The dilemma of 18F-FDG PET/CT thyroid incidentaloma: what we should expect from FNA. A systematic review and meta-analysis

Exceptionally Rare Isolated Thyroidal Metastasis of Pulmonary Carcinosarcoma: A Tale of 18F-FDG-positive Thyroid Nodule

Pulmonary carcinosarcomas (PCS) are uncommon and aggressive malignant tumors with epithelial and mesenchymal components and have a worse prognosis than other non-small-cell lung cancers. Metastases of non-thyroidal malignancies to the thyroid are rare. We reported a unique case of isolated thyroidal metastasis of PCS and discussed 18F-fluorodeoxyglucose (18F-FDG) positivity in incidentally found thyroid nodules on 18F-FDG positron emission tomography scan.

Prediction of Malignant Thyroid Nodules Using 18 F-FDG PET/CT-Based Radiomics Features in Thyroid Incidentalomas

OBJECTIVE

The purpose of the current study was to evaluate the diagnostic performances of 18 F-FDG PET/CT-based radiomics features for prediction of malignant thyroid nodules (TNs) in thyroid incidentaloma (TI).

METHODS

PubMed, Cochrane database, and EMBASE database, from the earliest available date of indexing through December 31, 2022, were searched for studies evaluating diagnostic performance of 18 F-FDG PET/CT-based radiomics features for prediction of malignant TNs in TI. We determined the sensitivities and specificities across studies, calculated positive and negative likelihood ratios (LRs; positive and negative LRs), and estimated pooled area under the curve.

RESULTS

Across 5 studies (518 patients), the pooled sensitivity of 18 F-FDG PET/CT was 0.77 (95% confidence interval [CI], 0.67-0.84), and a pooled specificity was 0.67. Likelihood ratio syntheses gave an overall positive LR of 2.3 (95% CI, 1.5-3.6) and negative LR of 0.35 (95% CI, 0.26-0.47). The pooled diagnostic odds ratio was 7 (95% CI, 4-12). The pooled area under the curve of fixed effects was 0.763 (95% CI, 0.736-0.791), and that of random effects was 0.763 (95% CI, 0.721-0.805).

CONCLUSION

18 F-FDG PET/CT-based radiomics features showed a good diagnostic performance for prediction of malignant TNs in TI.

The FDG Pattern of Autonomously Functioning Thyroid Nodules Correlates With Thyroid-Stimulating Hormone and Histopathology

PURPOSE OF THE REPORT

Whereas the 18 F-FDG PET/CT pattern of malignant thyroid neoplasia is known, the glucose uptake of autonomously functioning thyroid nodules (AFTNs) has not been fully investigated. We aimed to analyze the FDG uptake of AFTNs and its correlation with clinical, laboratory, ultrasonography, and histological features.

METHODS

We retrospectively analyzed the records of all AFTN patients from 2 centers, in which an 18 F-FDG PET/CT, thyroid function tests, and neck ultrasound were available within a 3-month window from the thyroid scintigraphy. Forty-five patients (35 female patients; median age, 65 years) were retrospectively included. The presence of FDG uptake was compared with the laboratory data and, whenever available, the histology.

RESULTS

Over a 36-month follow-up, 20 patients underwent surgery; 4 cancers, 10 follicular adenomas, and 6 follicular hyperplasias were found. Twenty-two AFTNs (48.9%) were FDG-positive, whereas the remaining 23 (51.1%) were not. Thyroid-stimulating hormone (TSH) was significantly lower in FDG-positive AFTNs than in negative ones (0.055 [0.02-0.42] vs 0.65 [0.2-0.96] mIU/L, P = 0.0018). On multivariate analysis, only TSH was independently associated with FDG uptake ( P = 0.008). On receiver operating characteristic curve analysis, TSH <0.08 mIU/L indicated FDG-positive AFTNs, with 64% sensitivity, 87% specificity, 4.88 positive likelihood ratio, and 0.42 negative likelihood ratio. In histologically proven benign lesions, TSH was significantly lower in follicular adenomas than in follicular hyperplasias ( P < 0.001). Patients with cancer had TSH in the low-normal range.

CONCLUSIONS

Autonomously functioning thyroid nodules show heterogeneous uptake FDG pattern, which depends on histopathology and thyroid function. Particularly, follicular adenomas tend to display increased glucose uptake and suppressed TSH.

The co-occurrence of both breast- and differentiated thyroid cancer: incidence, association and clinical implications for daily practice

Background

Breast cancer (BC) and differentiated thyroid cancer (TC) are two common cancer types with the highest incidence in women. BC and TC can develop synchronous or metachronous and the occurrence of both is higher than expected by chance. This study aimed to examine the association between BC and TC in the Netherlands.

