用深度学习从常规切片中量化前列腺癌间质变化，提升预后预测能力
Computational Mapping of Reactive Stroma in Prostate Cancer Yields Interpretable, Prognostic Biomarkers
- 基于H&E染色切片的深度学习框架，自动识别肿瘤间质特征
- 间质异常与收缩通路激活相关，能独立预测生化复发（c-index 0.80）
- 结果可解释且适用于不同数据集，优于病理医生人工判断
当前前列腺癌病理分级主要依赖腺体结构，忽视了肿瘤微环境。本文提出PROTAS深度学习框架，可量化常规苏木精-伊红（H&E）切片中的反应性间质（RS），并将其形态特征与潜在生物学机制关联。PROTAS定义的RS表现为核增大、胶原紊乱，并伴有收缩通路转录组富集。该方法在外部的前列腺、肺、结直肠和卵巢（PLCO）数据集中稳健检测RS，通过域对抗训练实现对诊断活检样本的泛化。与病理医生相比，PROTAS在RS检测上表现更优；空间间质特征可独立预测生化复发（c-index 0.80）。PROTAS捕捉到与肿瘤进展相关的细微间质表型，提供可解释、可扩展的风险分层生物标志物。
原文摘要 · Abstract (English)
Current histopathological grading of prostate cancer relies primarily on glandular architecture, largely overlooking the tumor microenvironment. Here, we present PROTAS, a deep learning framework that quantifies reactive stroma (RS) in routine hematoxylin and eosin (H&E) slides and links stromal morphology to underlying biology. PROTAS-defined RS is characterized by nuclear enlargement, collagen disorganization, and transcriptomic enrichment of contractile pathways. PROTAS detects RS robustly in the external Prostate, Lung, Colorectal, and Ovarian (PLCO) dataset and, using domain-adversarial training, generalizes to diagnostic biopsies. In head-to-head comparisons, PROTAS outperforms pathologists for RS detection, and spatial RS features predict biochemical recurrence independently of established prognostic variables (c-index 0.80). By capturing subtle stromal phenotypes associated with tumor progression, PROTAS provides an interpretable, scalable biomarker to refine risk stratification.
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