Normal tissue complication probability models of hypothyroidism after radiotherapy for breast cancer

Purpose

We aimed to develop Lyman-Kutcher-Burman (LKB) and multivariable normal tissue complication probability (NTCP) models to predict the risk of radiation-induced hypothyroidism (RIHT) in breast cancer patients.

Materials and methods

A total of 1,063 breast cancer patients who underwent whole breast irradiation between 2009 and 2016 were analyzed. Individual dose-volume histograms were used to generate LKB and multivariable logistic regression models. LKB model was fit using the thyroid radiation dose-volume parameters. A multivariable model was constructed to identify potential dosimetric and clinical parameters associated with RIHT. Internal validation was conducted using bootstrapping techniques, and model performance was evaluated using the area under the curve (AUC) and Hosmer-Lemeshow (HL) goodness-of-fit test.

Results

RIHT developed in 4 % of patients with a median follow-up of 77.7 months. LKB and multivariable NTCP models exhibited significant agreement between the predicted and observed results (HL P values > 0.05). The multivariable NTCP model outperformed the LKB model in predicting RIHT (AUC 0.62 vs. 0.54). In the multivariable model, systemic therapy, age, and percentage of thyroid volume receiving ≥ 10 Gy (V10) were significant prognostic factors for RIHT. The cumulative incidence of RIHT was significantly higher in patients who exceeded the cut-off values for all three risk predictors (systemic therapy, age ≥ 40 years, and thyroid V10 ≥ 26 %, P < 0.005).

Conclusions

Systemic therapy, age, and V10 of the thyroid were identified as strong risk factors for the development of RIHT. Our NTCP models provide valuable insights to clinicians for predicting and preventing hypothyroidism by identifying high-risk patients.

Analysis of correlative risk factors for radiation-induced hypothyroidism in head and neck tumors

OBJECTIVE

The aim of the study is to identify clinical and dosimetric factors that could predict the risk of radiation-induced hypothyroidism(RIHT) in head and neck cancer(HNC) patients following intensity-modulated radiotherapy(IMRT).

METHODS

A total of 103 HNC patients were included in our study. General clinical characteristic and dosimetric data of all recruited patients were analyzed, respectively. The univariate and multivariate logistic regression anlalysis were successively conducted to identify optimal predictors, which aim to construct the nomogram. And the joint prediction was performed.

RESULTS

The incidence of patients with HNC was 36.9% (38/103). Among the clinical factors, gender, N stage, chemotherapy, frequency of chemotherapy and surgery involving the thyroid were related to RIHT. Logistic regression analysis showed that thyroid volume, Dmean, VS45, VS50, VS60 and V30,60 were independent predictors of RIHT, which were also incorporated in the nomogram. An AUC of 0.937 (95%CI, 0.888-0.958) also was showed outstanding resolving ability of the nomogram. When the volume of the thyroid was greater than 10.6 cm3, the incidence of RIHT was 14.8%, and when the volume of the thyroid was equal to or smaller than 10.6 cm3, the incidence was 72.5%. The incidence rates of RIHT in the group with VS60≦8.4cm3 and VS60 > 8.4cm3 were 61.4% and 19.3%, respectively.

CONCLUSIONS

Thyroid volume and thyroid VS60 are independent predictors of RIHT in patients with HNC. Moreover, more attention should be paid to patients with thyroid volume ≤ 10.6cm3. Thyroid VS60 > 8.4cm3 may be a useful threshold for predicting the development of RIHT. The nomogram conducted by the research may become a potential and valuable tool that could individually predict the risk of RIHT for HNC patients.

Development and validation of a machine learning model of radiation-induced hypothyroidism with clinical and dose-volume features

BACKGROUND AND PURPOSE

Radiation-induced hypothyroidism (RIHT) is a common but underestimated late effect in head and neck cancers. However, no consensus exists regarding risk prediction or dose constraints in RIHT. We aimed to develop a machine learning model for the accurate risk prediction of RIHT based on clinical and dose-volume features and to evaluate its performance internally and externally.

