Accurately predicting patient survival is essential for cancer treatment decision. However, the prognostic prediction model based on histopathological images of stomach cancer patients is still yet to be developed. We propose a deep learning-based model (MultiDeepCox-SC) that predicts overall survival in patients with stomach cancer by integrating histopathological images, clinical data, and gene expression data. The MultiDeepCox-SC not only automatedly selects patches with more information for survival prediction, without manual labeling for histopathological images, but also identifies genetic and clinical risk factors associated with survival in stomach cancer. The prognostic accuracy of the MultiDeepCox-SC (C-index = 0.744) surpasses the result only based on histopathological image (C-index = 0.660). The risk score of our model was still an independent predictor of survival outcome after adjustment for potential confounders, including pathologic stage, grade, age, race, and gender on The Cancer Genome Atlas dataset (hazard ratio 1.555, p = 3.53e-08) and the external test set (hazard ratio 2.912, p = 9.42e-4). Our fully automated online prognostic tool based on histopathological images, clinical data, and gene expression data could be utilized to improve pathologists' efficiency and accuracy (https://yu.life.sjtu.edu.cn/DeepCoxSC).

中文翻译：

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使用表达数据和组织病理学图像的深度学习模型预测胃癌的生存

准确预测患者的生存对于癌症治疗决策至关重要。然而，基于胃癌患者组织病理学图像的预后预测模型仍有待开发。我们提出了一种基于深度学习的模型 (MultiDeepCox-SC)，该模型通过整合组织病理学图像、临床数据和基因表达数据来预测胃癌患者的总体存活率。MultiDeepCox-SC 不仅自动选择具有更多信息的补丁用于生存预测，无需手动标记组织病理学图像，而且还识别与胃癌生存相关的遗传和临床风险因素。MultiDeepCox-SC（C 指数 = 0.744）的预后准确性超过仅基于组织病理学图像的结果（C 指数 = 0.660）。p  = 3.53e-08) 和外部测试集（风险比 2.912，p  = 9.42e-4）。我们基于组织病理学图像、临床数据和基因表达数据的全自动在线预后工具可用于提高病理学家的效率和准确性 (https://yu.life.sjtu.edu.cn/DeepCoxSC)。