Yanghua Fan1, Zhenyu Liu2, Bo Hou3, Longfei Li4, Xiaohai Liu1, Zehua Liu4, Renzhi Wang1, Yusong Lin4, Feng Feng5, Jie Tian6, Ming Feng7. 1. Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100032, China. 2. Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100080, China. 3. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100032, China. 4. Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, Henan, China. 5. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100032, China. Electronic address: fengfeng@vip.163.com. 6. Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100080, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shanxi, 710126, China. Electronic address: jie.tian@ia.ac.cn. 7. Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100032, China. Electronic address: 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.
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. IFPApatients 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.
Authors: Paul Windisch; Carole Koechli; Susanne Rogers; Christina Schröder; Robert Förster; Daniel R Zwahlen; Stephan Bodis Journal: Cancers (Basel) Date: 2022-05-27 Impact factor: 6.575
Authors: Darius Kalasauskas; Michael Kosterhon; Naureen Keric; Oliver Korczynski; Andrea Kronfeld; Florian Ringel; Ahmed Othman; Marc A Brockmann Journal: Cancers (Basel) Date: 2022-02-07 Impact factor: 6.639