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
Department of Neurology, Qilu Hospital of Shandong University, Jinan, Shandong, China; Department of Radiology, Zibo Prevention and Treatment hospital for Occupation diseases, Zibo, Shandong, China.
Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, Shandong, China.
Mult Scler Relat Disord. 2025 Mar;95:106315. doi: 10.1016/j.msard.2025.106315. Epub 2025 Feb 24.
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).
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).
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.
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.
本研究旨在开发并验证一种用于鉴别髓鞘少突胶质细胞糖蛋白抗体相关疾病（MOGAD）和水通道蛋白4免疫球蛋白G阳性视神经脊髓炎谱系障碍（AQP4+NMOSD）的放射组学列线图。
我们回顾性分析了来自一个主要队列的数据，该队列由21例MOGAD患者和63例AQP4+NMOSD患者组成，以及一个外部验证队列，其中包括10例MOGAD患者和34例AQP4+NMOSD患者。从矢状位T2加权磁共振图像的颈脊髓和脑干病变中提取放射组学特征。我们通过将放射组学特征与临床数据相结合构建了一个预测模型，并使用校准曲线和决策曲线分析（DCA）评估其性能。
我们开发了一种综合列线图，将临床和放射组学特征相结合，以区分MOGAD和AQP4+NMOSD。列线图的鉴别能力通过受试者操作特征（ROC）曲线下面积（AUC）进行量化，在主要队列中AUC值为0.915（95%CI，0.859-0.970），在验证队列中为0.837（95%CI，0.715-0.959），表明诊断准确性高。校准分析显示模型预测结果与实际结果之间具有良好的一致性。
本研究成功验证了放射组学特征模型，证明其在区分MOGAD和AQP4+NMOSD方面具有卓越性能。该列线图将放射组学特征与脑干和颈髓病变的传统影像学特征相结合，显著增强了鉴别能力。两种模型在提高诊断准确性方面均被证明具有价值，其中放射组学特征贡献最为显著。
