Multimodal Radiopathomics Signature for Prediction of Response to Immunotherapy-based Combination Therapy in Gastric Cancer Using Interpretable Machine Learning.
Authors
Affiliations (18)
Affiliations (18)
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, China; Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
- Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
- School of Computer Science, Nankai University, Tianjin, China.
- Department of Radiology, Wake Forest University School of Medicine, Winston Salem, North Carolina, USA.
- Department of Radiation Oncology, Wake Forest University School of Medicine, Winston Salem, North Carolina, USA.
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Statistical Sciences, Wake Forest University, Winston Salem, North Carolina, USA.
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, China.
- Graduate Group of Epidemiology, University of California Davis, Davis, CA, USA.
- Department of Gastrointestinal Surgery, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China.
- Department of Gastrointestinal Oncology, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China.
- Department of Computer Science, Wake Forest University, Winston Salem, North Carolina, USA.
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China. Electronic address: [email protected].
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China; Department of Gastrointestinal Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China. Electronic address: [email protected].
- Department of Radiation Oncology, Wake Forest University School of Medicine, Winston Salem, North Carolina, USA. Electronic address: [email protected].
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.