Multimodal radiopathomics signature for prediction of response to immunotherapy-based combination therapy in gastric cancer using interpretable machine learning
Weicai Huang, Xiaoyan Wang, Rou Zhong, Zhe Li, Kangneng Zhou, Qing Lyu, Jinmin Han, Tao Chen, Md Tauhidul Islam, Qingyu Yuan, M. Usman Ahmad, Sitong Chen, Chuanli Chen, Jiongqiang Huang, Jingjing Xie, Yunhao Shen, Wenjun Xiong, Lin Shen, Yikai Xu, Fan Yang, Zhijun Xu, Guoxin Li, Yuming Jiang
Abstract
Immunotherapy has become a cornerstone in the treatment of advanced gastric cancer (GC). However, identifying reliable predictive biomarkers remains a considerable challenge. This study demonstrates the potential of integrating multimodal baseline data, including computed tomography scan images and digital H&E-stained pathology images, with biological interpretation to predict the response to immunotherapy-based combination therapy using a multicenter cohort of 298 GC patients. By employing seven machine learning approaches, we developed a radiopathomics signature (RPS) to predict treatment response and stratify prognostic risk in GC. The RPS demonstrated area under the receiver-operating-characteristic curves (AUCs) of 0.978 (95 % CI, 0.950-1.000), 0.863 (95 % CI, 0.744-0.982), and 0.822 (95 % CI, 0.668-0.975) in the training, internal validation, and external validation cohorts, respectively, outperforming conventional biomarkers such as CPS, MSI-H, EBV, and HER-2. Kaplan-Meier analysis revealed significant differences of survival between high- and low-risk groups, especially in advanced-stage and non-surgical patients. Additionally, genetic analyses revealed that the RPS correlates with enhanced immune regulation pathways and increased infiltration of memory B cells. The interpretable RPS provides accurate predictions for treatment response and prognosis in GC and holds potential for guiding more precise, patient-specific treatment strategies while offering insights into immune-related mechanisms.