本文由小咖机器人翻译整理

期刊来源：Nat Med

原文链接：https://doi.org/10.1038/s41591-024-02993-w

摘要内容如下：

预测子宫内膜癌（EC）的远处复发是个性化辅助治疗的关键。目前结合病理学和分子分析的黄金标准是昂贵的，阻碍了实施。在这里，我们开发了HECTO（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、全玻片图像和肿瘤分期作为输入，对来自8个EC队列（包括PORTEC-1/-2/-3随机试验）的2，072名患者进行了研究。Hector在内部（n=353）和两个外部（n=160和n=151）测试集中的C指数分别为0.789、0.828和0.815，优于目前的金标准，并确定了具有显著不同结果的患者（通过Kaplan-Meier分析，Hector低、中和高危组的10年无远处复发概率分别为97.0%、77.7%和58.1%）。赫克托还预测辅助化疗比目前的方法更好。形态学和基因组特征提取确定了赫克托风险组的相关性，其中一些具有治疗潜力。赫克托改进了目前的黄金标准，可能有助于在EC中提供个性化治疗。

英文原文如下：

Abstracts

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.

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