Xiaoxiao Ma1,2, Liwen Zhang2,3, Dehui Huang4, Jinhao Lyu1, Mengjie Fang2, Jianxing Hu1, Yali Zang2, Dekang Zhang1, Hang Shao5, Lin Ma1, Jie Tian2, Di Dong2, Xin Lou1. 1. Department of Radiology, Chinese PLA General Hospital, Beijing, China. 2. CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. 3. University of Chinese Academy of Sciences, School of Artificial Intelligence, Beijing, China. 4. Department of Neurology, Chinese PLA General Hospital, Beijing, China. 5. Automation Department, Tsinghua University, Beijing, China.
Abstract
BACKGROUND: Precise diagnosis and early appropriate treatment are of importance to reduce neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS) morbidity. Distinguishing NMOSD from MS based on clinical manifestations and neuroimaging remains challenging. PURPOSE: To investigate radiomic signatures as potential imaging biomarkers for distinguishing NMOSD from MS, and to develop and validate a diagnostic radiomic-signature-based nomogram for individualized disease discrimination. STUDY TYPE: Retrospective, cross-sectional study. SUBJECTS: Seventy-seven NMOSD patients and 73 MS patients. FIELD STRENGTH/SEQUENCE: 3T/T2 -weighted imaging. ASSESSMENT: Eighty-eight patients and 62 patients were respectively enrolled in the primary and validation cohorts. Quantitative radiomic features were automatically extracted from lesioned regions on T2 -weighted imaging. A least absolute shrinkage and selection operator analysis was used to reduce the dimensionality of features. Finally, we constructed a radiomic nomogram for disease discrimination. STATISTICAL TESTS: Features were compared using the Mann-Whitney U-test with a nonnormal distribution. We depicted the nomogram on the basis of the results of the logistic regression using the rms package in R. The Hmisc package was used to investigate the performance of the nomogram via Harrell's C-index. RESULTS: A total of 273 quantitative radiomic features were extracted from lesions. A multivariable analysis selected 11 radiomic features and five clinical features to be included in the model. The radiomic signature (P < 0.001 for both the primary and validation cohorts) showed good potential for building a classification model for disease discrimination. The area under the receiver operating characteristic curve was 0.9880 for the training cohort and 0.9363 for the validation cohort. The nomogram exhibited good discrimination, a concordance index of 0.9363, and good calibration in the primary cohort. The nomogram showed similar discrimination, concordance (0.9940), and calibration in the validation cohort. DATA CONCLUSION: The diagnostic radiomic-signature-based nomogram has potential utility for individualized disease discrimination of NMOSD from MS in clinical practice. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1113-1121.
BACKGROUND: Precise diagnosis and early appropriate treatment are of importance to reduce neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS) morbidity. Distinguishing NMOSD from MS based on clinical manifestations and neuroimaging remains challenging. PURPOSE: To investigate radiomic signatures as potential imaging biomarkers for distinguishing NMOSD from MS, and to develop and validate a diagnostic radiomic-signature-based nomogram for individualized disease discrimination. STUDY TYPE: Retrospective, cross-sectional study. SUBJECTS: Seventy-seven NMOSD patients and 73 MS patients. FIELD STRENGTH/SEQUENCE: 3T/T2 -weighted imaging. ASSESSMENT: Eighty-eight patients and 62 patients were respectively enrolled in the primary and validation cohorts. Quantitative radiomic features were automatically extracted from lesioned regions on T2 -weighted imaging. A least absolute shrinkage and selection operator analysis was used to reduce the dimensionality of features. Finally, we constructed a radiomic nomogram for disease discrimination. STATISTICAL TESTS: Features were compared using the Mann-Whitney U-test with a nonnormal distribution. We depicted the nomogram on the basis of the results of the logistic regression using the rms package in R. The Hmisc package was used to investigate the performance of the nomogram via Harrell's C-index. RESULTS: A total of 273 quantitative radiomic features were extracted from lesions. A multivariable analysis selected 11 radiomic features and five clinical features to be included in the model. The radiomic signature (P < 0.001 for both the primary and validation cohorts) showed good potential for building a classification model for disease discrimination. The area under the receiver operating characteristic curve was 0.9880 for the training cohort and 0.9363 for the validation cohort. The nomogram exhibited good discrimination, a concordance index of 0.9363, and good calibration in the primary cohort. The nomogram showed similar discrimination, concordance (0.9940), and calibration in the validation cohort. DATA CONCLUSION: The diagnostic radiomic-signature-based nomogram has potential utility for individualized disease discrimination of NMOSD from MS in clinical practice. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1113-1121.