Radiation-Induced Hypothyroidism in Patients with Oropharyngeal Cancer Treated with IMRT: Independent and External Validation of Five Normal Tissue Complication Probability Models

The continuum of care of anticancer treatment-induced hypothyroidism in patients with solid non thyroid tumors: time for an intimate collaboration between oncologists and endocrinologists

INTRODUCTION

Hypothyroidism is a common adverse event of various anticancer treatment modalities, constituting a notable paradigm of the integration of the endocrine perspective into precision oncology.

AREAS COVERED

The present narrative review provides a comprehensive and updated overview of anticancer treatment-induced hypothyroidism in patients with solid non-thyroid tumors. A study search was conducted on the following electronic databases: PubMed, Google Scholar, Scopus.com, ClinicalTrials.gov, and European Union Clinical Trials Register from 2011 until August 2021.

EXPERT OPINION

In patients with solid non-thyroid tumors, hypothyroidism is a common adverse event of radiotherapy, high dose interleukin 2 (HD IL-2), interferon alpha (IFN-α), bexarotene, immune checkpoint inhibitors (ICPi), and tyrosine kinase inhibitors (TKIs), while chemotherapy may induce hypothyroidism more often than initially considered. The path forward for the management of anticancer treatment-induced hypothyroidism in patients with solid non-thyroid tumors is an integrated approach grounded on 5 pillars: prevention, vigilance, diagnosis, treatment and monitoring. Current challenges concerning anticancer treatment-induced hypothyroidism await counteraction, namely awareness of the growing list of related anticancer treatments, identification of predictive factors, counteraction of diagnostic pitfalls, tuning of thyroid hormone replacement, and elucidation of its prognostic significance. Close collaboration of oncologists with endocrinologists will provide optimal patient care.

Dose-volume predictors of post-radiation primary hypothyroidism in head and neck cancer: a systematic review

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This systematic review included 29 studies (n = 4,530 patients) on dosimetric predictors of primary hypothyroidism in HNC.

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Average crude incidence of primary hypothyroidism after HNC radiotherapy was 41.4%.

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Thyroid Dmean and V50 were the most widely reported dosimetric predictors for hypothyroidism.

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Thyroid volume is a predictor of hypothyroidism (pooled aOR 0.89 per 1 cc increment) independent of radiation dosimetry.

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Thyroid gland constraints individualized for thyroid volume are crucial in HNC radiotherapy.

Background and Purpose

This systematic review aims to identify radiation dose-volume predictors of primary hypothyroidism after radiotherapy in patients with head and neck cancer (HNC).

Materials and methods

We performed a systematic literature search of Medline, EMBASE and Web of Science from database inception to July 1, 2021 for articles that discuss radiation dose-volume predictors of post-radiation primary hypothyroidism in patients with HNC. Data on the incidence, clinical risk factors and radiation dose-volume parameters were extracted. A meta-analysis was performed using the random-effects model to estimate the pooled odds ratio (OR) of thyroid volume as a predictor of the risk of post-radiation hypothyroidism, adjusted for thyroid radiation dosimetry.

Results

Our search identified 29 observational studies involving 4,530 patients. With median follow-up durations ranging from 1.0 to 5.3 years, the average crude incidence of post-radiation primary hypothyroidism was 41.4 % (range, 10 %–57 %). Multiple radiation dose-volume parameters were associated with post-radiation primary hypothyroidism, including the thyroid mean dose (Dmean), minimum dose, V25, V30, V35, V45, V50, V30–60, VS45 and VS60. Thyroid Dmean and V50 were the most frequently proposed dosimetric predictors. The pooled adjusted OR of thyroid volume on the risk of post-radiation primary hypothyroidism was 0.89 (95 % confidence interval, 0.85–0.93; p < 0.001) per 1 cc increment.

Conclusion

Post-radiation primary hypothyroidism is a common late complication after radiotherapy for HNC. Minimizing inadvertent exposure of the thyroid gland to radiation is crucial to prevent this late complication. Radiation dose-volume constraints individualized for thyroid volume should be considered in HNC radiotherapy planning.

Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models

Simple Summary

Radiation-induced hypothyroidism (RIHT) commonly develops in cancer survivors that receive radiation therapy for cancers in the head and neck region. The state-of-art normal tissue complication probability (NTCP) models perform satisfactorily; however, they do not use the whole spectrum of information that can be obtained from imaging techniques. The radiomic approach offers the ability to efficiently mine features, which are imperceptible to the human eye, but may provide crucial data about the patient’s condition. We gathered CT images and clinical data from 98 patients undergoing radiotherapy for head and neck cancers, 27 of whom later developed RIHT. For them, we created machine-learning models to predict RIHT using automatically extracted radiomic features and appropriate clinical and dosimetric parameters. We also validated the well-established external state-of-art NTCP models on our datasets and observed that our radiomic-based models performed very similarly to them. This shows that automated tools may perform as well as the current standard but can be theoretically applied faster and be implemented into existing imaging software used when planning radiotherapy.

Abstract

State-of-art normal tissue complication probability (NTCP) models do not take into account more complex individual anatomical variations, which can be objectively quantitated and compared in radiomic analysis. The goal of this project was development of radiomic NTCP model for radiation-induced hypothyroidism (RIHT) using imaging biomarkers (radiomics). We gathered CT images and clinical data from 98 patients, who underwent intensity-modulated radiation therapy (IMRT) for head and neck cancers with a planned total dose of 70.0 Gy (33–35 fractions). During the 28-month (median) follow-up 27 patients (28%) developed RIHT. For each patient, we extracted 1316 radiomic features from original and transformed images using manually contoured thyroid masks. Creating models based on clinical, radiomic features or a combination thereof, we considered 3 variants of data preprocessing. Based on their performance metrics (sensitivity, specificity), we picked best models for each variant ((0.8, 0.96), (0.9, 0.93), (0.9, 0.89) variant-wise) and compared them with external NTCP models ((0.82, 0.88), (0.82, 0.88), (0.76, 0.91)). We showed that radiomic-based models did not outperform state-of-art NTCP models (p > 0.05). The potential benefit of radiomic-based approach is that it is dose-independent, and models can be used prior to treatment planning allowing faster selection of susceptible population.