我们引入了一种开创性的方法，将病理学成像与转录组学和蛋白组学相结合，以识别与癌症的关键临床结果相关的预测性组织学特征。我们利用了来自CPTAC的6种癌症类型的657名患者的2,755张H&E染色组织病理学切片。我们的模型有效地复制了人类病理学家容易进行的区分：肿瘤与正常组织（AUROC=0.995）和组织起源（AUROC=0.979）。我们进一步研究了在H&E alone中通常不执行的预测性任务，包括TP53预测和病理分期。重要的是，我们描述了以前在临床实践中未被利用的预测性形态学特征。转录组学和蛋白组学的整合确定了驱动预测性组织学特征的通路级签名和细胞过程。通过使用TCGA确认了模型的推广能力和可解释性。我们提出了这些任务的分类系统，并提出了这种集成人和机器学习方法的潜在临床应用。一个公开可用的基于网络的平台实现了这些模型。版权所有 © 2023 The Author(s). 由Elsevier Inc.发表。保留所有权利。

We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level signatures and cellular processes driving predictive histology features. Model generalizability and interpretability is confirmed using TCGA. We propose a classification system for these tasks, and suggest potential clinical applications for this integrated human and machine learning approach. A publicly available web-based platform implements these models.Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.