A Deep Learning Framework for Predicting Teprotumumab Treatment Response in Thyroid Eye Disease

Purpose: To develop and evaluate a deep learning-based framework for quantifying thyroid eye disease (TED) severity before and after teprotumumab treatment, an insulin-like growth factor-1 receptor inhibitor, and to create a predictive model for forecasting individual patient responses to therapy. Design: A retrospective cohort study was conducted at a single institution, utilizing an image-based deep learning TED severity quantification model and a regression-based prediction model for teprotumumab treatment response. Subjects: One hundred eighty-four patients with a clinical diagnosis of TED and 44 individuals with normal orbital anatomy (controls) used for model training and validation. Additionally, 19 patients with pretreatment and posttreatment imaging were analyzed for treatment response. Methods: ). Main Outcome Measures: Thyroid eye disease severity scores and predicted treatment response. Results: of 0.82 in leave-one-out cross-validation and showed 89% concordance with clinician-assessed improvement. Conclusions: This deep learning framework demonstrates promising performance for objective TED severity quantification and exploratory prediction of teprotumumab treatment response. By integrating clinical and imaging data, this approach provides a proof-of-concept for data-driven tools that may support individualized treatment planning in TED. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.