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

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.654114

DOI=10.3389/fonc.2021.654114

ISSN=2234-943X

ABSTRACT=Objective To establish and verify a computed tomography (CT)-based multi-class prediction
model for discriminating the risk stratification of gastrointestinal stromal tumors (GISTs).
Methods A 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 (ROI) 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.
Results The GISTs risk three-class radiomics model had a macro/micro average 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.
Conclusion CT radiomics can discriminate GISTs risk stratification. The performance of the
three-class radiomics prediction model is excellent, 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.