Building CT Radiomics-Based Models for Preoperatively Predicting Malignant Potential and Mitotic Count of Gastrointestinal Stromal Tumors

Chao Wang; Hailin Li; Yeerfan Jiaerken; Peiyu Huang; Lifeng Sun; Fei Dong; Yajing Huang; Di Dong; Jie Tian; Minming Zhang

doi:10.1016/j.tranon.2019.06.005

Building CT Radiomics-Based Models for Preoperatively Predicting Malignant Potential and Mitotic Count of Gastrointestinal Stromal Tumors

Transl Oncol. 2019 Sep;12(9):1229-1236. doi: 10.1016/j.tranon.2019.06.005. Epub 2019 Jul 4.

Authors

Chao Wang¹, Hailin Li², Yeerfan Jiaerken¹, Peiyu Huang¹, Lifeng Sun³, Fei Dong¹, Yajing Huang⁴, Di Dong⁵, Jie Tian⁶, Minming Zhang⁷

Affiliations

¹ Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
² CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, School of Artificial Intelligence, Beijing, China.
³ Department of Surgical Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
⁴ Department of Pathology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
⁵ CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, School of Artificial Intelligence, Beijing, China. Electronic address: di.dong@ia.ac.cn.
⁶ CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China. Electronic address: jie.tian@ia.ac.cn.
⁷ Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. Electronic address: zhangminming@zju.edu.cn.

Abstract

Purpose: To build radiomic prediction models using contrast-enhanced computed tomography (CE-CT) to preoperatively predict malignant potential and mitotic count of gastrointestinal stromal tumors (GISTs).

Patients and methods: A total of 333 GISTs patients were retrospectively included in our study. Radiomic features were extracted from the preoperative CE-CT images. According to postoperative pathology, patients were categorized by malignant potential and mitotic count, respectively. The most valuable radiomic features were chosen to build a logistic regression model to predict the malignant potential and a random forest classifier model to predict the mitotic count. The performance of radiomic models was assessed with the receiver operating characteristics curve. Our study further developed a radiomic nomogram to preoperatively predict malignant potential in a personalized way for patients with GISTs.

Results: The predictive model was built to discriminate high- from low-malignant potential GISTs with an area under the curve (AUC) of 0.882 (95% CI 0.823-0.942) in the training set and 0.920 (95% CI 0.870-0.971) in the validation set. Moreover, the other radiomic model was built to differentiate high- from low-mitotic count GISTs with an AUC of 0.820 (95% CI 0.753-0.887) in the training set and 0.769 (95% CI 0.654-0.883) in the validation set.

Conclusion: The radiomic models using CE-CT showed a good predictive performance for preoperative risk stratification of GISTs and hold great potential for personalized clinical decision making.