Clinico-radiological nomogram for preoperatively predicting post-resection hepatic metastasis in patients with gastric adenocarcinoma

Abstract

Objective: To establish and validate a model comprising clinical and radiological features to pre-operatively predict post-resection hepatic metastasis (HM) in patients with gastric adenocarcinoma (GAC). Methods: We retrospectively analyzed 461 patients (HM, 106 patients); and non-metastasis (NM, 355 patients) who were confirmed to have GAC post-surgery. The patients were randomly divided into the training (n = 307) and testing (n = 154) cohorts in a 2:1 ratio. The main clinical risk factors were filtered using the least absolute shrinkage and selection operator algorithm according to their diagnostic value. The selected factors were then used to establish a clinical–radiological model using stepwise logistic regression. The Akaike’s information criterion and receiver operating characteristic (ROC) analyses were used to evaluate the prediction performance of the model. Results: Logistic regression analysis showed that the peak enhancement phase, tumor location, alpha-fetoprotein, cancer antigen (CA)−125, CA724 levels, CT-based Tstage and arterial phase CT values were important independent predictors. Based on these predictors, the areas under the ROC curve of the training and testing cohorts were 0.864 and 0.832, respectively, for predicting post-operative HM. Conclusion: This study built a synthetical nomogram using the pre-operative clinical and radiological features of patients to predict the likelihood of HM occurring after GAC surgery. It may help guide pre-operative clinical decision-making and benefit patients with GAC in the future. Advances in knowledge: 1. The combination of clinical risk factors and CT imaging features provided useful information for predicting HM in GAC. 2. A clinicoradiological nomogram is a tool for the pre-operative prediction of HM in patients with GAC.