Performance variability of radiomics machine learning models for the detection of clinically significant prostate cancer in heterogeneous MRI datasets.

ABSTRACT:

Background

Radiomics promises to enhance the discriminative performance for clinically significant prostate cancer (csPCa), but still lacks validation in real-life scenarios. This study investigates the classification performance and robustness of machine learning radiomics models in heterogeneous MRI datasets to characterize suspicious prostate lesions for non-invasive prediction of prostate cancer (PCa) aggressiveness compared to conventional imaging biomarkers.

Methods

A total of 142 patients with clinical suspicion of PCa underwent 1.5T or 3T biparametric MRI (7 scanner types, 14 institutions) and exhibited suspicious lesions [prostate Imaging Reporting and Data System (PI-RADS) score ≥3] in peripheral or transitional zones. Whole-gland and index-lesion segmentations wer

SUBMITTER: Gresser E 

PROVIDER: S-EPMC9622454 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature