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Development and validation of an MRI-based radiomic signature for the preoperative prediction of treatment response in patients with invasive functional pituitary adenoma

文献类型：期刊论文

作者Fan, Yanghua2; Liu, Zhenyu3,4; Hou, Bo1; Li, Longfei5; Liu, Xiaohai2; Liu, Zehua5; Wang, Renzhi2; Lin, Yusong5; Feng, Feng1; Tian, Jie3,4,6,7
刊名EUROPEAN JOURNAL OF RADIOLOGY
出版日期2019-12-01
卷号121页码:9
关键词Invasive functional pituitary adenoma Treatment response Magnetic resonance imaging Radiomics
ISSN号0720-048X
DOI10.1016/j.ejrad.2019.108647
通讯作者Feng, Feng(fengfeng@vip.163.com) ; Tian, Jie(jie.tian@ia.ac.cn) ; Feng, Ming(pumchfengming@163.com)
英文摘要Purpose: The preoperative prediction of treatment response is important for determining individual treatment strategies for invasive functional pituitary adenoma (IFPA). This study aimed to develop and validate a magnetic resonance imaging (MRI)-based radiomic signature for preoperative prediction of treatment response in IFPA. Method: One hundred and sixty-three patients with IFPA were enrolled and divided into primary (n= 108) and validation cohorts (n= 55) according to time point. IFPA patients were divided into remission and non-remission according to postoperative hormone levels. Radiomic features were extracted from their MR images and a radiomic signature was built using a support vector machine. Subsequently, multivariable logistic regression analysis was used to select the most informative clinical features, and a radiomic model incorporating the radiomic signature and selected clinical features was constructed and used as the final predictive model. Results: Seven radiomic features were selected to construct the radiomic signature, which achieved an area under the curve (AUC) of 0.834 and 0.808 on the primary and validation cohorts respectively. The radiomic model incorporating the radiomic signature and Knosp grade showed good discrimination abilities and calibration, with AUCs of 0.832 and 0.811 for the primary and validation cohorts respectively. The radiomic signature and radiomic model better estimated the treatment responses of patients with IFPA than our clinical features model. Decision curve analysis showed the radiomic model was clinically useful. Conclusions: This radiomic model may help neurosurgeons predict the treatment responses of patients with IFPA before surgery and determine individual treatment strategies.
WOS关键词CUSHINGS-SYNDROME ; REMISSION ; DIAGNOSIS ; CONSENSUS ; NOMOGRAM ; SURVIVAL ; THERAPY ; SURGERY ; CANCER
资助项目Graduate Innovation Fund of Peking Union Medical College[2018-1002-01-10] ; Chinese Academy of Medical Sciences[2017-I2M-3-014] ; National Natural Science Foundation of China[81922040] ; National Natural Science Foundation of China[81772012] ; National Natural Science Foundation of China[81772009] ; Beijing Natural Science Foundation[7182137] ; Beijing Natural Science Foundation[7182109] ; Scientific and Technological Research Project of Henan Province[182102310162]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000500465900015
出版者ELSEVIER IRELAND LTD
资助机构Graduate Innovation Fund of Peking Union Medical College ; Chinese Academy of Medical Sciences ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Scientific and Technological Research Project of Henan Province
源URL[http://ir.ia.ac.cn/handle/173211/29401]  
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Feng, Feng; Tian, Jie; Feng, Ming
作者单位1.Chinese Acad Med Sci & Peking Union Med Coll, Peking Union Med Coll Hosp, Dept Radiol, Beijing 100032, Peoples R China
2.Chinese Acad Med Sci & Peking Union Med Coll, Peking Union Med Coll Hosp, Dept Neurosurg, Beijing 100032, Peoples R China
3.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100080, Peoples R China
5.Zhengzhou Univ, Collaborat Innovat Ctr Internet Healthcare, Zhengzhou 450052, Henan, Peoples R China
6.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing 100191, Peoples R China
7.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian 710126, Shanxi, Peoples R China
推荐引用方式
GB/T 7714		 Fan, Yanghua,Liu, Zhenyu,Hou, Bo,et al. Development and validation of an MRI-based radiomic signature for the preoperative prediction of treatment response in patients with invasive functional pituitary adenoma[J]. EUROPEAN JOURNAL OF RADIOLOGY,2019,121:9.
APA Fan, Yanghua.,Liu, Zhenyu.,Hou, Bo.,Li, Longfei.,Liu, Xiaohai.,...&Feng, Ming.(2019).Development and validation of an MRI-based radiomic signature for the preoperative prediction of treatment response in patients with invasive functional pituitary adenoma.EUROPEAN JOURNAL OF RADIOLOGY,121,9.
MLA Fan, Yanghua,et al."Development and validation of an MRI-based radiomic signature for the preoperative prediction of treatment response in patients with invasive functional pituitary adenoma".EUROPEAN JOURNAL OF RADIOLOGY 121(2019):9.

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来源：自动化研究所

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