Evaluation of the Radiobiological Models predicting the Radiation-Induced Hypothyroidism in the Partially Irradiated Thyroid Gland of Patients with Breast Cancer

Radiation-induced hypothyroidism (RHT) is one of the side effects that might have an impact on the quality of life of patients with breast cancer treated with radiotherapy.

The aim of the current study was to evaluate the performances of the Lyman-Kutcher-Burman (LKB) and Log-Logistic models in the prediction of hypothyroidism (HT) as well as the estimation of the model parameters for the incidence of RHT among patients with breast cancer.

Fifty-two patients treated with radiation therapy (RT) for breast cancer were prospectively evaluated. Patients' serum samples [tri-iodothyronine, thyroxine, thyroid-stimulating hormone (TSH), free triiodothyronine, and free thyroxine] were measured before RT and also at a regular time interval until 1 year after the completion of RT. For each patient, dose-volume histograms (DVHs) of the thyroid gland were derived from their treatment planning dataset. Patients whose TSH levels were higher than normal with a decrease in FT4 levels were considered as cases with RHT. The LKB and Log-Logistic radiobiological models were evaluated by comparing them with the resultant follow-up data. The parameters for radiobiological models have been deduced by fitting the models to the follow-up data. The models were fitted in a Bayesian setting and compared according to the widely applicable information criterion (WAIC).

Twenty-one (40%) patients developed RHT at a follow-up of 1 year after the end of radiation treatment. The fitted values of D50 for the LKB and Log-Logistic models were 37.71 and 25.50 Gy, respectively for the partially irradiated thyroid of patients with breast cancer. The mean time to the incidence of RHT was obtained at 6.7 months in the studied group.

A volumetric effect was found for the thyroid gland in the implemented normal tissue complication probability models. Compared to the follow-up data, the Log-Logistic model was ranked as the best model for predicting the rate of RHT in patients with breast cancer.