Can I read Predicting Gastric Cancer Tumor Mutational Burden from Histopathological Images Using Multimodal Deep Learning on EtoBox?

Predicting Gastric Cancer Tumor Mutational Burden from Histopathological Images Using Multimodal Deep Learning by Jing Li; Haiyan Liu; Wei Liu; Peijun Zong; Kaimei Huang; Zibo Li; Haigang Li; Ting Xiong; Geng Tian; Chun Li; Jialiang Yang is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Predicting Gastric Cancer Tumor Mutational Burden from Histopathological Images Using Multimodal Deep Learning about?

## Abstract 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 thresh

Who reads Predicting Gastric Cancer Tumor Mutational Burden from Histopathological Images Using Multimodal Deep Learning?

Author

Jing Li; Haiyan Liu; Wei Liu; Peijun Zong; Kaimei Huang; Zibo Li; Haigang Li; Ting Xiong; Geng Tian; Chun Li; Jialiang Yang

Publisher

Oxford University Press (OUP)

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)