Shuaitong Zhang1,2, Guidong Song3, Yali Zang4,5, Jian Jia3, Chao Wang1,6, Chuzhong Li3,7, Jie Tian1,2,8, Di Dong1,2, Yazhuo Zhang9,10. 1. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. 2. University of Chinese Academy of Sciences, Beijing, 100080, China. 3. Beijing Neurosurgical Institute, Capital Medical University, Beijing, 100050, China. 4. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. zangyali@fingerpass.net.cn. 5. University of Chinese Academy of Sciences, Beijing, 100080, China. zangyali@fingerpass.net.cn. 6. Electrical Engineering School, Harbin University of Science & Technology, Harbin, 150040, China. 7. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China. 8. CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. 9. Beijing Neurosurgical Institute, Capital Medical University, Beijing, 100050, China. zyz2004520@yeah.net. 10. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China. zyz2004520@yeah.net.
Abstract
PURPOSE: To make individualised preoperative prediction of non-functioning pituitary adenoma (NFPAs) subtypes between null cell adenomas (NCAs) and other subtypes using a radiomics approach. METHODS: 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. RESULTS: 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). CONCLUSION: 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. KEY POINTS: • 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.
PURPOSE: To make individualised preoperative prediction of non-functioning pituitary adenoma (NFPAs) subtypes between null cell adenomas (NCAs) and other subtypes using a radiomics approach. METHODS: 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. RESULTS: 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). CONCLUSION: 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. KEY POINTS: • 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.
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