利用多模态深度学习从组织病理学图像预测胃癌肿瘤突变负荷

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Jing Li, Haiyan Liu, Wei Liu, Peijun Zong, Kaimei Huang, Zibo Li, Haigang Li, Ting Xiong, Geng Tian, Chun Li, Jialiang Yang
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引用次数: 0

摘要

肿瘤突变负荷(TMB)是选择可能从免疫检查点抑制剂治疗中获益的患者的重要预测性生物标志物。全外显子组测序是测量TMB的常用方法;然而,其临床应用受到了高成本、耗时的湿实验室实验和生物信息学分析的限制。为了应对这一挑战,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,我们进行了综合分析,研究 TMB、临床因素、基因表达以及从苏木精和伊红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,从而预测 TMB 水平(即高 TMB 和低 TMB)的可行性。此外,我们还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omics数据结合起来预测TMB水平。我们使用不同的 TMB 阈值评估了我们的模型与各种最先进方法的性能,并取得了令人满意的结果。具体来说,我们的组织病理学图像分析模型的曲线下面积(AUC)达到了 0.749。值得注意的是,多模态融合模型的表现明显优于仅依赖组织病理学图像的模型,AUC 最高,达到 0.971。我们的研究结果表明,组织病理学图像可用于预测胃癌患者的 TMB 水平,且准确度较高,而多模态深度学习可达到更高的准确度。这项研究为预测胃癌患者的 TMB 带来了新的启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting gastric cancer tumor mutational burden from histopathological images using multimodal deep learning.

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.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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