Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery
To make individualised preoperative prediction of non-functioning pituitary adenoma (NFPAs) subtypes between null cell adenomas (NCAs) and other subtypes using a radiomics approach.
We enrolled 112 patients (training set: n = 75; test set: n = 37) with complete T1-weighted magnetic resonance imaging (MRI) and contrast-enhanced T1-weighted MRI (CE-T1). A total of 1482 quantitative imaging features were extracted from T1 and CE-T1 images. Support vector machine trained a predictive model that was validated using a receiver operating characteristics (ROC) analysis on an independent test set. Moreover, a nomogram was constructed incorporating clinical characteristics and the radiomics signature for individual prediction.
T1 image features yielded area under the curve (AUC) values of 0.8314 and 0.8042 for the training and test sets, respectively, while CE-T1 image features provided no additional contribution to the predictive model. The nomogram incorporating sex and the T1 radiomics signature yielded good calibration in the training and test sets (concordance index (CI) = 0.854 and 0.857, respectively).
This study focused on the preoperative prediction of NFPA subtypes between NCAs and others using a radiomics approach. The developed model yielded good performance, indicating that radiomics had good potential for the preoperative diagnosis of NFPAs.
• MRI may help in the pre-operative diagnosis of NFPAs subtypes
• Retrospective study showed T1-weighted MRI more useful than CE-T1 in NCAs diagnosis
• Treatment decision making becomes more individualised
• Radiomics approach had potential for classification of NFPAs
KeywordsNon-functioning pituitary adenomas Null cell adenomas Radiomics Support vector machine Nomograms
Non-functioning pituitary adenomas
Null cell adenomas
Support vector machine
Area under the curve
Receiver operating characteristic
Inter-observer correlation coefficients
Bayesian information criterion
Net reclassification improvement
This study has received funding by National Key Research and Development Program of China (2017YFA0205200, 2017YFC1308700, 2106YFC0103702, 2016YFA0201401, 2017YFC1308701, 2017YFC1309100, 2016CZYD0001), National Natural Science Foundation of China (81227901, 81527805, 61231004, 81501616, 81671851), Beijing Municipal Science & Technology Commission (Z161100002616022, Z171100000117023), the Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-SW-STS-160), the International Innovation Team of CAS (20140491524), the Instrument Developing Project of the Chinese Academy of Sciences (YZ201502) and National High Technology Research and Development Program of China (2015AA020504).
Compliance with ethical standards
The scientific guarantor of this publication is Jie Tian.
Conflict of interest
The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and biometry
Dr. Di Dong from the University of Chinese Academy of Sciences, who is one of the authors, has significant statistical expertise.
Written informed consent was waived by the Institutional Review Board of Beijing Tiantan Hospital Affiliated to Capital Medical University.
Institutional Review Board approval was obtained.
• diagnostic or prognostic study
• performed at one institution