoperative CRT). SUV max , SUV mean , metabolic tumour volume (MTV), and total lesion glycolysis (TLG) were determined at these two time points. The predictive potential of (the change in) these parameters for a poor pathologic response, progression-free survival (PFS) and overall survival (OS) was assessed. A poor pathologic response after induction chemotherapy and preoperative CRT was found in 27 patients (39 %). Patients with a poor pathologic response experienced less of a reduction in TLG after induction chemotherapy (p < 0.01). The change in TLG was predictive for a poor pathologic response at a threshold of -26 % (sensitivity 67 %, specificity 84 %, accuracy 77 %, PPV 72 %, NPV 80 %), yielding an area-under-the-curve of 0.74 in ROC analysis. Also, patients with a decrease in TLG lower than 26 % had a significantly worse PFS (p = 0.02), but not OS (p = 0.18). 18 F-FDG PET appears useful to predict a poor pathologic response as well as PFS early after induction chemotherapy in patients with oesophageal adenocarcinoma undergoing a three-step treatment strategy. As such, the early 18 F-FDG PET response after induction chemotherapy could aid in individualizing treatment by modification or withdrawal of subsequent preoperative CRT in poor responders. (orig.)

Mid-Treatment Sleep Duration Predicts Clinically Significant Knee Osteoarthritis Pain reduction at 6 months: Effects From a Behavioral Sleep Medicine Clinical Trial.

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Salwen, Jessica K; Smith, Michael T; Finan, Patrick H

2017-02-01

To determine the relative influence of sleep continuity (sleep efficiency, sleep onset latency, total sleep time [TST], and wake after sleep onset) on clinical pain outcomes within a trial of cognitive behavioral therapy for insomnia (CBT-I) for patients with comorbid knee osteoarthritis and insomnia. Secondary analyses were performed on data from 74 patients with comorbid insomnia and knee osteoarthritis who completed a randomized clinical trial of 8-session multicomponent CBT-I versus an active behavioral desensitization control condition (BD), including a 6-month follow-up assessment. Data used herein include daily diaries of sleep parameters, actigraphy data, and self-report questionnaires administered at specific time points. Patients who reported at least 30% improvement in self-reported pain from baseline to 6-month follow-up were considered responders (N = 31). Pain responders and nonresponders did not differ significantly at baseline across any sleep continuity measures. At mid-treatment, only TST predicted pain response via t tests and logistic regression, whereas other measures of sleep continuity were nonsignificant. Recursive partitioning analyses identified a minimum cut-point of 382 min of TST achieved at mid-treatment in order to best predict pain improvements 6-month posttreatment. Actigraphy results followed the same pattern as daily diary-based results. Clinically significant pain reductions in response to both CBT-I and BD were optimally predicted by achieving approximately 6.5 hr sleep duration by mid-treatment. Thus, tailoring interventions to increase TST early in treatment may be an effective strategy to promote long-term pain reductions. More comprehensive research on components of behavioral sleep medicine treatments that contribute to pain response is warranted. © Sleep Research Society 2016. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

Diffusion-weighted imaging for pretreatment evaluation and prediction of treatment effect in patients undergoing CT-guided injection for lumbar disc herniation

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Niu, Xiang Ke [Dept. of Radiology, Affiliated Hospital of Chengdu University, Chengdu (China); Bhetuwal, Anup; Yang, Han Feng [Schuan Key Laboratory of Medical Imaging and Dept. of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong (China)

