Prediction of microsatellite-stable/epithelial-to-mesenchymal transition molecular subtype gastric cancer using CT radiomics and clinicopathologic factors

Abstract

Objectives

This study aimed to develop a predictive model for the microsatellite-stable (MSS)/epithelial-to-mesenchymal transition (EMT) subtype of gastric cancer (GC) using computed tomography (CT) radiomics and clinicopathological factors.

Materials and Methods

This retrospective study included 418 patients with GC who underwent primary resection and transcriptome analysis with microarray between October 1995 and May 2008. Using preoperative CT images, radiomic features from the volume of interest in the portal venous phase images were extracted. The patient data were randomly divided into training (70%) and testing (30%) datasets. Optimal radiomics features were selected through a thorough feature-selection process. The final radiomic and clinicopathological factors were selected using a stepwise variable selection method. The area under the curve (AUC) was calculated to evaluate performance.

Results

Seventy patients had EMT subtype GC, and 348 patients had non-EMT subtype based on transcriptome analysis. There were 276 men (66.0 %), with a median age of 59 years (interquartile range: 50–67). Eleven radiomic features were selected for the prediction model using the combined variance inflation factor (VIF) and least absolute shrinkage and selection operator (LASSO) method. A CT radiomics-based prediction model was constructed using logistic regression with AUCs of 0.824 and 0.736 for training and testing, respectively. When clinicopathological factors such as age, tumor size, signet ring cell histology, and Lauren classification were combined, the AUCs of the models increased to 0.849 and 0.840 for training and testing, respectively (p < 0.001 for testing).

Conclusion

A prediction model using CT radiomics and clinicopathological factors showed good performance in predicting the EMT subtype of GC.