Bayesian Feature Selection for Radiomics Using Reliability Metrics

Authors

Katherine Shoemaker
Rachel Ger
Laurence E Court
Hugo Aerts
Marina Vannucci
Christine B Peterson

Publication Date

1-1-2023

Journal

Frontiers in Genetics

Abstract

Introduction: Imaging of tumors is a standard step in diagnosing cancer and making subsequent treatment decisions. The field of radiomics aims to develop imaging based biomarkers using methods rooted in artificial intelligence applied to medical imaging. However, a challenging aspect of developing predictive models for clinical use is that many quantitative features derived from image data exhibit instability or lack of reproducibility across different imaging systems or image-processing pipelines. Methods: To address this challenge, we propose a Bayesian sparse modeling approach for image classification based on radiomic features, where the inclusion of more reliable features is favored via a probit prior formulation. Results: We verify through simulation studies that this approach can improve feature selection and prediction given correct prior information. Finally, we illustrate the method with an application to the classification of head and neck cancer patients by human papillomavirus status, using as our prior information a reliability metric quantifying feature stability across different imaging systems.

Keywords

Bayesian modeling, classification, quantitative imaging, probit prior, radiomics, variable selection

Comments

Supplementary Materials

Data Availability Statement

PMID: 36968604