Logistic regression identifies key predictors of Graves' ophthalmopathy activity using T2WI histogram analysis, suggesting enhanced clinical decision-making.
Motivation: Accurate prediction of Graves' ophthalmopathy (GO) activity is crucial for effective management. Goal(s): This study aimed to evaluate the utility of T2WI histogram analysis in predicting GO activity. Approach: One hundred and eighteen participants with clinically diagnosed GO were analyzed by comparing the histogram parameters of extraocular muscles (EOMs) in active (36) and inactive (82) GO groups. Logistic regression and ROC curve analyses were used to identify predictors. Results: The active GO participants showed greater EOM signal intensity and volume. Key predictors included superior muscle group SImax and SISD, achieving an ROC AUC of 0.872 for predictive performance. Impact: T2WI histogram analysis is reliable for predicting GO activity, enhancing clinical decision-making, and providing treatment personalization.
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Yingcong et al. (2025) studied this question.
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