MATERIALS AND METHODS

We retrospectively searched two institutions for patients aged >20 years treated with definitive radiotherapy for nasopharyngeal or oropharyngeal cancer, and extracted their clinical information and dose-volume features. One was designated the developmental cohort, the other as the external validation cohort. We compared the performances of machine learning models with those of published normal tissue complication probability (NTCP) models.

RESULTS

The developmental and external validation cohorts consisted of 378 and 49 patients, respectively. The estimated cumulative incidence rates of grade ≥1 hypothyroidism were 53.5% and 61.3% in the developmental and external validation cohorts, respectively. Machine learning models outperformed traditional NTCP models by having lower Brier scores at every time point and a lower integrated Brier score, while demonstrating a comparable calibration index and mean area under the curve. Even simplified machine learning models using only thyroid features performed better than did traditional NTCP algorithms. The machine learning models showed consistent performance between folds. The performance in a previously unseen external validation cohort was comparable to that of the cross-validation.

CONCLUSIONS

Our model outperformed traditional NTCP models, with additional capabilities of predicting the RIHT risk at individual time points. A simplified model using only thyroid dose-volume features still outperforms traditional NTCP models and can be incorporated into future treatment planning systems for biological optimization.

Early post-treatment 18F-FDG PET/CT for predicting radiation-induced hypothyroidism in head and neck cancer

BACKGROUND

Radiation-induced hypothyroidism (RIHT) is a common, but underestimated, late adverse effect in head and neck cancer. We investigated the value of early post-treatment ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for predicting RIHT.

METHODS

We searched our institutional database for patients aged ≥ 20 years who had undergone definitive radiotherapy for nasopharyngeal or oropharyngeal cancer between 2005 and 2017, followed by ¹⁸F-FDG PET/CT within 180 days of radiotherapy completion. We visually assessed and compared PET/CT and baseline characteristics in patients with and without RIHT using the chi-square test for categorical variables and the t-test for continuous variables. Variable predictive ability was evaluated by measuring the area under receiver operating characteristic curves.

RESULTS

Fifty-two patients were included; 22 (42%) developed RIHT and 30 (58%) did not. Two patients presented with diffuse thyroid uptake on PET/CT via visual assessment, and both developed RIHT later. Among the PET/CT variables, thyroid functioning volume was significantly higher in patients without RIHT than in patients with RIHT (16.30 ± 6.03 cm³ vs. 10.61 ± 3.81 cm³, p < 0.001). The maximum standard uptake values of the thyroid and pituitary glands did not differ significantly between the groups. Two patient characteristics, pretreatment thyroid volume and mean radiotherapy dose to the thyroid, also showed significant differences between the groups. An algorithmic approach combining visual grading of thyroid ¹⁸F-FDG uptake and thyroid functioning volume cutoff of 14.01 yielded an area under curve of 0.89 (95% confidence interval, 0.80-0.98); the sensitivity, specificity, positive predictive value, and negative predictive value were 87.0%, 82.3%, 80.0%, and 88.9%, respectively.

CONCLUSION

Early post-treatment PET/CT-derived thyroid functioning volume was a good predictor of RIHT development. Diffusely increased thyroid ¹⁸F-FDG uptake on PET/CT may indicate impending RIHT. Routine surveillance of thyroid function is warranted in patients at high risk of developing RIHT.

Radiation-induced hypothyroidism in patients with nasopharyngeal carcinoma treated with intensity-modulated radiation therapy with or without chemotherapy: Development of a nomogram based on the equivalent dose

OBJECTIVE

The aim of this study was to establish a nomogram for predicting radiation-induced hypothyroidism (RHT) based on an equivalent dose at 2 Gy per fraction (EQD₂) in patients with nasopharyngeal carcinoma (NPC) treated with intensity-modulated radiation therapy (IMRT) with or without chemotherapy.

METHODS

Two hundred forty-four eligible patients with NPC were recruited for this study. Patients' clinical factors and dose-volume parameters of the thyroid gland were retrieved from medical records and the IMRT treatment planning system, respectively. The irradiation doses were converted into EQD₂ for analysis. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression analysis were performed to identify optimal predictors of RHT for constructing the nomogram.

