Prediction model based on radiomics and clinical features for preoperative lymphovascular invasion in patients with gastric cancer with Lauren classification

Abstract

Abstract Objective Lymphovascular invasion (LVI) is an independent risk factor of gastric cancer (GC) prognosis; however, LVI cannot be determined preoperatively. We explored whether a model based on contrast-enhanced computed tomography (CECT) radiomics features combined with clinicopathological factors can evaluate preoperative LVI in patients with GC with a clear Lauren classification. Methods We retrospectively analyzed 495 patients with GC, including 288 with LVI. The extracted CECT features were standardized, followed by consistency testing, correlation analysis, univariate analysis, and multivariate least absolute shrinkage and selection operator (LASSO) regression analysis. The radiomics score (Radcore) was calculated for each patient. Univariate (p < 0.10) and multivariate (p < 0.050) analyses were used to identify the clinical risk factors associated with LVI. Accordingly, three prediction models were established: Clinical + Arterial phase_Radcore, Clinical + Venous phase_Radcore, and a combined model. The prediction performances of the models were verified using receiver operating characteristics, decision curve analysis, and calibration curves in the testing dataset. The relationship between Lauren classification and LVI was analyzed using a histogram. Results The areas under the curve of the combined model were 0.8629 (95% confidence interval [CI], 0.8247–0.9011) and 0.8343 (95% CI, 0.7673–0.9012) in the training and testing datasets, respectively. The combined model had superior performance compared with the other models. Diffuse-type GC according to the Lauren classification accounted for 43.4% of LVI cases. Conclusions CECT-based radiomics models can effectively predict the preoperative LVI status in patients with GC with Lauren classification. The prediction ability of the models was effectively improved by incorporating clinicopathological factors.