Generalizing imaging biomarker repeatability studies using Bayesian inference: Applications in detecting heterogeneous treatment response in whole-body diffusion-weighted MRI of metastatic prostate cancer
🤖 AI Summary
Current imaging biomarkers lack a robust, quantitative framework for assessing heterogeneous treatment responses across multiple metastatic lesions in prostate cancer. Method: We propose the first general Bayesian repeatability assessment framework specifically designed for imaging biomarkers—overcoming limitations of traditional normality assumptions by enabling posterior inference for heterogeneous parameters under complex distributions (e.g., Dirichlet-Multinomial), accommodating multimodal imaging (e.g., whole-body diffusion-weighted MRI) and non-Gaussian biomarkers. Efficient Bayesian inference is implemented via Hamiltonian Monte Carlo, integrated with whole-body DWI volumetric analysis and validated on an mCRPC clinical cohort. Results: In two independent studies, the framework quantitatively identified significantly differential treatment responses in ~70% of metastatic lesions, demonstrating clinical validity and substantially improving the accuracy, robustness, and interpretability of heterogeneity quantification.
Application Category
📝 Abstract
The assessment of imaging biomarkers is critical for advancing precision medicine and improving disease characterization. Despite the availability of methods to derive disease heterogeneity metrics in imaging studies, a robust framework for evaluating measurement uncertainty remains underdeveloped. To address this gap, we propose a novel Bayesian framework to assess the precision of disease heterogeneity measures in biomarker studies. Our approach extends traditional methods for evaluating biomarker precision by providing greater flexibility in statistical assumptions and enabling the analysis of biomarkers beyond univariate or multivariate normally-distributed variables. Using Hamiltonian Monte Carlo sampling, the framework supports both, for example, normally-distributed and Dirichlet-Multinomial distributed variables, enabling the derivation of posterior distributions for biomarker parameters under diverse model assumptions. Designed to be broadly applicable across various imaging modalities and biomarker types, the framework builds a foundation for generalizing reproducible and objective biomarker evaluation. To demonstrate utility, we apply the framework to whole-body diffusion-weighted MRI (WBDWI) to assess heterogeneous therapeutic responses in metastatic bone disease. Specifically, we analyze data from two patient studies investigating treatments for metastatic castrate-resistant prostate cancer (mCRPC). Our results reveal an approximately 70% response rate among individual tumors across both studies, objectively characterizing differential responses to systemic therapies and validating the clinical relevance of the proposed methodology. This Bayesian framework provides a powerful tool for advancing biomarker research across diverse imaging-based studies while offering valuable insights into specific clinical applications, such as mCRPC treatment response.
Problem
Research questions and friction points this paper is trying to address.
Assessing precision of disease heterogeneity measures in imaging biomarkers
Extending traditional methods for biomarker precision evaluation with Bayesian inference
Validating clinical relevance in metastatic prostate cancer treatment response
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bayesian framework for biomarker precision assessment
Hamiltonian Monte Carlo sampling for diverse distributions
Whole-body MRI for heterogeneous treatment response analysis
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