Abstract
RATIONALE AND OBJECTIVES: Neoadjuvant chemotherapy (NAC) is a promising therapeutic strategy for managing locally advanced gastric cancer (LAGC), aiming to reduce tumor burden, enhance resection rates, and improve clinical outcomes. Due to variability in patient responses, the objective of this study was to enhance the prediction of NAC tumor regression grade (TRG) in patients with LAGC by integrating radiomic features with clinical biomarkers through machine learning (ML) approaches. MATERIALS AND METHODS: We analyzed a cohort of 255 patients with LAGC who underwent NAC prior to surgical resection at the Affiliated Cancer Hospital of Guangxi Medical University. Among these patients, 57 (22.4%) were classified as responders (TRG 0-1), and 198 (77.6%) were identified as non-responders (TRG 2-3). The cohort was divided into a training set (n = 178) and a validation set (n = 77) in a 7:3 ratio. Pre-treatment portal venous-phase computed tomography scans were used to extract 1130 radiomic features via the OnekeyAI platform software. Through feature engineering, we generated a radiomics score (rad score) by linearly combining these features. A variety of ML algorithms were applied to integrate the rad score with clinical biomarkers, resulting in the construction of a hybrid model. The model's diagnostic performance was evaluated using receiver operating characteristic curves and the area under the curve (AUC). RESULTS: Among the ML models tested, the random forest (RF) model performed best when both the rad score and clinical biomarkers were used as input features, leading to our hybrid model development. This hybrid model (AUC = 0.814) outperformed the radiomics (AUC = 0.755) and clinical (AUC = 0.682) models. CONCLUSION: A RF-based hybrid model was developed by integrating radiomics and clinical biomarkers to predict NAC response in patients with LAGC undergoing surgical resection, providing personalized treatment insights.