基于组织形态学的AI模型，可精准预测结直肠癌五年生存率。
Morphology-Aware Prognostic model for Five-Year Survival Prediction in Colorectal Cancer from H&E Whole Slide Images
- 融合连续形态变异谱，捕捉肿瘤演化过程中的细微变化。
- 在424例患者数据上实现AUC 0.70、准确率68.37%的预测性能。
- 模型对性别和化疗方案不敏感，适合临床实用化部署。
结直肠癌（CRC）是全球第三大常见恶性肿瘤，预计2025年新增病例约15.4万例，死亡约5.4万例。当前计算病理学中的基础模型多采用任务无关方法，常忽略器官特异性的重要形态特征，而这些特征反映不同生物学过程，可能从根本上影响肿瘤行为、治疗反应和患者预后。本研究提出一种新型可解释人工智能模型PRISM（Prognostic Representation of Integrated Spatial Morphology），通过在每种形态中引入连续变异谱，刻画表型多样性，体现恶性转化是渐进式演化而非突变的本质。PRISM基于424例III期CRC患者的手术切除标本，共训练874万张组织图像。模型在五年总生存率预测中表现优异：AUC = 0.70 ± 0.04，准确率 = 68.37% ± 4.75%，风险比HR = 3.34（95% CI: 2.28–4.90，p < 0.0001），较现有CRC专用方法提升15%，较通用AI基础模型提升约23%准确率。模型具有性别无关鲁棒性（AUC差值0.02，准确率差值0.15%），在不同化疗方案（5FU/LV vs CPT-11/5FU/LV）间性能波动极小（准确率差值仅1.44%），复现了Alliance队列中两种疗法无生存差异的结果。
原文摘要 · Abstract (English)
Colorectal cancer (CRC) remains the third most prevalent malignancy globally, with approximately 154,000 new cases and 54,000 projected deaths anticipated for 2025. The recent advancement of foundation models in computational pathology has been largely propelled by task agnostic methodologies that can overlook organ-specific crucial morphological patterns that represent distinct biological processes that can fundamentally influence tumor behavior, therapeutic response, and patient outcomes. The aim of this study is to develop a novel, interpretable AI model, PRISM (Prognostic Representation of Integrated Spatial Morphology), that incorporates a continuous variability spectrum within each distinct morphology to characterize phenotypic diversity and reflecting the principle that malignant transformation occurs through incremental evolutionary processes rather than abrupt phenotypic shifts. PRISM is trained on 8.74 million histological images extracted from surgical resection specimens of 424 patients with stage III CRC. PRISM achieved superior prognostic performance for five-year OS (AUC = 0.70 +- 0.04; accuracy = 68.37% +- 4.75%; HR = 3.34, 95% CI = 2.28-4.90; p < 0.0001), outperforming existing CRC-specific methods by 15% and AI foundation models by ~23% accuracy. It showed sex-agnostic robustness (AUC delta = 0.02; accuracy delta = 0.15%) and stable performance across clinicopathological subgroups, with minimal accuracy fluctuation (delta = 1.44%) between 5FU/LV and CPT-11/5FU/LV regimens, replicating the Alliance cohort finding of no survival difference between treatments.
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