Document Detail


A Bayesian hierarchical model for the analysis of a longitudinal dynamic contrast-enhanced MRI oncology study.
MedLine Citation:
PMID:  19097226     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Imaging in clinical oncology trials provides a wealth of information that contributes to the drug development process, especially in early phase studies. This article focuses on kinetic modeling in DCE-MRI, inspired by mixed-effects models that are frequently used in the analysis of clinical trials. Instead of summarizing each scanning session as a single kinetic parameter--such as median k(trans) across all voxels in the tumor ROI-we propose to analyze all voxel time courses from all scans and across all subjects simultaneously in a single model. The kinetic parameters from the usual nonlinear regression model are decomposed into unique components associated with factors from the longitudinal study; e.g., treatment, patient, and voxel effects. A Bayesian hierarchical model provides the framework to construct a data model, a parameter model, as well as prior distributions. The posterior distribution of the kinetic parameters is estimated using Markov chain Monte Carlo (MCMC) methods. Hypothesis testing at the study level for an overall treatment effect is straightforward and the patient- and voxel-level parameters capture random effects that provide additional information at various levels of resolution to allow a thorough evaluation of the clinical trial. The proposed method is validated with a breast cancer study, where the subjects were imaged before and after two cycles of chemotherapy, demonstrating the clinical potential of this method to longitudinal oncology studies.
Authors:
Volker J Schmid; Brandon Whitcher; Anwar R Padhani; N Jane Taylor; Guang-Zhong Yang
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Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine     Volume:  61     ISSN:  1522-2594     ISO Abbreviation:  Magn Reson Med     Publication Date:  2009 Jan 
Date Detail:
Created Date:  2008-12-24     Completed Date:  2009-02-27     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8505245     Medline TA:  Magn Reson Med     Country:  United States    
Other Details:
Languages:  eng     Pagination:  163-74     Citation Subset:  IM    
Affiliation:
Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, United Kingdom.
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Antineoplastic Combined Chemotherapy Protocols / administration & dosage*
Artificial Intelligence
Bayes Theorem
Breast Neoplasms / diagnosis*,  drug therapy*
Cluster Analysis
Computer Simulation
Contrast Media
Female
Humans
Image Enhancement / methods*
Image Interpretation, Computer-Assisted / methods*
Longitudinal Studies
Magnetic Resonance Imaging / methods*
Medical Oncology / methods
Models, Biological
Models, Statistical
Pattern Recognition, Automated / methods*
Prognosis
Reproducibility of Results
Sensitivity and Specificity
Treatment Outcome
Chemical
Reg. No./Substance:
0/Contrast Media

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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