Development and validation of a CT-based radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma.

Abstract

ObjectivesTo develop and validate a radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma (GA).MethodsThis retrospective study enrolled 592 patients with clinicopathologically confirmed GA (low-grade: n=154; high-grade: n=438) from January 2008 to March 2018 who were divided into training (n=450) and validation (n=142) sets according to the time of computed tomography (CT) examination. Radiomic features were extracted from the portal venous phase CT images. The Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression model were used for feature selection, data dimension reduction and radiomics signature construction. Multivariable logistic regression analysis was applied to develop the prediction model. The radiomics signature and independent clinicopathologic risk factors were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration and discrimination.ResultsA radiomics signature containing 12 selected features was significantly associated with the histologic grade of GA (P<0.001 for both training and validation sets). A nomogram including the radiomics signature and tumor location as predictors was developed. The model showed both good calibration and good discrimination, in which C-index in the training set, 0.752 [95% confidence interval (95% CI): 0.701−0.803]; C-index in the validation set, 0.793 (95% CI: 0.711−0.874).ConclusionsThis study developed a radiomics nomogram that incorporates tumor location and radiomics signatures, which can be useful in facilitating preoperative individualized prediction of histologic grade of GA.