Artificial intelligence-based prediction of claudin 18.2 expression and immune phenotype from routine histology to guide treatment decisions in patients with gastric cancer.

Authors

H-D Kim, S Shin, W Hwang, J Shin, T Lee, J Hyung, J Park, S Pereira, C-Y Ock, A Puccini, G Carloni, Y S Park, M-H Ryu

Published in

ESMO open. Volume 11. Issue 7. Pages 108021. Jul 06, 2026. Epub Jul 06, 2026.

Abstract

First-line treatment of gastric cancer is evolving with the integration of immune checkpoint inhibitors (ICIs) and targeted agents, complicating biomarker stratification. Claudin 18.2 (CLDN18.2) is an established target for zolbetuximab; however, immunohistochemistry (IHC) is limited by tissue requirements, cost, and turnaround time. Artificial intelligence (AI) analysis of hematoxylin and eosin (H&E)-stained slides may provide a scalable alternative. We developed and validated an AI model to predict CLDN18.2 expression from H&E slides and evaluated its clinical utility integrated with AI-derived immune phenotyping.
This retrospective study included three independent cohorts of patients with gastric cancer. The development cohort comprised 622 patients (497 for training and 125 for tuning). The internal validation cohort included 378 patients treated with first-line nivolumab plus chemotherapy or chemotherapy alone. The external validation cohort included 98 patients from diverse ethnic backgrounds. Whole-slide H&E-stained images were analyzed using a Vision Transformer-based AI model to predict CLDN18.2 expression. A separate AI model classified the immune microenvironment as inflamed or noninflamed. Primary outcomes were predictive performance metrics, including area under the receiver operating characteristic curve (AUROC). Secondary outcomes included progression-free survival (PFS) and overall survival (OS), stratified by AI-predicted CLDN18.2 status and immune phenotype.
CLDN18.2 positivity by IHC was 42.9% (development), 36.8% (internal validation), and 25.5% (external validation). The AI model yielded AUROC values of 0.752 (internal validation) and 0.856 (external validation). In the internal validation cohort, patients with AI-predicted CLDN18.2-negative/inflamed tumors exhibited improved outcomes with nivolumab plus chemotherapy versus chemotherapy alone [PFS: hazard ratio (HR) 0.35, 95% confidence interval (CI) 0.15-0.82; OS: HR 0.40, 95% CI 0.18-0.89]. Patients with CLDN18.2-positive/noninflamed tumors showed no benefit from nivolumab plus chemotherapy.
An AI model using routine histology predicted CLDN18.2 expression and immune phenotype in gastric cancer, identifying subgroups with differential benefit from ICI-based chemotherapy.

PMID:
42407198
Bibliographic data and abstract were imported from PubMed on 07 Jul 2026.

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