Methods

This is a retrospective cohort study during the period of 1989–2020 retrieved from the Netherlands Cancer Registry (NCR). Patients diagnosed with BC-TC and BC alone as control group and TC-BC and TC alone as control group were included. The primary outcome was the standardized incidence ratio (SIR) of BC-TC and TC-BC. Secondary outcomes included data on the demographics, type of malignancy, treatment and overall survival (OS).

Results

The incidence of TC among 318.002 women with BC (BC-TC) was 0.1% (423 patients) (SIR = 1.86 (95% CI: 1.40–2.32)) and the incidence of BC among 12,370 patients with TC (TC-BC) was 2.9% (355 patients) (SIR = 1.46 (95% CI: 1.09–1.83)). BC-TC patients were younger compared to the BC alone group at BC diagnosis (55 vs 60 years, p < 0.001). The age-adjusted odds ratio to develop TC was not significantly increased for patients who received chemotherapy and radiotherapy. Most TC cases were synchronous tumors after BC diagnosis (19%) with a TNM stage 1. Only 6% of the BC tumors after TC occurred synchronous with a TNM stage 1 in most cases. The OS of all groups was the most favorable in patients with both BC and TC compared to BC- and TC alone.

Conclusion and relevance

The SIR of TC after BC diagnosis and BC after TC diagnosis was higher than predicted based on the rates of the general population. TC and BC as second primary tumors were diagnosed in an early stage and did not affect overall survival. Therefore, Dutch women who have been treated for BC or TC require no special surveillance for their thyroid- and breast gland.

Personalized Diagnosis in Differentiated Thyroid Cancers by Molecular and Functional Imaging Biomarkers: Present and Future

Personalized diagnosis can save unnecessary thyroid surgeries, in cases of indeterminate thyroid nodules, when clinicians tend to aggressively treat all these patients. Personalized diagnosis benefits from a combination of imagery and molecular biomarkers, as well as artificial intelligence algorithms, which are used more and more in our timeline. Functional imaging diagnosis such as SPECT, PET, or fused images (SPECT/CT, PET/CT, PET/MRI), is exploited at maximum in thyroid nodules, with a long history in the past and a bright future with many suitable radiotracers that could properly contribute to diagnosing malignancy in thyroid nodules. In this way, patients will be spared surgery complications, and apparently more expensive diagnostic workouts will financially compensate each patient and also the healthcare system. In this review we will summarize essential available diagnostic tools for malignant and benignant thyroid nodules, beginning with functional imaging, molecular analysis, and combinations of these two and other future strategies, including AI or NIS targeted gene therapy for thyroid carcinoma diagnosis and treatment as well.

Radiomics Analysis of [18F]FDG PET/CT Thyroid Incidentalomas: How Can It Improve Patients’ Clinical Management? A Systematic Review from the Literature

Background: We performed a systematic review of the literature to provide an overview of the application of PET-based radiomics of [18F]FDG-avid thyroid incidentalomas and to discuss the additional value of PET volumetric parameters and radiomic features over clinical data. Methods: The most relevant databases were explored using an algorithm constructed based on a combination of terms related to our subject and English-language articles published until October 2021 were considered. Among the 247 identified articles, 19 studies were screened for eligibility and 11 met the criteria, with 4 studies including radiomics analyses. Results: We summarized the available literature based on a study of PET volumetric parameters and radiomics. Ten articles provided accurate details about volumetric parameters and their prospective value in tumour assessment. We included the data provided by these articles in a sub-analysis, but could not obtain statistically relevant results. Four publications analyzed the diagnostic potential of radiomics and the possibility of creating precise predictive models, their corresponding quality score being assessed. Conclusions: The use of PET volumetric parameters and radiomics analysis in patients with [18F]FDG-avid thyroid incidentalomas outlines a great prospect in diagnosis and stratification of patients with malignant nodules and may represent a way of limiting the need for unnecessary invasive procedures; however, further studies need to be performed for a standardization of the method.

Artificial Intelligence in Thyroid Field-A Comprehensive Review

Simple Summary

The incidence of thyroid pathologies has been increasing worldwide. Historically, the detection of thyroid neoplasms relies on medical imaging analysis, depending mainly on the experience of clinicians. The advent of artificial intelligence (AI) techniques led to a remarkable progress in image-recognition tasks. AI represents a powerful tool that may facilitate understanding of thyroid pathologies, but actually, the diagnostic accuracy is uncertain. This article aims to provide an overview of the basic aspects, limitations and open issues of the AI methods applied to thyroid images. Medical experts should be familiar with the workflow of AI techniques in order to avoid misleading outcomes.

Abstract

Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.