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

文献类型：期刊论文

作者Wang, Chao1; Li, Hailin2,3; Jiaerken, Yeerfan1; Huang, Peiyu1; Sun, Lifeng4; Dong, Fei1; Huang, Yajing5; Dong, Di2,3; Tian, Jie2,6,7; Zhang, Minming1
刊名TRANSLATIONAL ONCOLOGY
出版日期2019-09-01
卷号12期号:9页码:1229-1236
ISSN号1936-5233
DOI10.1016/j.tranon.2019.06.005
通讯作者Dong, Di(di.dong@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Zhang, Minming(zhangminming@ziu.edu.cn)
英文摘要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 radiomicmodels using CE-CT showed a good predictive performance for preoperative risk stratification of GISTs and hold great potential for personalized clinical decision making.
WOS关键词COMPUTED-TOMOGRAPHY ; PROGNOSTIC-FACTORS ; FOLLOW-UP ; RISK ; DIAGNOSIS ; RECURRENCE ; IMATINIB ; FEATURES ; PET ; SIGNATURE
资助项目Zhejiang Provincial Natural Science Foundation of China[LQ18H180001] ; Zhejiang Medicine and Health Science and Technology Program[2017KY080] ; Zhejiang Medicine and Health Science and Technology Program[2018KY418] ; National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1309100] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81671851] ; Beijing Natural Science Foundation[L182061] ; Bureau of International Cooperation of Chinese Academy of Sciences[173211KYSB20160053] ; Instrument Developing Project of the Chinese Academy of Sciences[YZ201502] ; Youth Innovation Promotion Association CAS[2017175]
WOS研究方向Oncology
语种英语
WOS记录号WOS:000480687900012
出版者ELSEVIER SCIENCE INC
资助机构Zhejiang Provincial Natural Science Foundation of China ; Zhejiang Medicine and Health Science and Technology Program ; National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Bureau of International Cooperation of Chinese Academy of Sciences ; Instrument Developing Project of the Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS
源URL[http://ir.ia.ac.cn/handle/173211/27543]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Dong, Di; Tian, Jie; Zhang, Minming
作者单位1.Zhejiang Univ, Sch Med, Dept Radiol, Affiliated Hosp 2, 88 Jiefang Rd, Hangzhou 310009, Zhejiang, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
4.Zhejiang Univ, Sch Med, Dept Surg Oncol, Affiliated Hosp 2, Hangzhou, Zhejiang, Peoples R China
5.Zhejiang Univ, Sch Med, Dept Pathol, Affiliated Hosp 2, Hangzhou, Zhejiang, Peoples R China
6.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China
7.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Sch Life Sci & Technol, Xian, Shaanxi, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Chao,Li, Hailin,Jiaerken, Yeerfan,et al. Building CT Radiomics-Based Models for Preoperatively Predicting Malignant Potential and Mitotic Count of Gastrointestinal Stromal Tumors[J]. TRANSLATIONAL ONCOLOGY,2019,12(9):1229-1236.
APA Wang, Chao.,Li, Hailin.,Jiaerken, Yeerfan.,Huang, Peiyu.,Sun, Lifeng.,...&Zhang, Minming.(2019).Building CT Radiomics-Based Models for Preoperatively Predicting Malignant Potential and Mitotic Count of Gastrointestinal Stromal Tumors.TRANSLATIONAL ONCOLOGY,12(9),1229-1236.
MLA Wang, Chao,et al."Building CT Radiomics-Based Models for Preoperatively Predicting Malignant Potential and Mitotic Count of Gastrointestinal Stromal Tumors".TRANSLATIONAL ONCOLOGY 12.9(2019):1229-1236.

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来源：自动化研究所

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