从常规组织学切片深度学习提高前列腺癌生物化学复发的风险分层
Deep Learning From Routine Histology Improves Risk Stratification for Biochemical Recurrence in Prostate Cancer
AI总结：
本文提出基于深度学习的生物标志物，通过H&E染色的整张前列腺切除术切片预测生物化学复发风险，提升风险分层准确性，增强临床实用性。
摘要：
准确预测根治性前列腺切除术后生物化学复发（BCR）对于指导辅助治疗和监测决策至关重要。然而，现有的临床病理风险模型将复杂的形态简化为相对粗糙的描述，使得常规组织病理学中蕴含的大量预后信息未被充分利用。我们提出了一种基于深度学习的生物标志物，能够从H&E染色的整张前列腺切除术切片中直接预测连续的、患者特异性的BCR风险。该模型在四个独立的国际队列中进行训练和评估，展示了在不同机构和患者群体中的稳健泛化能力。当与CAPRA-S临床风险评分结合时，深度学习风险评分在提高BCR的区分能力方面表现一致，使一致性指数从0.725-0.772提升到0.749-0.788。为了支持临床可解释性，基于结果的分析揭示了与复发风险相关的细微组织形态学模式，这些模式未被传统临床病理风险评分所捕捉。这项多队列研究证明，将深度学习应用于常规前列腺组织病理学可以提供可重复且具有临床应用价值的生物标志物，从而增强术后风险分层，有望支持真实世界临床环境中的前列腺癌个性化管理。
英文摘要：
Accurate prediction of biochemical recurrence (BCR) after radical prostatectomy is critical for guiding adjuvant treatment and surveillance decisions in prostate cancer. However, existing clinicopathological risk models reduce complex morphology to relatively coarse descriptors, leaving substantial prognostic information embedded in routine histopathology underexplored. We present a deep learning-based biomarker that predicts continuous, patient-specific risk of BCR directly from H&E-stained whole-slide prostatectomy specimens. Trained end-to-end on time-to-event outcomes and evaluated across four independent international cohorts, our model demonstrates robust generalization across institutions and patient populations. When integrated with the CAPRA-S clinical risk score, the deep learning risk score consistently improved discrimination for BCR, increasing concordance indices from 0.725-0.772 to 0.749-0.788 across cohorts. To support clinical interpretability, outcome-grounded analyses revealed subtle histomorphological patterns associated with recurrence risk that are not captured by conventional clinicopathological risk scores. This multicohort study demonstrates that deep learning applied to routine prostate histopathology can deliver reproducible and clinically generalizable biomarkers that augment postoperative risk stratification, with potential to support personalized management of prostate cancer in real-world clinical settings.
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