Our study revealed three main findings. First, all the quantitative parameters of EOMs, LGs, and IF based on Dixon MRI showed significant differences between patients with active and inactive TAO. These findings indicate that the EOMs, LGs, and IF demonstrate potential as target organs for staging TAO. Second, the EOM-SIRmean, LG-SIRmean, and LG-FFmean values were found to be independent predictors of active TAO. Third, compared with a single parameter based on EOMs, a combined model integrating the EOM-SIRmean, LG-SIRmean, and LG-FFmean values could further improve the performance in staging patients with TAO.

The involvement of EOMs is a known disease process in patients with TAO [16, 17]. In this study, we found that the SIRmin/mean/max values of EOMs were significantly greater in active TAOs than those in inactive TAOs, consistent with previous studies [18, 19]. In addition, using the Dixon MRI technique, our study indicated that active TAOs had higher water-related metrics (EOM-WFmean and EOM-WFmin) and lower fat-related parameters (EOM-FFmax and EOM-FFmean) than did inactive TAOs. Previous studies have indicated that the active phase of TAO is dominated by inflammatory responses, while the inactive phase of TAO is dominated by fibrosis, fatty infiltration, and collagen deposition [4, 20]. These mechanisms might explain the elevated water-related metrics in active TAOs and the increased fat-related metrics in inactive TAOs.

Increased orbital fat is another major characteristic of TAO [21]. Previously, Potgieser et al reported that a greater volume of orbital fat is associated with a longer duration of TAO [22]; however, they did not analyze the change in the signal intensity of orbital fat. In our study, the SIRmean/max, FFmean/min, and WFmean/max values of orbital fat differed significantly between active and inactive TAOs. Previous studies have revealed that orbital fat is histologically characterized by lymphocytic infiltration and edema due to the accumulation of hydrophilic interstitial glycosaminoglycans [23]. We suspect that this accumulation is potentially the mechanism underlying the increased SIR and WF values in patients with active TAO.

As LGs are another potential target organ, changes in LGs in patients with TAO have attracted increasing attention [24]. Gagliardo et al reported that patients with right and left active TAO demonstrated significantly greater herniation of the LGs on MRI than in those with inactive TAO [25]. Using the T2 mapping technique, Jiang et al reported that the T2 mapping values of LGs differed significantly between active and inactive TAO. Together with clinical indicators, the T2 mapping technique could effectively stage patients with TAO [26]. In addition, using the diffusion tensor imaging technique, Chen et al reported that the LGs of active TAO showed significantly lower fractional anisotropy and a higher apparent diffusion coefficient than those of inactive TAO [27]. In our study, similar to the change in EOMs, we found that the LGs of active TAOs had higher SIRmean/max and WFmean values and lower FFmean values. The abovementioned pathological changes in EOMs and IFs could help explain these findings. In addition, two LG-based parameters (LG-SIRmean and LG-FFmean) were found to be independently associated with TAO activity. Our results confirmed that the LGs are involved in the TAO process and deserve further study.

According to the binary logistic regression analysis, the EOM-SIRmean, LG-SIRmean, and LG-FFmean values were found to be independent predictors of active TAO. No IF-related metric was found to be an independent variable, possibly due to our study population’s specific sample size and constitution. Furthermore, we constructed a predictive model by integrating the LG-SIRmean and LG-FFmean on the basis of the EOM-SIRmean for staging patients with TAO. The ROC analysis results indicated that the combined model outperformed the EOM-SIRmean alone in both the training and validation cohorts. These results indicated that information on EOMs and other target organs (e.g., LGs and IF) should be integrated and analyzed for staging TAO. Further multicenter studies with larger sample sizes are needed to confirm our results and establish a more robust model for staging patients with TAO in clinical practice.

Our study has several limitations. First, this was a retrospective study from a single center. Further studies with larger study populations and external validation are needed to confirm the findings presented here. Second, the exact pathological state of orbital tissues remains unclear due to the difficulty in obtaining histological samples from patients with TAO, especially those with active disease. Future studies to determine the correlations between imaging metrics and histological changes are needed [28]. Third, this study focused only on the usefulness of the Dixon MRI sequence in staging TAO, and other functional MR sequences (e.g., diffusion or mapping sequences) were not simultaneously scanned. Further studies using machine learning methods to integrate more information from more functional sequences could further improve staging performance.

In conclusion, our study showed that the quantitative parameters of EOMs, LGs, and IF derived from Dixon MR images are useful for differentiating active from inactive TAOs. Integrating multiple parameters from EOMs, LGs, and IF could further improve TAO patient staging.