Bayesian feature selection for radiomics using reliability metrics.

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 studi

SUBMITTER: Shoemaker K 

PROVIDER: S-EPMC10030957 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature