What question did this study set out to answer?

To develop a multimodal deep learning model for predicting the response of gastric cancer patients to immunotherapy based on integrated clinical and imaging data.

April 5, 2026

Abstract 6728: Predicting gastric cancer response to anti-PD-1 first-line treatment based on multi-modal data: A multi-center study.

Key Points

To develop a multimodal deep learning model for predicting the response of gastric cancer patients to immunotherapy based on integrated clinical and imaging data.
Retrospective analysis of 477 gastric cancer patients from five centers.
Integration of clinical data, CT-derived radiomics, and histopathology features.
Weakly supervised computational pathology was used for extracting features from whole-slide images.
Deep learning embeddings and traditional pathomics features were combined in a Transformer-based model.
The clinical-radio-pathomic model achieved a validation AUC of 0.857 and a test set AUC of 0.917 for progression-free survival (PFS).
This model was validated as an independent prognostic biomarker for PFS and overall survival (OS), with significant hazard ratios for both.
The model outperformed unimodal approaches using CT or histopathology alone, suggesting enhanced predictive capability.

Abstract

Abstract Objectives: Excepted for PD-L1 expression, no predictive maker for immunotherapy has been identified in gastric cancer. Our study aimed to develop a multimodal deep learning model by integrating clinical features, CT-derived radiomics, and H handcrafted pathomics features (morphological, textural, color) were also extracted to complement deep learning pathology embeddings. Contrast-enhanced CT images were segmented via a nnU-Net cascade. The nnU-Net encoder generated deep radiological embeddings, while handcrafted radiomics features (first-order statistics, texture matrices, shape descriptors) were computed from segmented regions to quantify tumor heterogeneity. A Transformer-based architecture integrated the above pathology/imaging features and patient metastatic information via cross-modal self-attention, enabling synergistic multimodal learning. Results: The dataset was allocated into training (n = 322, Center 1) and validation cohort (n = 115, Center 2-5). The integrated clinical-radio-pathomic (CRP) model demonstrated strong performance (Validation AUC = 0.857, Test set AUC=0.917, C-index = 0.746 for PFS prediction and C-index = 0.864 for OS prediction) and emerged as an independent prognostic biomarker for PFS (hazard ratio HR = 4.29, 95% confidence interval CI,1.20-15.35, p = 0.025) and OS (HR = 8.39, 95% CI, 1.47-47.75, p = 0.017) controlling for other clinical variables (i.e., age, sex and PD-L1 CPS). Importantly, the CRP model outperformed unimodal models derived from radiology (Test AUC = 0.7583,Validation AUC = 0.759) or pathology (Test AUC = 0.83;Validation AUC = 0.796) alone, as well as PD-L1 CPS. Conclusion: Our findings highlight the promise of multimodal deep learning in precisely identifying patients who are most likely to benefit from first-line immunotherapy. Citation Format: Jingyuan Wang, Peng Kuang, Yufu Lin, Yutong Liu, Kaiyue Zheng, Yu Jiang, Guangjun Yu, Ying Ding, Yingyong Hou, Tianshu Liu. Predicting gastric cancer response to anti-PD-1 first-line treatment based on multi-modal data: A multi-center study abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6728.

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Cite This Study

Wang et al. (Fri,) studied this question.

synapsesocial.com/papers/69d1fcd4a79560c99a0a28d9 https://doi.org/https://doi.org/10.1158/1538-7445.am2026-6728

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