ORIGINAL RESEARCH article
Front. Physiol.
Sec. Medical Physics and Imaging
Volume 16 - 2025 | doi: 10.3389/fphys.2025.1599657
This article is part of the Research TopicFoundation Models for Healthcare: Innovations in Generative AI, Computer Vision, Language Models, and Multimodal SystemsView all 12 articles
Research on a machine learning method for predicting discharge time of thyroid cancer patients receiving 131 I treatment: a retrospective study
Provisionally accepted
- First Affiliated Hospital, Nanjing Medical University, Nanjing, China
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Radioactive iodine-131 ( 131 I) based internal irradiation therapy has become one of the main methods for treating thyroid cancer, but patient usually need to be hospitalized after taking 131 I until the residual activity meets the discharge criteria. However, the complex metabolism of 131 I drug in individualized patient may make it difficult to assess when patients would meet discharge criteria, thereby increasing the hospital stay.In this study, some basic data of 1044 thyroid cancer patients received 131 I treatment at the First Affiliated Hospital with Nanjing Medical University from January 2022 to January 2024 are collected. Numerical analysis methods are used to analyze the absorption and metabolism of 131 I drug in different patients and support vector machine (SVM) model is used to predict the discharge time of different patients. Results show that the effective half-life of 131 I in both male and female patients are 10.35 h and 9.64 h, whose residual activity less than 400 MBq after 48 h of taking 131 I. While the effective half-life of 131 I in both male and female patients are 14.07 and 13.47 h for that the residual activity are greater than 400 MBq after 48 h of taking 131 I. Furthermore, a discharge time prediction method based on SVM has been developed and the accuracy and precision of this method in predicting whether a patient could be discharged from the hospital after 48 h of taking the 131 I drug are 88.04% and 96.89%. These results show that the discharge time prediction method could be expected to improve the rotation efficiency of nuclear medicine wards and provide timely treatment for more thyroid cancer patients receiving 131 I treatment in the future.
Keywords: 131 I, thyroid cancer, Effective half-life, Support vector machine, Discharge time
Received: 25 Mar 2025; Accepted: 22 Aug 2025.
Copyright: © 2025 Tian, Zhang, Zhang and Tang. 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: Feng Tian, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
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