Abstract Objectives Accurate pre-biopsy or preoperative diagnosis and risk stratification of prostate cancer remain challenging. This study aimed to develop an automated framework for prostate and suspicious lesion segmentation, feature extraction, and multiclass classification of prostate cancer. Materials and methods This study enrolled 2543 patients with suspected clinically significant prostate cancer (csPCa). An nnUNet was used for prostate segmentation, followed by suspected lesion identification and feature extraction. Unimodal and multimodal deep learning models were trained to classify patients into non-csPCa (benign/ISUP 1), mid-risk csPCa (ISUP 2–3), and high-risk csPCa (ISUP 4–5). Subsequently, we extracted prostate radiomics, lesion radiomics, and learned features from the unimodal DL model. Radiomics features, learned features from the unimodal deep learning model, and clinical variables were integrated to develop machine-learning classifiers. The best-performing model was compared with conventional clinical variables and evaluated across subgroups defined by PSA levels and PI-RADS 4. Results The nnUNet achieved scores of 0.971 in internal validation and 0.934 in external testing. The optimal machine learning model, which integrated all features, achieved a macro-AUC of 0.87 0.84–0.90 across both internal and external evaluations. Further comparisons indicate it outperforms traditional metrics such as total PSA, PSAD, and PI-RADS. Additionally, it exhibits strong robustness in most subgroup analyses, consistently surpassing conventional variables. Conclusion We developed an automated bpMRI-based framework that segments the prostate and suspicious lesions and enables multiclass risk stratification of prostate cancer by integrating imaging and clinical features. The proposed approach demonstrated strong performance and robustness, outperforming conventional clinical variables. Key Points Question accurate pre-biopsy stratification of prostate cancer is essential for clinical decision-making. Current studies predominantly focus on binary classification of csPCa, and multi-class classification remains challenging. Findings the comprehensive framework achieved an area under the curve of 0.87 in both internal and external tests, exceeding clinical variables and demonstrating robustness. Critical relevance statement this study presents a robust multi-class classification framework based on biparametric MRI and machine learning to improve pre-biopsy prostate cancer stratification, outperform conventional metrics, and provide a valuable tool for risk stratification and clinical decision-making.
Zheng et al. (Mon,) studied this question.