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Clinical and Dosimetric Predictors of Early Onset Postradiation Hypothyroidism in Patients with Head and Neck Malignancies: A Logistic Regression Analysis

Year: 2025

Type: article

Abstract: Hypothyroidism commonly occurs as a side effect following radiotherapy for head and neck malignancies, yet limited information exists to predict the risk of postradiation hypothyroidism. This study aims to investigate the clinical and dosimetric factors that may predict early onset postradiation hypothyroidism (EO-PRH). A retrospective study was conducted on patients with head and neck cancer treated between 2018 and 2021, with a minimum follow-up duration of 12 months. The thyroid gland was contoured on computed tomography (CT) scans, and dose-volume histograms were analyzed, incorporating thyroid volume and V 5–60 into the analysis. Logistic regression and receiver operating characteristic (ROC) analysis were performed to identify predictors and assess the model’s predictive value. Among the 84 eligible patients, 17 (20.2%) developed hypothyroidism within 1 year. The percentage of thyroid volume receiving 30 Gy (V 30 ) emerged as the sole significant dosimetric predictor of EO-PRH (odds ratio [OR] 1.02, 95% confidence interval [95% CI] 1.005–1.05, p = 0.03). Univariable analysis revealed significant differences in cancer histopathology, primary tumor site, V 15,30 , and VS 15,30 (the volume of the thyroid spared from radiation doses 15 Gy and 30 Gy) between the hypothyroid and euthyroid groups ( p < 0.10). Multivariable analysis identified the primary cancer site (OR 9.09, 95% CI 1.59–100) and V 30 (OR 1.26, 95% CI 1.007–1.76) as independent significant variables predicting EO-PRH. The predictive model incorporating cancer histopathology, primary tumor site, V 15,30 , and VS 15,30 effectively predicted postradiation thyroid dysfunction (area under the receiver operating characteristic curve [AUC-ROC] 0.84, 95% CI 0.73–0.95, p < 0.001). V 30 could serve as a dosimetric predictor of hypothyroidism following neck radiotherapy. This study underscores that a predictive model encompassing cancer type and site, along with V 15,30 and VS 15,30 , can effectively predict EO-PRH.

Source: Oncology and Therapy

DOI: 10.1007/s40487-025-00338-2