MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study.

ABSTRACT:

Objectives

To build a machine learning (ML) model to detect extraprostatic extension (EPE) of prostate cancer (PCa), based on radiomics features extracted from prostate MRI index lesions.

Methods

Consecutive MRI exams of patients undergoing radical prostatectomy for PCa were retrospectively collected from three institutions. Axial T2-weighted and apparent diffusion coefficient map images were annotated to obtain index lesion volumes of interest for radiomics feature extraction. Data from one institution was used for training, feature selection (using reproducibility, variance and pairwise correlation analyses, and a correlation-based subset evaluator), and tuning of a support vector machine (SVM) algorithm, with stratified 10-fold cross-validation. The model was tested on th

SUBMITTER: Cuocolo R 

PROVIDER: S-EPMC8452573 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature