Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
| 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 Publication | EUROPEAN JOURNAL OF RADIOLOGY
 |
| ISSN | 0720-048X |
| 2019-12-01 | |
| Volume | 121Pages:9 |
| Corresponding Author | Feng, Feng(fengfeng@vip.163.com) ; Tian, Jie(jie.tian@ia.ac.cn) ; Feng, Ming(pumchfengming@163.com) |
| Abstract | 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. |
| Keyword | Invasive functional pituitary adenoma Treatment response Magnetic resonance imaging Radiomics |
| DOI | 10.1016/j.ejrad.2019.108647 |
| WOS Keyword | CUSHINGS-SYNDROME ; REMISSION ; DIAGNOSIS ; CONSENSUS ; NOMOGRAM ; SURVIVAL ; THERAPY ; SURGERY ; CANCER |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] ; 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 Organization | 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 |
| WOS Research Area | Radiology, Nuclear Medicine & Medical Imaging |
| WOS Subject | Radiology, Nuclear Medicine & Medical Imaging |
| WOS ID | WOS:000500465900015 |
| Publisher | ELSEVIER IRELAND LTD |
| Sub direction classification | 医学影像处理与分析 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/29402 |
| Collection | 中国科学院分子影像重点实验室 |
| Corresponding Author | Feng, Feng; Tian, Jie; Feng, Ming |
| Affiliation | 1.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 Affilication | Institute 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. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment