Nomogram Based on High‐Resolution Vessel Wall MRI Features to Differentiate Moyamoya Disease From Atherosclerosis‐Associated Moyamoya Vasculopathy

Background The angiographic features of moyamoya disease (MMD) and atherosclerosis‐associated moyamoya vasculopathy (AS‐MMV) are similar, but the etiology and clinical treatment strategies are different. Differentiating MMD from AS‐MMV helps to choose the appropriate treatment. Purpose To investigate the feasibility of a nomogram based on high‐resolution vessel wall (HR‐VWI) MRI features to differentiate MMD from AS‐MMV. Study Type Retrospective. Subjects One hundred and two patients with MMD (N = 52) or AS‐MMV (N = 50) in the training cohort (9–72 years; 54 females) and 70 patients with MMD (N = 42) or AS‐MMV (N = 28) in the validation cohort (7–69 years; 33 females). Field Strength/Sequence 3‐T, three‐dimensional time‐of‐flight MR angiography (3D‐TOF‐MRA), spin echo high‐resolution 3D T1‐weighted imaging (3D‐T1WI), 3D T2‐weighted imaging (3D‐T2WI), and contrast‐enhanced 3D‐T1WI. Assessment Image assessment was performed by three neuroradiologists (with 10, 15, and 18 years of experience). Demographic characteristic and image features were evaluated and compared. Independent factors of MMD were screened to construct a nomogram model in the training cohort. The validation cohort was used to validated its generality. Statistical Tests Interclass correlation coefficient (ICC), kappa, t ‐test, χ 2 test, receiver operating characteristic (ROC) curve, area under the curve (AUC), calibration curve and concordance index (C‐index). A P ‐value <0.05 was considered statistically significant. Results Significant differences were observed between MMD and AS‐MMV in terms of age, vessel outer diameter, vessel wall thickening pattern, maximum thickness, dot sign, and anterior cerebral artery (ACA) involved. Age, outer diameter, dot sign, and ACA involved were independent factors. The C‐index was 0.886 in the training cohort and 0.859 in the validation cohort. The ROC demonstrated high diagnostic efficacy with an AUC of 0.884 in the training cohort and 0.857 in the validation cohort. Data Conclusion A nomogram model based on age, vessel outer diameter, dot sign and ACA involved may effectively distinguish MMD from AS‐MMV with good reliability and accuracy. Evidence Level 4 Technical Efficacy Stage 2