用病理谱指导前列腺癌分层，提升MRI诊断精准度。
Histopathological Spectrum-Guided Prostate Stratification via Segmentation-Assisted Diagnostic Transformer
- 结合零样本分割提供解剖先验，融合多模态影像进行分类
- 在344例患者上达到0.633平均准确率与0.768联合召回率
- 适合需要精细化风险分层的临床医生和医学AI研究者
基于多参数MRI（mpMRI）的前列腺癌诊断通常依赖PI-RADS评分或二分类，存在主观性强且无法捕捉临床相关病理异质性的问题。为解决此问题，我们构建了前列腺癌病理谱数据集（PCa-HSD），提出具有临床意义的四类分类任务，弥补了良性病变在现有数据集中易被误判为癌症的不足。我们提出语言引导的分割辅助诊断变换器模型（LSDT），利用零样本分割提供解剖先验，并实现有效的多模态切片融合分类。该方法在不同骨干网络下均显著提升性能，在344名患者的五折交叉验证中，平均准确率达到0.633，联合召回率为0.768。结果表明，整合病理监督与解剖先验能显著增强细粒度前列腺MRI分类能力，为风险分层提供更符合临床需求的新范式。代码将在后续版本公开。
原文摘要 · Abstract (English)
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.
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