Computer-aided diagnosis of gastrointestinal stromal tumors: a radiomics method on endoscopic ultrasound image

The purpose of our study is to propose a preoperative computer-aided diagnosis system based on a radiomics method to differentiate gastrointestinal stromal tumors (GISTs) of the higher-risk group (HRG) from those of the lower-risk group (LRG) on endoscopic ultrasound (EUS) images.

Gastro-EUS (G-EUS) images of four different risk level GISTs were collected from 19 hospitals. The datasheet included 168 case HRG GISTs and 747 case LRG GISTs. A radiomics method with image segmentation, feature extraction, feature selection and classification was developed. Here 439 radiomics features were firstly extracted, and then, the least absolute shrinkage selection operator (lasso) model with a tenfold cross-validation and 31 bootstraps was used to reduce the dimension of feature sets. Finally, random forest was applied to establish the classification model.

The proposed model differentiated 32 case HRG GISTs from 149 case LRG GISTs. Result for the testing set achieved the area under the receiver operating characteristic curve of 0.839, the accuracy of 0.823, the sensitivity of 0.813 and the specificity of 0.826.

The model could increase preoperative diagnostic accuracy and provide a valuable reference for the doctors.