1. A predictive model for L-T4 dose in postoperative DTC after RAI therapy and its clinical validation in two institutions.
- Author
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Jian-Jing Liu, Zi-Yang Wang, Yuan-Fang Yue, Guo-Tao Yin, Li-Na Tong, Jie Fu, Xiao-Ying Ma, Yan Li, Xue-Yao Liu, Li-Bo Zhang, Qian Su, Zhao Yang, Xiao-Feng Li, Wen-Gui Xu, and Dong Dai
- Subjects
IODINE isotopes ,THYROTROPIN ,BODY surface area ,THYROID cancer ,LONGITUDINAL method - Abstract
Purpose: To develop a predictive model using machine learning for levothyroxine (L-T4) dose selection in patients with differentiated thyroid cancer (DTC) after resection and radioactive iodine (RAI) therapy and to prospectively validate the accuracy of the model in two institutions. Methods: Atotal of266DTCpatientswhoreceivedRAI therapy after thyroidectomy and achieved target thyroid stimulating hormone (TSH) level were included in this retrospective study. Sixteen clinical and biochemical characteristics that could potentially influence the L-T4 dose were collected; Significant features correlated with L-T4 dose were selected usingmachine learning randomforestmethod, and a total of eight regression models were established to assess their performance in prediction of L-T4 dose after RAI therapy; The optimalmodelwas validated through a two-center prospective study (n=263). Results: Six significant clinical and biochemical features were selected, including body surface area (BSA), weight, hemoglobin (HB), height, body mass index (BMI), and age. Cross-validation showed that the support vector regression (SVR) model was with the highest accuracy (53.4%) for prediction of L-T4 dose among the established eight models. In the two-center prospective validation study, a total of 263 patients were included. The TSH targeting rate based on constructed SVR model were dramatically higher than that basedonempiricaladministration (Rate 1 (first rate):52.09%(137/263) vs 10.53% (28/266); Rate 2 (cumulative rate): 85.55% (225/263) vs 53.38% (142/266)). Furthermore, themodel significantly shortens thetime (days) toachieve targetTSHlevel (62.61 ± 58.78 vs 115.50 ± 71.40). Conclusions: The constructed SVR model can effectively predict the L-T4 dose for postoperative DTC after RAI therapy, thus shortening the time to achieve TSH target level and improving the quality of life for DTC patients. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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