CT Radiomics Model for Discriminating the Risk Stratification of Gastrointestinal Stromal Tumors: A Multi-Class Classification and Multi-Center Study.

Zhonghua Chen,Yue Xiong,Linyi Xu,Chuanmin Zhang,Zhan Feng,Minhong Wang,Chencui Huang

doi:10.3389/fonc.2021.654114

Abstract

ObjectiveTo establish and verify a computed tomography (CT)-based multi-class prediction model for discriminating the risk stratification of gastrointestinal stromal tumors (GISTs).Materials and MethodsA total of 381 patients with GISTs were confirmed by surgery and pathology. Information on 213 patients were obtained from one hospital and used as training cohort, whereas the details of 168 patients were collected from two other hospitals and used as independent validation cohort. Regions of interest on CT images of arterial and venous phases were drawn, radiomics features were extracted, and dimensionality reduction processing was performed. Using a one-vs-rest method, a Random Forest-based GISTs risk three-class prediction model was established, and the receiver operating characteristic curve (ROC) was used to evaluate the performance of the multi-class classification model, and the generalization ability was verified using external data.ResultsThe training cohort included 96 very low-risk and low-risk, 60 intermediate-risk and 57 high-risk patients. External validation cohort included 82 very low-risk and low-risk, 48 intermediate-risk and 38 high-risk patients. The GISTs risk three-class radiomics model had a macro/micro average area under the curve (AUC) of 0.84 and an accuracy of 0.78 in the training cohort. It had a stable performance in the external validation cohort, with a macro/micro average AUC of 0.83 and an accuracy of 0.80.ConclusionCT radiomics can discriminate GISTs risk stratification. The performance of the three-class radiomics prediction model is good, and its generalization ability has also been verified in the external validation cohort, indicating its potential to assist stratified and accurate treatment of GISTs in the clinic.

Highlights

Gastrointestinal stromal tumors (GISTs) originate from the interstitial cells of the gastrointestinal pacemaker Cajal cells, which are the most common mesenchymal tissue-derived tumors in the digestive system
A total of 381 patients with GISTs were enrolled in this study, including a training cohort of 119 men and 94 women, and an external validation cohort of 89 men and 79 women
Zhang et al conducted a four-class prediction study on the risk of GISTs, and the results suggested that the training cohort area under the curve (AUC) was 0.86 with an accuracy of 0.65, and the internal validation cohort AUC was 0.80, with an accuracy of 0.67, proving a good prediction performance of the model [13]

Summary

Introduction

Gastrointestinal stromal tumors (GISTs) originate from the interstitial cells of the gastrointestinal pacemaker Cajal cells, which are the most common mesenchymal tissue-derived tumors in the digestive system. These usually occur in the stomach and small intestine, accounting for approximately 1 to 2% of all malignant tumors of the digestive tract [1]. Joensuu et al proposed a modified version of the National Institutes of Health (NIH) risk stratification standard, which integrates these four prognostic factors into an evaluation system and classifies the risk of GISTs into four levels: very low, low, intermediate, and high. It is currently a clinical stratification standard for predicting the risk of recurrence with relatively high practicality [5]

Methods

Results

Discussion

Conclusion