2015-08-15

To determine whether a change in apparent diffusion coefficient (ADC) value could predict early response to CT-guided Oxygen-Ozone (O{sub 2}-O{sub 3}) injection therapy in patients with unilateral mono-radiculopathy due to lumbar disc herniation. A total of 52 patients with unilateral mono-radiculopathy received a single intradiscal (3 mL) and periganglionic (5 mL) injection of an O{sub 2}-O{sub 3} mixture. An ADC index of the involved side to the intact side was calculated using the following formula: pre-treatment ADC index = ([ADC involved side - ADC intact side] / ADC intact side) x 100. We analyzed the relationship between the pre-treatment Oswestry Disability Index (ODI) and the ADC index. In addition, the correlation between ODI recovery ratio and ADC index was investigated. The sensitivity and specificity of the ADC index for predicting response in O{sub 2}-O{sub 3} therapy was determined. Oswestry Disability Index and the ADC index was not significantly correlated (r = -0.125, p = 0.093). The ADC index and ODI recovery ratio was significantly correlated (r = 0.819, p < 0.001). When using 7.10 as the cut-off value, the ADC index obtained a sensitivity of 86.3% and a specificity of 82.9% for predicting successful response to therapy around the first month of follow-up. This preliminary study demonstrates that the patients with decreased ADC index tend to show poor improvement of clinical symptoms. The ADC index may be a useful indicator to predict early response to CT-guided O{sub 2}-O{sub 3} injection therapy in patients with unilateral mono-radiculopathy due to lumbar disc herniation.

Diffusion-weighted imaging for pretreatment evaluation and prediction of treatment effect in patients undergoing CT-guided injection for lumbar disc herniation

International Nuclear Information System (INIS)

Niu, Xiang Ke; Bhetuwal, Anup; Yang, Han Feng

2015-01-01

To determine whether a change in apparent diffusion coefficient (ADC) value could predict early response to CT-guided Oxygen-Ozone (O 2 -O 3 ) injection therapy in patients with unilateral mono-radiculopathy due to lumbar disc herniation. A total of 52 patients with unilateral mono-radiculopathy received a single intradiscal (3 mL) and periganglionic (5 mL) injection of an O 2 -O 3 mixture. An ADC index of the involved side to the intact side was calculated using the following formula: pre-treatment ADC index = ([ADC involved side - ADC intact side] / ADC intact side) x 100. We analyzed the relationship between the pre-treatment Oswestry Disability Index (ODI) and the ADC index. In addition, the correlation between ODI recovery ratio and ADC index was investigated. The sensitivity and specificity of the ADC index for predicting response in O 2 -O 3 therapy was determined. Oswestry Disability Index and the ADC index was not significantly correlated (r = -0.125, p = 0.093). The ADC index and ODI recovery ratio was significantly correlated (r = 0.819, p < 0.001). When using 7.10 as the cut-off value, the ADC index obtained a sensitivity of 86.3% and a specificity of 82.9% for predicting successful response to therapy around the first month of follow-up. This preliminary study demonstrates that the patients with decreased ADC index tend to show poor improvement of clinical symptoms. The ADC index may be a useful indicator to predict early response to CT-guided O 2 -O 3 injection therapy in patients with unilateral mono-radiculopathy due to lumbar disc herniation

Genotype-based ancestral background consistently predicts efficacy and side effects across treatments in CATIE and STAR*D.

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Daniel E Adkins

Full Text Available Only a subset of patients will typically respond to any given prescribed drug. The time it takes clinicians to declare a treatment ineffective leaves the patient in an impaired state and at unnecessary risk for adverse drug effects. Thus, diagnostic tests robustly predicting the most effective and safe medication for each patient prior to starting pharmacotherapy would have tremendous clinical value. In this article, we evaluated the use of genetic markers to estimate ancestry as a predictive component of such diagnostic tests. We first estimated each patient's unique mosaic of ancestral backgrounds using genome-wide SNP data collected in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE (n = 765 and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D (n = 1892. Next, we performed multiple regression analyses to estimate the predictive power of these ancestral dimensions. For 136/89 treatment-outcome combinations tested in CATIE/STAR*D, results indicated 1.67/1.84 times higher median test statistics than expected under the null hypothesis assuming no predictive power (p<0.01, both samples. Thus, ancestry showed robust and pervasive correlations with drug efficacy and side effects in both CATIE and STAR*D. Comparison of the marginal predictive power of MDS ancestral dimensions and self-reported race indicated significant improvements to model fit with the inclusion of MDS dimensions, but mixed evidence for self-reported race. Knowledge of each patient's unique mosaic of ancestral backgrounds provides a potent immediate starting point for developing algorithms identifying the most effective and safe medication for a wide variety of drug-treatment response combinations. As relatively few new psychiatric drugs are currently under development, such personalized medicine offers a promising approach toward optimizing pharmacotherapy for psychiatric conditions.