RESULTS

With a median follow-up of 63.0 months, the cumulative incidence rates of RHT at 3 months and 1-, 2-, 3-, 4- and 5- year after IMRT were 10.2%, 36.2%, 47.6%, 54.2%, 58.8% and 69.4%, respectively. Four independent factors for predicting RHT, including gender, age, pretreatment volume of the thyroid gland and V_35Gy(3Gy) of the thyroid gland, were identified and incorporated into the nomogram. The area under the ROC curve of the nomogram was 0.747 (95% confidence interval 0.685 - 0.809). Calibration curves and DCA curves showed that the nomogram was in good agreement with the actual observations and clinical usefulness.

CONCLUSIONS

The nomogram proposed in this study provides a reliable estimate of RHT risk in patients with NPC after IMRT and appears to have the potential to be a useful tool for widespread clinical applications.

Development of a normal tissue complication probability (NTCP) model for radiation-induced hypothyroidism in nasopharyngeal carcinoma patients

BACKGROUND

The objectives of this study were to build a normal tissue complication probability (NTCP) model of radiation-induced hypothyroidism (RHT) for nasopharyngeal carcinoma (NPC) patients and to compare it with other four published NTCP models to evaluate its efficacy.

METHODS

Medical notes of 174 NPC patients after radiotherapy were reviewed. Biochemical hypothyroidism was defined as an elevated level of serum thyroid-stimulating hormone (TSH) value with a normal or decreased level of serum free thyroxine (fT4) after radiotherapy. Logistic regression with leave-one-out cross-validation was performed to establish the NTCP model. Model performance was evaluated and compared by the area under the receiver operating characteristic curve (AUC) in our NPC cohort.

RESULTS

With a median follow-up of 24 months, 39 (22.4%) patients developed biochemical hypothyroidism. Gender, chemotherapy, the percentage thyroid volume receiving more than 50 Gy (V50), and the maximum dose of the pituitary (Pmax) were identified as the most predictive factors for RHT. A NTCP model based on these four parameters were developed. The model comparison was made in our NPC cohort and our NTCP model performed better in RHT prediction than the other four models.

CONCLUSIONS

This study developed a four-variable NTCP model for biochemical hypothyroidism in NPC patients post-radiotherapy. Our NTCP model for RHT presents a high prediction capability.

TRIAL REGISTRATION

This is a retrospective study without registration.

Prevalence of hypothyroidism among patients with breast cancer treated with radiation to the supraclavicular field: a single-centre survey

Purpose

We investigated the prevalence of hypothyroidism (HT) in patients with breast cancer who received radiation therapy to the supraclavicular (SC) field to evaluate the effect of radiation on thyroid.

Methods

Between April 2007 and May 2016, consecutive patients with invasive breast cancer who received SC radiation were recruited. Thyroid-stimulating hormone (TSH) and free thyroxine (fT4) were measured between April and August 2016. On the basis of the radiation-planning CT images, thyroid volume was calculated and dose–volume parameters were estimated. The endpoints were the prevalence of HT as determined by high levels of TSH and low levels of fT4 in serum, and the prevalence of subclinical HT, determined by high-serum TSH and normal fT4.

Results

Among the 68 consecutive patients, 26 were excluded from evaluation (10 patients died, 6 had a history of previous thyroid disease and 10 were lost to follow-up). One (2.4%) and six (14.3%) of these patients had HT and subclinical HT, respectively, with a mean TSH level of 8.27 µU/mL. By univariate analysis, a predictive factor of HT and subclinical HT was a thyroid volume <8 cm³ (OR 6.44, 95% CI 1.14 to 36.6; p=0.043). Multivariate analysis also showed an association between thyroid volume <8 cm³ and HT or subclinical HT (OR 18.48, 95% CI 1.48 to 230.86; p=0.024).

Conclusions

The prevalence of HT in patients with breast cancer studied was relatively low. Although thyroid volume appeared to be a predictive marker of HT in this cohort, further prospective evaluation is needed.