Deep Learning-Based Stratification of Gastric Cancer Patients from Hematoxylin and Eosin-Stained Whole Slide Images by Predicting Molecular Features for Immunotherapy Response

Abstract

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 (Horizontal-Vertical Network) was used to quantify the cellular composition of tumor stroma. The model performance was evaluated by measuring the area under the curve (AUC). The tumor extraction model achieved an AUC of 0.9386 and 0.9062 in the internal and external test sets, respectively. The stratification model could predict the immunotherapy-sensitive subtypes (AUC range, 0.8685 to 0.9461), the genetic mutations (AUC range, 0.8283 to 0.9225), and the pathway activity (AUC range, 0.7568 to 0.8612) fairly accurately. In external validation, the prediction performance of Epstein-Barr virus and programmed cell death ligand 1 expression status achieved AUCs of 0.7906 and 0.6384, respectively. The segmentation model identified a relatively high proportion of inflammatory cells and connective cells in some immunotherapy-sensitive subtypes. The deep learning-based models potentially may serve as a valuable tool to screen for the beneficiaries of immunotherapy in gastric cancer patients.