本研究旨在利用组织病理学图像预测子宫内膜癌（EC）的分子特征和EC患者的总生存期（OS）。采用癌症基因组图谱（TCGA）中的患者，按1:1比例分为训练集（n = 215）和测试集（n = 214）。通过分析定量组织病理学图像特征并建立经交叉验证验证的随机森林模型，我们构建了OS的预测模型。模型性能用时间相关接收操作特征曲线（AUC）在测试集上进行评估。基于组织病理学图像特征（HIF）的预测模型预测了测试集上的OS（5年AUC = 0.803）。组织病理学与组学相结合的综合模型的性能优于基因组学、转录组学或蛋白组学单独使用。此外，包括HIF、基因组学、转录组学和蛋白组学在内的多维组学数据在1年、3年和5年分别达到了最大的AUC值为0.866、0.869和0.856，展示了生存率上最高的差异（HR = 18.347，95% CI [11.09-25.65]，p < 0.001）。实验结果表明，HIF的互补特征可以提高EC患者的预后性能。此外，HIF和多维组学数据的整合可能改善临床实践中的生存预测和风险分层。© 2023 Li等。

This study aimed to predict the molecular features of endometrial carcinoma (EC) and the overall survival (OS) of EC patients using histopathological imaging.The patients from The Cancer Genome Atlas (TCGA) were separated into the training set (n = 215) and test set (n = 214) in proportion of 1:1. By analyzing quantitative histological image features and setting up random forest model verified by cross-validation, we constructed prognostic models for OS. The model performance is evaluated with the time-dependent receiver operating characteristics (AUC) over the test set.Prognostic models based on histopathological imaging features (HIF) predicted OS in the test set (5-year AUC = 0.803). The performance of combining histopathology and omics transcends that of genomics, transcriptomics, or proteomics alone. Additionally, multi-dimensional omics data, including HIF, genomics, transcriptomics, and proteomics, attained the largest AUCs of 0.866, 0.869, and 0.856 at years 1, 3, and 5, respectively, showcasing the highest discrepancy in survival (HR = 18.347, 95% CI [11.09-25.65], p < 0.001).The results of this experiment indicated that the complementary features of HIF could improve the prognostic performance of EC patients. Moreover, the integration of HIF and multi-dimensional omics data might ameliorate survival prediction and risk stratification in clinical practice.© 2023 Li et al.