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A Non-invasive Radiomic Method Using F-18-FDG PET Predicts Isocitrate Dehydrogenase Genotype and Prognosis in Patients With Glioma

文献类型：期刊论文

作者Li, Longfei5,6; Mu, Wei6; Wang, Yaning7; Liu, Zhenyu6; Liu, Zehua5,6; Wang, Yu7; Ma, Wenbin7; Kong, Ziren7; Wang, Shuo6; Zhou, Xuezhi6,8
刊名FRONTIERS IN ONCOLOGY
出版日期2019-11-14
卷号9页码:11
ISSN号2234-943X
关键词F-18-FDG PET radiomics glioma isocitrate dehydrogenase non-invasive prediction
DOI10.3389/fonc.2019.01183
通讯作者Cheng, Xin(pumch_chengxin@126.com) ; Lin, Yusong(yslin@ha.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要Purpose: We aimed to analyze F-18-fluorodeoxyglucose positron emission tomography (F-18-FDG PET) images via the radiomic method to develop a model and validate the potential value of features reflecting glioma metabolism for predicting isocitrate dehydrogenase (IDH) genotype and prognosis. Methods: PET images of 127 patients were retrospectively analyzed. A series of quantitative features reflecting the metabolic heterogeneity of the tumors were extracted, and a radiomic signature was generated using the support vector machine method. A combined model that included clinical characteristics and the radiomic signature was then constructed by multivariate logistic regression to predict the IDH genotype status, and the model was evaluated and verified by receiver operating characteristic (ROC) curves and calibration curves. Finally, Kaplan-Meier curves and log-rank tests were used to analyze overall survival (OS) according to the predicted result. Results: The generated radiomic signature was significantly associated with IDH genotype (p < 0.05) and could achieve large areas under the ROC curve of 0.911 and 0.900 on the training and validation cohorts, respectively, with the incorporation of age and type of tumor metabolism. The good agreement of the calibration curves in the validation cohort further validated the efficacy of the constructed model. Moreover, the predicted results showed a significant difference in OS between high- and low-risk groups (p < 0.001). Conclusions: Our results indicate that the F-18-FDG metabolism-related features could effectively predict the IDH genotype of gliomas and stratify the OS of patients with different prognoses.
WOS关键词GRADE ; CLASSIFICATION ; ASTROCYTOMAS ; DIAGNOSIS ; ONCOLOGY ; SURVIVAL ; FEATURES ; SYSTEM ; ADULTS
资助项目National Natural Science Foundation of China[81772009] ; National Natural Science Foundation of China[81922040] ; National Natural Science Foundation of China[81772012] ; National Natural Science Foundation of China[81227901] ; National Key Research and Development Plan of China[2017YFA0205200] ; National Key Research and Development Plan of China[2016YFA0100900] ; National Key Research and Development Plan of China[2016YFA0100902] ; Scientific and Technological Research Project of Henan Province[182102310162] ; Beijing Natural Science Foundation[7182109] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences[2016-I2M-2-001] ; Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences[2018-I2M-3-001] ; Fundamental Research Funds for the Central Universities[3332018029] ; Youth Innovation Promotion Association CAS[2019136]
WOS研究方向Oncology
语种英语
出版者FRONTIERS MEDIA SA
WOS记录号WOS:000501792200001
资助机构National Natural Science Foundation of China ; National Key Research and Development Plan of China ; Scientific and Technological Research Project of Henan Province ; Beijing Natural Science Foundation ; Chinese Academy of Sciences ; Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences ; Fundamental Research Funds for the Central Universities ; Youth Innovation Promotion Association CAS
源URL[http://ir.ia.ac.cn/handle/173211/29329]  
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Cheng, Xin; Lin, Yusong; Tian, Jie
作者单位1.Xian Polytech Univ, Sch Elect & Informat, Xian, Shaanxi, Peoples R China
2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
4.Zhengzhou Univ, Sch Software, Zhengzhou, Henan, Peoples R China
5.Zhengzhou Univ, Collaborat Innovat Ctr Internet Healthcare, Zhengzhou, Henan, Peoples R China
6.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
7.Chinese Acad Med Sci & Peking Union Med Coll, Peking Union Med Coll Hosp, Dept Neurosurg, Beijing, Peoples R China
8.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China
9.Chinese Acad Med Sci & Peking Union Med Coll, Peking Union Med Coll Hosp, Dept Nucl Med, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Li, Longfei,Mu, Wei,Wang, Yaning,et al. A Non-invasive Radiomic Method Using F-18-FDG PET Predicts Isocitrate Dehydrogenase Genotype and Prognosis in Patients With Glioma[J]. FRONTIERS IN ONCOLOGY,2019,9:11.
APA Li, Longfei.,Mu, Wei.,Wang, Yaning.,Liu, Zhenyu.,Liu, Zehua.,...&Tian, Jie.(2019).A Non-invasive Radiomic Method Using F-18-FDG PET Predicts Isocitrate Dehydrogenase Genotype and Prognosis in Patients With Glioma.FRONTIERS IN ONCOLOGY,9,11.
MLA Li, Longfei,et al."A Non-invasive Radiomic Method Using F-18-FDG PET Predicts Isocitrate Dehydrogenase Genotype and Prognosis in Patients With Glioma".FRONTIERS IN ONCOLOGY 9(2019):11.

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