CASIA OpenIR  > 中国科学院分子影像重点实验室
Development and validation of an MRI-based radiomic signature for the preoperative prediction of treatment response in patients with invasive functional pituitary adenoma
Fan, Yanghua1; Liu, Zhenyu2,3; Hou, Bo4; Li, Longfei5; Liu, Xiaohai1; Liu, Zehua5; Wang, Renzhi1; Lin, Yusong5; Feng, Feng4; Tian, Jie2,3,6,7; Feng, Ming1
Source PublicationEUROPEAN JOURNAL OF RADIOLOGY
ISSN0720-048X
2019-12-01
Volume121Pages:9
Corresponding AuthorFeng, Feng(fengfeng@vip.163.com) ; Tian, Jie(jie.tian@ia.ac.cn) ; Feng, Ming(pumchfengming@163.com)
AbstractPurpose: 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.
KeywordInvasive functional pituitary adenoma Treatment response Magnetic resonance imaging Radiomics
DOI10.1016/j.ejrad.2019.108647
WOS KeywordCUSHINGS-SYNDROME ; REMISSION ; DIAGNOSIS ; CONSENSUS ; NOMOGRAM ; SURVIVAL ; THERAPY ; SURGERY ; CANCER
Indexed BySCI
Language英语
Funding ProjectGraduate 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] ; 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]
Funding OrganizationGraduate 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
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000500465900015
PublisherELSEVIER IRELAND LTD
Sub direction classification医学影像处理与分析
Citation statistics
Cited Times:24[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/29402
Collection中国科学院分子影像重点实验室
Corresponding AuthorFeng, Feng; Tian, Jie; Feng, Ming
Affiliation1.Chinese Acad Med Sci & Peking Union Med Coll, Peking Union Med Coll Hosp, Dept Neurosurg, Beijing 100032, Peoples R China
2.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100080, Peoples R China
4.Chinese Acad Med Sci & Peking Union Med Coll, Peking Union Med Coll Hosp, Dept Radiol, Beijing 100032, 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
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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