肿瘤突变负荷（TMB）是选择可能从免疫检查点抑制剂治疗中受益的患者的重要预测生物标志物。全外显子测序是一种常用的TMB测量方法，然而，其临床应用受到高昂的成本和耗时的湿实验和生物信息学分析的限制。为了解决这个挑战，我们从癌症基因组图谱中下载了326例胃癌患者的多模态数据，包括组织病理学图像、临床数据和各种分子数据。利用这些数据，我们进行了全面分析，研究了TMB、临床因素、基因表达和从血红素和伊红染色图像中提取的图像特征之间的关系。我们进一步探索了利用基于残差网络（Resnet）的深度学习算法进行组织病理学图像分析来预测TMB水平的可行性。此外，我们开发了一种多模态融合深度学习模型，将组织病理学图像与组学数据相结合，以预测TMB水平。我们利用不同的TMB阈值评估了我们的模型与各种最先进方法的性能，并获得了令人满意的结果。具体而言，我们的组织病理学图像分析模型达到了0.749的曲线下面积（AUC）。值得注意的是，多模态融合模型在AUC最高达到0.971的情况下明显优于仅依赖组织病理学图像的模型。我们的发现表明，组织病理学图像可以以合理的准确性预测胃癌患者的TMB水平，而多模态深度学习可以实现更高水平的准确性。这项研究为胃癌患者TMB的预测提供了新的思路。© 作者 2023。牛津大学出版社保留所有权利。请发送电子邮件至journals.permissions@oup.com以获取权限。

Tumor mutational burden (TMB) is a significant predictive biomarker for selecting patients that may benefit from immune checkpoint inhibitor therapy. Whole exome sequencing is a common method for measuring TMB; however, its clinical application is limited by the high cost and time-consuming wet-laboratory experiments and bioinformatics analysis. To address this challenge, we downloaded multimodal data of 326 gastric cancer patients from The Cancer Genome Atlas, including histopathological images, clinical data and various molecular data. Using these data, we conducted a comprehensive analysis to investigate the relationship between TMB, clinical factors, gene expression and image features extracted from hematoxylin and eosin images. We further explored the feasibility of predicting TMB levels, i.e. high and low TMB, by utilizing a residual network (Resnet)-based deep learning algorithm for histopathological image analysis. Moreover, we developed a multimodal fusion deep learning model that combines histopathological images with omics data to predict TMB levels. We evaluated the performance of our models against various state-of-the-art methods using different TMB thresholds and obtained promising results. Specifically, our histopathological image analysis model achieved an area under curve (AUC) of 0.749. Notably, the multimodal fusion model significantly outperformed the model that relied only on histopathological images, with the highest AUC of 0.971. Our findings suggest that histopathological images could be used with reasonable accuracy to predict TMB levels in gastric cancer patients, while multimodal deep learning could achieve even higher levels of accuracy. This study sheds new light on predicting TMB in gastric cancer patients.© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.