Abstract
BACKGROUND: Neoadjuvant immunotherapy has been shown to improve survival in patients with gastric cancer. This study sought to develop and validate a radiomics-based machine learning (ML) model for patients with locally advanced gastric cancer (LAGC), specifically to predict whether patients will achieve a major pathological response (MPR) following neoadjuvant immunotherapy. With its predictive capabilities, this tool shows promise for enhancing clinical decision-making processes in the future. METHODS: This study utilized a multicenter cohort design, retrospectively gathering clinical data and computed tomography (CT) images from 268 patients diagnosed with advanced gastric cancer who underwent neoadjuvant immunotherapy between January 2019 and December 2023 from two medical centers. Radiomic features were extracted from CT images, and a multi-step feature selection procedure was applied to identify the top 20 representative features. Nine ML algorithms were implemented to build prediction models, with the optimal algorithm selected for the final prediction model. The hyperparameters of the chosen model were fine-tuned using Bayesian optimization and grid search. The performance of the model was evaluated using several metrics, including the area under the curve (AUC), accuracy, and Cohen's kappa coefficient. RESULTS: Three cohorts were included in this study: the development cohort (DC, n = 86), the internal validation cohort (IVC, n = 59), and the external validation cohort (EVC, n = 52). Nine ML models were developed using DC cases. Among these, an optimized Bayesian-LightGBM model, demonstrated robust predictive performance for MPR following neoadjuvant immunotherapy in LAGC patients across all cohorts. Specifically, within DC, the LightGBM model attained an AUC of 0.828, an overall accuracy of 0.791, a Cohen's kappa coefficient of 0.552, a sensitivity of 0.742, a specificity of 0.818, a positive predictive value (PPV) of 0.586, a negative predictive value (NPV) of 0.867, a Matthews correlation coefficient (MCC) of 0.473, and a balanced accuracy of 0.780. Comparable performance metrics were validated in both the IVC and the EVC, with AUC values of 0.777 and 0.714, and overall accuracies of 0.729 and 0.654, respectively. These results suggested good fitness and generalization of the Bayesian-LightGBM model. Shapley Additive Explanations (SHAP) analysis identified significant radiomic features contributing to the model's predictive capability. The SHAP values of the features wavelet.LLH_gldm_SmallDependenceLowGrayLevelEmphasis, wavelet.HHL_glrlm_RunVariance, and wavelet.LLH_glszm_LargeAreaHighGrayLevelEmphasis were ranked among the top three, highlighting their significant contribution to the model's predictive performance. In contrast to existing radiomic models that exclusively focus on neoadjuvant chemotherapy, our model integrates both neoadjuvant immunotherapy and chemotherapy, thereby offering more precise predictive capabilities. CONCLUSION: The radiomics-based ML model demonstrated significant efficacy in predicting the pathological response to neoadjuvant immunotherapy in LAGC patients, thereby providing a foundation for personalized treatment strategies.