Deep Learning –Based Stratification of Gastric Cancer Patients from Hematoxylin and Eosin–Stained Whole Slide Images by Predicting Molecular Features for Immunotherapy Response
Determining the molecular characteristics of cancer patients is crucial for optimal immunotherapy decisions. The aim of this study was to screen immunotherapy beneficiaries by predicting key molecular features from hematoxylin and eosin –stained images based on deep learning models. An independent data set from Asian gastric cancer patients was included for external validation. In addition, a segmentation model (HoVer-Net) was used to quantify the cellular composition of tumor stroma. The model performance was evaluated by measur ing the area under the curve (AUC).
Source: American Journal of Pathology - Category: Pathology Authors: Zheng Wei, Xu Zhao, Jing Chen, Qiuyan Sun, Zeyang Wang, Yanli Wang, Zhiyi Ye, Yuan Yuan, Liping Sun, Jingjing Jing Tags: Regular article Source Type: research
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