Organizations: College of Artificial Intelligence, Nankai University, Tianjin 300350, China · Tianjin First Central Hospital, Tianjin 300382, China · School of Electronics and Information Engineering, Tiangong University, Tianjin 300387, China · School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China · North China Digital Health Technology Co., Ltd., Jinan 250117, China · School of Computing and Information Systems, Singapore Management University, Singapore 178902, Singapore
Prostate cancer diagnosis with multiparametric MRI (mpMRI) is commonly based on PI-RADS assessment or binary classification, which suffer from subjectivity and fail to capture clinically relevant pathological heterogeneity. To address this limitation, we construct a Prostate Cancer Histopathology Spectrum Dataset (PCa-HSD) and formulate a clinically meaningful four-class classification task, addressing the underrepresentation of benign lesions that are easily confounded with prostate cancer in existing datasets. We propose Language-guided Segmentation-assisted Diagnostic Transformer model (LSDT), which leverages zero-shot segmentation to provide anatomical priors and performs effective multi-modal slice fusion for classification. Our proposed method consistently improves accuracy across backbones, achieving the best average accuracy of 0.633 and JointRecall of 0.768 in five-fold cross-validation on a cohort of 344 patients. These results demonstrate that integrating pathology supervision and anatomical priors significantly enhances fine-grained prostate MRI classification and provides a more clinically relevant paradigm for risk stratification. Code will be made publicly available in a future revision.