ArticleFrontiers in endocrinology2025
Prediction of total iodine dose of I-131 therapy for Graves' hyperthyroidism achieved remission status: a random forest regressor model approach to assess treatment efficacy.
Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: Graves' hyperthyroidism (GH) presents significant challenges in optimizing Iodine-131 (I-131) therapy, largely due to the variability in patient responses and the limitations of traditional dosing methods. This study aimed to develop and validate a Random Forest Regressor (RFR) model to predict the effective total iodine dose (TID) necessary to achieve remission in patients with GH, thereby enhancing precision and individualization in patient management. Methods: A retrospective cohort study design was employed, analyzing comprehensive clinical data from 975 adult GH patients who achieved remission and underwent Results: Key predictive variables identified through LASSO-Gaussian regression analysis were gender, IDPG, FT4, RAIU24h, Teff, and thyroid weight. The RFR model demonstrated strong predictive performance, achieving an R-squared value of 0.858 ± 0.05 on the validation set and 0.838 on the temporal validation set, indicating its high capability to explain the variance in TID. SHAP analysis provided crucial insights into the contribution of each feature, highlighting, for example, that high FT4, Teff, and thyroid weight were primary positive contributors to the predicted TID, while RAIU24h offered a compensatory negative contribution. Conclusion: In conclusion, this study successfully developed and validated an RFR model that accurately predicts the TID for GH patients achieved remission. By integrating multi-dimensional features and providing interpretability through SHAP values, this model offers a sophisticated approach to dose personalization. This advancement has the potential to significantly improve
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