Da Guan
Background: Therapeutic vulnerability in gastric cancer is profoundly influenced by the tumor microenvironment (TME), yet reliable and clinically actionable preoperative indicators remain insufficient. Methods: We developed and validated an artificial intelligence-driven multi-omics TME score (DLRS/TMEscore) by integrating CT-derived imaging features with transcriptomic, immunohistochemical, and molecular profiling. The score was evaluated for its associations with survival outcomes, benefit from adjuvant chemotherapy, and response to anti-PD-1 therapy. Results: The DLRS/TMEscore reproducibly stratified disease-free and overall survival across independent cohorts. Patients in the low-risk subgroup derived substantial benefit from adjuvant chemotherapy, whereas those in the high-risk subgroup demonstrated attenuated benefit. Among individuals receiving immunotherapy, the score enriched objective responders and predicted more durable clinical outcomes, outperforming established biomarkers including PD-L1 combined positive score (CPS) and microsatellite instability (MSI). In addition, DLRS/TMEscore correlated with multiple surgical parameters, such as operative complexity, resection margin status, nodal involvement, and postoperative recovery, indicating relevance in perioperative risk assessment. Conclusion: This AI-enabled multi-omics framework offers a robust and interpretable approach for characterizing microenvironment-defined therapeutic vulnerability, supporting preoperative risk stratification and individualized systemic treatment strategies in gastric cancer.