Quantification of intratumoral heterogeneity using habitat-based MRI radiomics for predicting high-Gleason scores and castration-resistant PCa: retrospective study

作者：Cheng-Feng Zhai, Xin Yang, Xuan Qi, Hongkai Yang, Yongsheng He · 发表于：BMC Cancer · 年份：2025 · DOI：10.1186/s12885-025-15300-8 · 被引用次数：5 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Cardiac Imaging and Diagnostics、Medical Imaging Techniques and Applications

PURPOSE: Prostate cancer (PCa) with high-Gleason Scores (GS) tends to have aggressive clinicopathological characteristics and a poor prognosis. we aimed to develop and validate an MRI-based habitat imaging (HI) model for the preoperative prediction of high-GS and castration-resistant prostate cancer (CRPC). METHODS AND MATERIALS: We collected T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images from 264 prostate cancer patients.Task 1 involved distinguishing between high and low GS PCa, followed by Task 2 which aimed to predict the development of CRPC in high-GS PCa cases. Task 1 extracted whole-tumor radiomic features and tumor microenvironment heterogeneity-based radiomic features from MRI images of 264 patients to construct a radiomic signature and an intratumoral heterogeneity (ITH) signature. Multivariate logistic regression analysis was employed to identify significant independent clinical predictive variables, which were then integrated with the radiomic and ITH signatures to develop a combined model. In Task 2, whole-tumor and tumor microenvironment heterogeneity-based radiomic features were extracted from MRI images of 142 high-GS patients to build an intratumoral heterogeneity signature. The discriminatory performance of these features was evaluated through receiver operating characteristic (ROC) curve analysis, while subsequent decision curve analysis (DCA) was conducted to assess the clinical utility value o...