Histopathological Spectrum-Guided Prostate Stratification via Segmentation-Assisted Diagnostic Transformer

2026-07-19

AI Summary

Current MRI-based diagnosis of prostate cancer relies on subjective PI-RADS scoring or coarse binary classification, which fails to capture pathological heterogeneity and is hindered by the scarcity of benign samples. To address these limitations, this work introduces PCa-HSD, the first histopathological spectrum dataset for prostate cancer, and formulates a fine-grained four-class risk stratification task. The authors propose a Language-guided Segmentation-assisted Diagnostic Transformer (LSDT) that incorporates anatomical priors via zero-shot segmentation and fuses multimodal MRI slices. Evaluated on 344 patients using five-fold cross-validation, the model achieves an average accuracy of 0.633 and a JointRecall of 0.768, significantly outperforming baseline methods. This approach effectively mitigates the challenge of insufficient benign samples and enhances clinical relevance.

Abstract

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.

Problem

• prostate cancer
• histopathological heterogeneity
• mpMRI diagnosis
• benign lesion underrepresentation
• clinical risk stratification

Innovation

• Histopathological Spectrum
• Segmentation-Assisted Transformer
• Zero-shot Segmentation
• Multi-modal Fusion
• Prostate Cancer Stratification