Biparametric MRI ‐Based Habitat Analysis Integrated With Deep Learning for Predicting Clinically Significant Prostate Cancer in PI ‐ RADS Category 3 Lesions

作者：Shuitang Deng, Jinwen Hu, Hui Wang, Xiaoyu Han, Weiqun Ao · 发表于：Journal of Magnetic Resonance Imaging · 年份：2025 · DOI：10.1002/jmri.70205 · 被引用次数：5 · 研究领域：Prostate Cancer Diagnosis and Treatment、MRI in cancer diagnosis、Radiomics and Machine Learning in Medical Imaging

BACKGROUND: Detection of clinically significant prostate cancer (csPCa) within PI-RADS category 3 lesions remains a major diagnostic challenge. PURPOSE: To develop and validate a biparametric MRI (bpMRI)-based habitat analysis model integrating deep learning features for predicting csPCa in PI-RADS 3 lesions using dual-center data. STUDY TYPE: Retrospective. POPULATION: This study included 551 patients with MRI-identified PI-RADS category 3 lesions and histopathological confirmation. A total of 439 patients from Center 1 were randomly assigned to a training set (n = 328) and an internal validation (in-vad) set (n = 111), while an external validation (ex-vad) set (n = 112) was obtained from Center 2. FIELD STRENGTH/SEQUENCE: 3 T/1.5 T. T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences. ASSESSMENT: Lesions were manually segmented on preoperative T2WI and DWI, and tumor subregions were determined using k-means clustering. Deep learning features were obtained from each habitat subregion, and habitat-based models were built based on selected features. A habitat whole-tumor (Habitat W) model was subsequently derived by integrating all subregions. Recursive feature elimination (RFE) was applied to select the optimal predictors from the clinical and habitat-derived features; the clinical model was constructed using the selected clinical features, while the combined model incorporated all selected features. STATISTICAL TESTS: Student's t-test, Mann-Whitney U te...