预测子宫内膜癌（EC）的远处复发对于个性化辅助治疗至关重要。目前结合病理和分子分析的黄金标准成本高昂，阻碍了实施。在这里，我们开发了 HECTOR（基于组织病理学的子宫内膜癌定制结果风险），这是一种多模式深度学习预后模型，使用苏木精和伊红染色的全幻灯片图像和肿瘤分期作为输入，对来自 8 个 EC 队列（包括 PORTEC-1）的 2,072 名患者进行了研究/-2/-3 随机试验。 HECTOR 证明，内部 (n = 353) 和两个外部 (n = 160 和 n = 151) 测试集的 C 指数分别为 0.789、0.828 和 0.815，优于当前的金标准，并识别出具有显着不同结果的患者 (10根据 Kaplan-Meier 分析，HECTOR 低风险组、中风险组和高风险组的 1 年远处无复发概率分别为 97.0%、77.7% 和 58.1%。 HECTOR 还预测辅助化疗的益处比现有方法更好。形态学和基因组特征提取确定了 HECTOR 风险组的相关性，其中一些具有治疗潜力。 HECTOR 改进了当前的黄金标准，可能有助于在 EC 中提供个性化治疗。© 2024。作者。

Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopathology-based endometrial cancer tailored outcome risk), a multimodal deep learning prognostic model using hematoxylin and eosin-stained, whole-slide images and tumor stage as input, on 2,072 patients from eight EC cohorts including the PORTEC-1/-2/-3 randomized trials. HECTOR demonstrated C-indices in internal (n = 353) and two external (n = 160 and n = 151) test sets of 0.789, 0.828 and 0.815, respectively, outperforming the current gold standard, and identified patients with markedly different outcomes (10-year distant recurrence-free probabilities of 97.0%, 77.7% and 58.1% for HECTOR low-, intermediate- and high-risk groups, respectively, by Kaplan-Meier analysis). HECTOR also predicted adjuvant chemotherapy benefit better than current methods. Morphological and genomic feature extraction identified correlates of HECTOR risk groups, some with therapeutic potential. HECTOR improves on the current gold standard and may help delivery of personalized treatment in EC.© 2024. The Author(s).