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Abstract #1402

MRI-Based Radiomics and Deep Learning of Thyroid Eye Disease: One Slice Speaks Volume

Haiyang Zhang1,2,3, Huijie Zhang1,2,3, Jiaxin Li1,2,3, Mengda Jiang4, Xiaofeng Tao4, Hoi Chi Chan1,2,3, Jipeng Li1,2,3, Yinwei Li1,2,3, Jing Sun1,2,3, Xuefei Song1,2,3, Xianqun Fan1,2,3, and Huifang Zhou1,2,3
1Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 2Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China, 3Center for Basic Medical Research and Innovation in Visual System Diseases, Ministry of Education, Shanghai, China, 4Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

Synopsis

Keywords: Diagnosis/Prediction, Diagnosis/Prediction, Thyroid eye disease; T2-weighted imaging; Radiomics analysis; Deep learning

Motivation: Traditional multi-slice MRI segmentation is time-consuming and resource-intensive. This study investigates the potential of single-slice MRI for predicting intravenous glucocorticoid (IVGC) treatment response in thyroid eye disease (TED).

Goal(s): To evaluate whether single-slice MRI can effectively predict treatment responses in TED patients compared to multi-slice models.

Approach: A retrospective study of 127 TED patients treated with IVGC. Radiomics analysis and deep learning algorithm were applied to both single-slice and multi-slice MRI data.

Results: Single-slice models demonstrated comparable performance to multi-slice models, suggesting single-slice MRI as a cost-effective alternative for clinical use of TED.

Impact: This study highlights the feasibility of single-slice MRI as an efficient, cost-effective alternative to multi-slice segmentation for predicting IVGC treatment response in TED patients. It opens avenues for more accessible clinical applications, reducing time and resource requirements while maintaining performance.

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Keywords