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Abstract #0955

Prediction of Graves’ ophthalmopathy activity via histogram analysis of T2WI

Zhang Yingcong1, Bi Qiu2, Gong Xiarong2, and Wu Yunzhu3
1College of Medicine,Kunming University of Science and Technology,Kunming,China;, Kunming, China, 2Department of MRI,the First People’s Hospital of Yunnan Province(the Affiliated Hospital of Kunming University of Science and Technology),Kunming, Kunming, China, 3MR Research Collaboration Team, Siemens Healthineers Ltd., Shanghai, China, Shanghai, China

Synopsis

Keywords: Software Tools, Analysis/Processing

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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Keywords