This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Immune checkpoint inhibitors (ICIs) are a group of drugs employed in the treatment of various types of malignant tumors and improve the therapeutic effect. ICIs blocks negative co-stimulatory molecules, such as programmed cell death gene-1 (PD-1) and its ligand (PD-L1) and cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4), reactivating the recognition and killing effect of the immune system on tumors. However, the reactivation of the immune system can also lead to the death of normal organs, tissues, and cells, eventually leading to immune-related adverse events (IRAEs). IRAEs involve various organs and tissues and also cause thyroid dysfunction. This article reviews the epidemiology, clinical manifestations, possible pathogenesis, and management of ICIs-related thyroid dysfunction.

Thyroid cancers (TC) have increasingly been detected following advances in diagnostic methods. Risk stratification guided by refined information becomes a crucial step toward the goal of personalized medicine. The diagnosis of TC mainly relies on imaging analysis, but visual examination may not reveal much information and not enable comprehensive analysis. Artificial intelligence (AI) is a technology used to extract and quantify key image information by simulating complex human functions. This latent, precise information contributes to stratify TC on the distinct risk and drives tailored management to transit from the surface (population-based) to a point (individual-based). In this review, we started with several challenges regarding personalized care in TC, for example, inconsistent rating ability of ultrasound physicians, uncertainty in cytopathological diagnosis, difficulty in discriminating follicular neoplasms, and inaccurate prognostication. We then analyzed and summarized the advances of AI to extract and analyze morphological, textural, and molecular features to reveal the ground truth of TC. Consequently, their combination with AI technology will make individual medical strategies possible.

Many thyroid cancer patients have suffered from treatment delays caused by the coronavirus disease 2019 pandemic. Although there have been many reviews, recommendations, or clinical experiences, clinical evidence that evaluates patient disease status is lacking. The aim of our research was to evaluate thyroid cancer behaviour in the post-COVID-19 era. Patients and Methods: A retrospective study was conducted and thyroid cancer patient data from February 1, 2017 to September 15, 2020 were pooled for analysis. The demographic, ultrasound and pathological data of the pre-and post-COVID-19 groups were compared. Lymph node metastases, tumour size, extrathyroidal extension, and multifocality were compared year-by-year to evaluate annual changes in patient characteristics. Regression analyses were adopted to reveal cancer behaviour along with the admission date interval and to reveal risk factors for lymph node metastasis. Patient ultrasound data were compared before and after the lockdown to assess tumour progression. The outcomes of delays in treatment ≤180 days were then studied. Results: The post-lockdown patients were more likely to have multiple lesions (31.2% vs 36.5%, p = 0.040), extrathyroidal extension (65.5% vs 72.2%, p = 0.011) and lymph node metastases (37.7% vs 45.0%, p = 0.007), while tumour size remained stable (1.01cm vs.1.02cm, p = 0.758). The lymph node metastasis rate increased by year (p < 0.001). The tumour size correlated negatively with the post-lockdown admission date (p = 0.002). No significant difference in tumour size, multifocality or lymph node metastasis on ultrasound was revealed between the pre-and post-lockdown group. No significant difference in tumour size, multifocality, extrathyroidal extension or lymph node metastasis was revealed among patients with a delayed treatment time ≤180 days. Conclusion:Patients with a COVID-19-induced treatment delay had more aggressive cancer behaviour. Rebound medical visits and annually increasing aggressiveness may be potential reasons for this observation, as individual patient tumour did not progress during the delay.

No abstract