Development and validation of an individualized nomogram for predicting distant metastases in gastric cancer using a CT radiomics-clinical model.

Purpose: This study aimed to develop and validate a model for accurately assessing the risk of distant metastases in patients with gastric cancer (GC). Methods: A total of 301 patients (training cohort, n = 210; testing cohort, n = 91) with GC were retrospectively collected. Relevant clinical predictors were determined through the application of univariate and multivariate logistic regression analyses. Then the clinical model was established. Venous phase computed tomography (VPCT) images were utilized to extract radiomic features, and relevant features were selected using univariate analysis, Spearman correlation coefficient, and the least absolute shrinkage and selection operator (Lasso) regression. Subsequently, radiomics scores were calculated based on the selected features. Radiomics models were constructed using five machine learning algorithms according to the screened features. Furthermore, separate joint models incorporating radiomic features and clinically independent predictors were established using traditional logistic regression algorithms and machine learning algorithms, respectively. All models were comprehensively assessed through discrimination, calibration, reclassification, and clinical benefit analysis. Results: The multivariate logistic regression analysis revealed that age, histological grade, and N stage were independent predictors of distant metastases. The radiomics score was derived from 15 selected features out of a total of 944 radiomic features. The predictive performance of the joint model 1 [AUC (95% CI) 0.880 (0.811-0.949)] constructed using logistic regression is superior to that of the joint model 2 [AUC (95% CI) 0.834 (0.736-0.931)] constructed using SVM algorithm. The joint model 1 [AUC(95% CI) 0.880(0.811-0.949)], demonstrated superior performance compared to the clinical model [AUC(95% CI) 0.781(0.689-0.873)] and radiomics model [AUC(95% CI) 0.740(0.626-0.855), using LR algorithm]. The NRI and IDI values for the joint model 1 and clinical model were 0.115 (95% CI 0.014 -0.216) and 0.132 (95% CI 0.093-0.171), respectively; whereas for the joint model 1 and LR model, they were found to be 0.130 (95% CI 0.018-0.243) and 0.116 (95% CI 0.072-0.160), respectively. Decision curve analysis indicated that the joint model 1 exhibited a higher clinical net benefit than other models. Conclusions: The nomogram of the joint model, integrating radiomic features and clinically independent predictors, exhibits robust predictive capability for early identification of high-risk patients with a propensity for distant metastases of GC.