Deep learning based digital pathology for predicting treatment response to first-line PD-1 blockade in advanced gastric cancer

作者：Yifan Liu, Wei Chen, Ruiwen Ruan, Zhimei Zhang, Zhixiong Wang, Zhixiong Wang, Tianpei Guan, Qi Lin, Wei Tang, Jun Deng, Zhao Wang, Zhao Wang, Guanghua Li · 发表于：Journal of Translational Medicine · 年份：2024 · DOI：10.1186/s12967-024-05262-z · 被引用次数：25 · 研究领域：Cancer Immunotherapy and Biomarkers、Radiomics and Machine Learning in Medical Imaging、AI in cancer detection

BACKGROUND: Advanced unresectable gastric cancer (GC) patients were previously treated with chemotherapy alone as the first-line therapy. However, with the Food and Drug Administration's (FDA) 2022 approval of programmed cell death protein 1 (PD-1) inhibitor combined with chemotherapy as the first-li ne treatment for advanced unresectable GC, patients have significantly benefited. However, the significant costs and potential adverse effects necessitate precise patient selection. In recent years, the advent of deep learning (DL) has revolutionized the medical field, particularly in predicting tumor treatment responses. Our study utilizes DL to analyze pathological images, aiming to predict first-line PD-1 combined chemotherapy response for advanced-stage GC. METHODS: In this multicenter retrospective analysis, Hematoxylin and Eosin (H&E)-stained slides were collected from advanced GC patients across four medical centers. Treatment response was evaluated according to iRECIST 1.1 criteria after a comprehensive first-line PD-1 immunotherapy combined with chemotherapy. Three DL models were employed in an ensemble approach to create the immune checkpoint inhibitors Response Score (ICIsRS) as a novel histopathological biomarker derived from Whole Slide Images (WSIs). RESULTS: Analyzing 148,181 patches from 313 WSIs of 264 advanced GC patients, the ensemble model exhibited superior predictive accuracy, leading to the creation of ICIsNet. The model demonstrated robust performance acro...