Potential value of CT radiomics in the distinction of intestinal-type gastric adenocarcinomas.

Abstract

The purpose of the study was to investigate the role of CT radiomics for the preoperative distinction of intestinal-type gastric adenocarcinomas. A total of 187 consecutive patients with preoperative contrast CT examination and pathologically proven gastric adenocarcinoma were retrospectively collected. Patients were divided into a training set (n = 150) and a test set (n = 37). Arterial phase (AP), portal phase (PP), and delay phase (DP) images were retrieved for analysis. A dedicated postprocessing software was used to segment the lesions and extract radiomics features. Random forest (RF) algorithm was applied to construct the classifier models. A nomogram was developed by incorporating multiphase radiomics scores. Receiver operating characteristic (ROC) curves were used to evaluate the performance of the radiomics model and nomogram in both sets. The radiomics model showed a favorable capability in the distinction of intestinal-type gastric adenocarcinomas. The areas under curves (AUCs) of the AP, PP, and DP radiomics models were 0.754 (95% CI: 0.676, 0.820), 0.815 (95% CI: 0.744, 0.874), and 0.764 (95% CI: 0.688, 0.829) in the training set, respectively, which were confirmed in the test set with AUCs of 0.742 (95% CI: 0.572, 0.872), 0.775 (95% CI: 0.608, 0.895), and 0.857 (95% CI: 0.703, 0.950), respectively. The nomogram yielded excellent performance for distinguishing intestinal-type adenocarcinomas in both sets, with AUCs of 0.928 (95%: 0.875, 0.964) and 0.904 (95% CI: 0.761, 0.976). The multiphase CT radiomics nomogram holds promise for the individual preoperative discrimination of intestinal-type gastric adenocarcinoma. • CT radiomics has a potential role in the distinction of intestinal-type gastric adenocarcinomas. • Single-phase enhanced CT-based radiomics showed favorable capability in distinguishing intestinal-type tumors. • The nomogram which incorporates the multiphase radiomics scores could facilitate the individual prediction of intestinal-type lesions.