Improving Clinically Significant Prostate Cancer Detection with a Multimodal Machine Learning Approach: A Large-Scale Multicenter Study.
Journal:
Radiology. Imaging cancer
Published Date:
Sep 1, 2025
Abstract
Purpose To develop and prospectively validate a clinical and radiologic model to predict clinically significant prostate cancer (csPCa) using biparametric MRI (bpMRI). Materials and Methods Retrospective data (acquired before March 31, 2022) from 12 medical centers were collected. Radiomic features were extracted from the whole prostate gland using segmentations generated by an automatic deep learning algorithm. A model incorporating bpMRI radiomics, age, prostate-specific antigens, the Prostate Imaging Reporting and Data System (PI-RADS), and the prostate zone lesion location was trained. A retrospective validation set and prospective data (acquired after March 31, 2022) were used to compare PI-RADS scoring (area under the receiver operating characteristic curve [AUC] and specificity at PI-RADS >3). Sensitivity analyses for sequence (T2-weighted, apparent diffusion coefficient, diffusion-weighted imaging) and scanner vendor (GE, Philips, Siemens) were performed, in addition to fairness analyses for relevant categories. Results The retrospective dataset for model development included 7157 male patients (mean age, 64.78 years; 3342 [46.7%] with csPCa), and the prospective dataset for model validation included 1629 patients (mean age, 66.19 years; 592 [36.3%] with csPCa). The multimodal model outperformed PI-RADS in the retrospective (AUC, 0.88 vs 0.80, = .005; specificity of 71% vs 58%, = .002) and prospective validation sets (AUC, 0.91 vs 0.85, < .001; specificity of 77% vs 66%, < .001), leading to 22.7% fewer biopsies compared with PI-RADS. Sensitivity analyses showed the importance of multiple sequences and vendors in achieving model generalization, as using specific sequences or vendors alone led to worse performance. Fairness analysis showed generalizability across different categories but highlighted increased sensitivity with higher PI-RADS and reduced performance in one medical center. Conclusion A multimodal model provided a temporally generalizable predictor of csPCa that outperformed PI-RADS. Algorithm Development, Machine Learning, Model Validation, Model Training, Genital/Reproductive, Neoplasms-Primary, Oncology, Comparative Studies, Technology Assessment © RSNA, 2025.
