Risk prediction of dysthyroid optic neuropathy based on CT imaging features combined the bony orbit with the soft tissue structures - Cheng, Shengnan; Ming, Yangcan; Hu, Mang; Zhang, Yan; Jiang, Fagang; Wang, Xinghua; Xiao, Zefeng (Frontiers in Medicine, 2022)
Purpose: To analyze CT imaging features of dysthyroid optic neuropathy (DON) patients retrospectively, and deduce a more appropriate predictive model. Methods: The CT scans and medical records of 60 patients with clinically proved Graves’ ophthalmopathy (GO) with (26 women and 10 men) and without DON (16 women and 8 men) were retrospectively reviewed, and 20 age- and sex-matched control participants (12 women and 8 men) were enrolled consecutively. The bony orbit (orbital rim angle (ORA), medial and lateral orbital wall angles (MWA and LWA), orbital apex angle (OAA), and length of the lateral orbital wall (LWL)), and the soft tissue structures (maximum extraocular muscle diameters (Max EOMD), muscle diameter index (MDI), medial and lateral rectus bulk from inter-zygomatic line (MRIZL and LRIZL), proptosis, intraorbital optic nerve stretching length (IONSL), superior ophthalmic vein diameter (SOVD), apical crowding, and presence of intracranial fat prolapse) were assessed on a clinical workstation. The CT features among groups were compared, and multivariate logistic regression analysis was performed to evaluate the predictive features of DON. Results: All bony orbital angles indicators, except ORA (p=0.461), were statistical different among the three groups (all p＜0.05). The MWA, LWA, OAA, and LWL were larger in the orbits with DON group than in the orbits without DON group (all p＜0.05). The MDI, MRIZL, proptosis, IONSL, and SOVD were statistically significantly different among three groups (all p＜0.05), in which the orbits with DON group were significantly higher than the orbits without DON group and control group. The apical crowding was more severe in the orbits with DON group than in the orbits without DON group (p=0.000). There were no significant differences in the LRIZL and the presence of intracranial fat prolapse (all p＞0.05). The multivariate regression analysis showed that the MWA, MDI and SOVD were the independent factors predictive of DON. The sensitivity and the specificity for the presence of DON by combining these three indicators were 89% and 83%. Conclusion: Bone and soft tissue CT features are useful in the risk prediction of DON, especially the MWA, MDI and SOVD were the independent factors predictive of DON.