用活检切片训练AI模型，预测前列腺癌术后复发风险。
AI-based Prediction of Biochemical Recurrence from Biopsy and Prostatectomy Samples
- 基于基础模型与注意力机制学习病理图像特征
- 跨队列验证5年时间依赖AUC达0.64至0.70
- 融合临床数据可显著提升风险分层能力
根治性前列腺切除术后的生化复发（BCR）是侵袭性前列腺癌的标志，但现有预后工具精度不足。我们在STHLM3队列（n=676）的诊断性前列腺活检切片上训练了一种基于AI的模型，采用基础模型和基于注意力的多实例学习方法预测患者个体化的BCR风险。通过三个外部前列腺切除术队列（LEOPARD, n=508；CHIMERA, n=95；TCGA-PRAD, n=379）评估泛化能力，图像模型在5年时间依赖AUC分别为0.64、0.70和0.70。整合临床变量增强了预测价值，并实现统计学显著的风险分层。相比指南推荐的CAPRA-S评分，该AI模型在术后预后判断上具有增量价值。结果表明，基于活检训练的病理学AI可在不同标本类型间泛化，支持术前与术后决策，但其多模态方法相较于简单模型的额外增益仍需进一步研究验证。
原文摘要 · Abstract (English)
Biochemical recurrence (BCR) after radical prostatectomy (RP) is a surrogate marker for aggressive prostate cancer with adverse outcomes, yet current prognostic tools remain imprecise. We trained an AI-based model on diagnostic prostate biopsy slides from the STHLM3 cohort (n = 676) to predict patient-specific risk of BCR, using foundation models and attention-based multiple instance learning. Generalizability was assessed across three external RP cohorts: LEOPARD (n = 508), CHIMERA (n = 95), and TCGA-PRAD (n = 379). The image-based approach achieved 5-year time-dependent AUCs of 0.64, 0.70, and 0.70, respectively. Integrating clinical variables added complementary prognostic value and enabled statistically significant risk stratification. Compared with guideline-based CAPRA-S, AI incrementally improved postoperative prognostication. These findings suggest biopsy-trained histopathology AI can generalize across specimen types to support preoperative and postoperative decision making, but the added value of AI-based multimodal approaches over simpler predictive models should be critically scrutinized in further studies.
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