A Subregional Prediction Model for Radiation-induced Hypothyroidism

Overview

Journal BMC Med Imaging

Publisher Biomed Central

Specialty Radiology

Date 2025 Mar 4

PMID 40038595

Authors

Wenting Ren

Ziqi Pan

Kuo Men

Bin Liang

Qingfeng Xu

Junlin Yi

Jianrong Dai

Affiliations

Soon will be listed here.

Abstract

Background: Considering the potential association between radiation-induced hypothyroidism (RHT) and the thyroid subregions as well as the received radiation dose in each subregion, this study aims to develop a subregional prediction model for RHT.

Methods: CT images and dose images of 128 patients with nasopharyngeal carcinoma were collected retrospectively. The thyroid subregion was obtained by clustering thyroid voxels and voxel entropy. After extracting 1781 radiomics features and 1767 dosiomics features, a subregional RHT prediction model was established, and its performance was compared with that of the whole thyroid model. The phenotype and dosimetry parameters of each subregion were analyzed by AUC, T test and Delong test.

Results: Three subregions (S1, S2, S3) were identified. The subregional prediction model was constructed based on 34 radiomics and dosiomics features. According to the Delong test, the prediction performance of the subregional model was significantly superior than that of the whole thyroid model (0.813 VS 0.624, p = 0.038). Subregional analysis suggests that S1 and S3 regions may have higher radiosensitivity than S2 regions.

Conclusions: In this study, a subregional model for predicting RHT was established and the radiosensitivity of the relevant subregions was evaluated. The subregion-based RHT prediction model may help to improve radiotherapy plan design for better thyroid function protection.

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