Multiple sclerosis and related disorders · 2025
MRI-based radiomics for differention of aquaporin 4-immunoglobulin G-positive neuromyelitis optic spectrum disorder and anti myelin oligodendrocyte glycoprotein immunoglobulin G-associated disorder.
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Wang Ningning, Chen Wei, Wang Huijun, Yao Yongjie, Li Yuxin, Li Haiqing, Liu Xueling, Liu Zhuyun, Abouzied Ahmed, Jin Xiaodi, Wang Shengjun, Bai Xue, Shan Jingli, Li Anning
Аннотация
OBJECTIVES: This study was designed to develop and validate a radiomic nomogram for the differential diagnosis of myelin oligodendrocyte glycoprotein antibody-related disease (MOGAD) and aquaporin-4 immunoglobulin G-positive neuromyelitis optica spectrum disorder (AQP4+NMOSD).
METHODS: We retrospectively analysed data from a primary cohort consisting of 21 MOGAD and 63 AQP4+NMOSD patients and an external validation cohort comprising 10 MOGAD and 34 AQP4+NMOSD patients. Radiomic features were extracted from lesions of the cervical spinal cord and brainstem from sagittal T2-weighted MR images. We constructed a prediction model by integrating radiomic features with clinical data and evaluated its performance using calibration curves and decision curve analysis (DCA).
RESULTS: We developed a comprehensive nomogram that combines clinical and radiomic features to distinguish MOGAD from AQP4+NMOSD. The discriminative ability of the nomogram was quantified by the area under the receiver operating characteristic (ROC) curve (AUC), achieving values of 0.915 (95 % CI, 0.859-0.970) in the primary cohort and 0.837 (95 % CI, 0.715-0.959) in the validation cohort, indicating high diagnostic accuracy. The calibration analyses showed good concordance between the model predicted and actual outcomes.
CONCLUSIONS: This study successfully validated the radiomic feature model, demonstrating its superior performance in differentiating MOGAD from AQP4+NMOSD. The nomogram, integrating radiomic features with conventional imaging characteristics of brainstem and cervical cord lesions, significantly enhanced differentiation capability. Both models proved valuable in improving diagnostic accuracy, with radiomic features contributing most significantly.
METHODS: We retrospectively analysed data from a primary cohort consisting of 21 MOGAD and 63 AQP4+NMOSD patients and an external validation cohort comprising 10 MOGAD and 34 AQP4+NMOSD patients. Radiomic features were extracted from lesions of the cervical spinal cord and brainstem from sagittal T2-weighted MR images. We constructed a prediction model by integrating radiomic features with clinical data and evaluated its performance using calibration curves and decision curve analysis (DCA).
RESULTS: We developed a comprehensive nomogram that combines clinical and radiomic features to distinguish MOGAD from AQP4+NMOSD. The discriminative ability of the nomogram was quantified by the area under the receiver operating characteristic (ROC) curve (AUC), achieving values of 0.915 (95 % CI, 0.859-0.970) in the primary cohort and 0.837 (95 % CI, 0.715-0.959) in the validation cohort, indicating high diagnostic accuracy. The calibration analyses showed good concordance between the model predicted and actual outcomes.
CONCLUSIONS: This study successfully validated the radiomic feature model, demonstrating its superior performance in differentiating MOGAD from AQP4+NMOSD. The nomogram, integrating radiomic features with conventional imaging characteristics of brainstem and cervical cord lesions, significantly enhanced differentiation capability. Both models proved valuable in improving diagnostic accuracy, with radiomic features contributing most significantly.