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ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Thyroid Endocrinology
Volume 15 - 2024 |
doi: 10.3389/fendo.2024.1425101
This article is part of the Research Topic Levothyroxine Therapy in Patients with Hypothyroidism: Volume II View all 10 articles
A Predictive Model for L-T4 Dose in Postoperative DTC after RAI Therapy and Its Clinical Validation in Two Institutions
Provisionally accepted- 1 Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- 2 Tianjin Cancer Hospital Airport Hospital, Tianjin, China
- 3 Qilu Hospital, Shandong University, Jinan, Shandong Province, China
Purpose Purpose Purpose 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 Methods Methods Methods: : : : A total of 266 DTC patients who received RAI 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 using machine learning random forest method, and a total of eight regression models were established to assess their performance in prediction of L-T4 dose after RAI therapy; The optimal model was 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 based on empirical administration (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, the model significantly shorten the time (days) to achieve target TSH level (62.61±58.78 vs 115.50±71.40).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.
Keywords: Differentiated thyroid cancer, Radioactive iodine therapy, Levothyroxine, Thyroid stimulating hormone, machine learning
Received: 29 Apr 2024; Accepted: 30 Jul 2024.
Copyright: © 2024 Liu, Wang, Yue, Yin, Tong, Fu, Ma, Li, Li, Liu, Su, Yang, Li, Xu and Dai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Dong Dai, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
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