CASIA OpenIR
Building CT Radiomics-Based Models for Preoperatively Predicting Malignant Potential and Mitotic Count of Gastrointestinal Stromal Tumors
Wang, Chao1; Li, Hailin2,3; Jiaerken, Yeerfan1; Huang, Peiyu1; Sun, Lifeng4; Dong, Fei1; Huang, Yajing5; Dong, Di2,3; Tian, Jie2,6,7; Zhang, Minming1
Source PublicationTRANSLATIONAL ONCOLOGY
ISSN1936-5233
2019-09-01
Volume12Issue:9Pages:1229-1236
Corresponding AuthorDong, Di(di.dong@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Zhang, Minming(zhangminming@ziu.edu.cn)
AbstractPURPOSE: 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.
DOI10.1016/j.tranon.2019.06.005
WOS KeywordCOMPUTED-TOMOGRAPHY ; PROGNOSTIC-FACTORS ; FOLLOW-UP ; RISK ; DIAGNOSIS ; RECURRENCE ; IMATINIB ; FEATURES ; PET ; SIGNATURE
Indexed BySCI
Language英语
Funding ProjectZhejiang 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]
Funding OrganizationZhejiang 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
WOS Research AreaOncology
WOS SubjectOncology
WOS IDWOS:000480687900012
PublisherELSEVIER SCIENCE INC
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27544
Collection中国科学院自动化研究所
Corresponding AuthorDong, Di; Tian, Jie; Zhang, Minming
Affiliation1.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
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
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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