Abstract
Context
During the COVID-19 pandemic, both people with underlying diseases and previously healthy people were infected with SARS-CoV-2. In our institute, most hospitalized patients underwent chest computed tomography (CT) to evaluate pulmonary involvement and complication of COVID-19. There are currently limited data regarding thyroid CT incidentalomas in healthy people.
Objective
We aimed to investigate the prevalence and predictors of thyroid incidentalomas among hospitalized patients with COVID-19.
Methods
A single-center retrospective study included hospitalized patients aged ≥15 years with COVID-19 who underwent chest CT during April 2020 and October 2021. Thyroid incidentalomas were reviewed and identified by an experienced radiologist. Logistic regression analysis was used to determine predictors for thyroid incidentalomas.
Results
In the 1326 patients (mean age 49.4 years and 55.3% female) that were included, the prevalence of thyroid incidentalomas was 20.2%. Patients with thyroid incidentalomas were older (59.6 years vs 46.8 years, P < .001) and more often female than those without incidentalomas (63.4% vs 53.2%, P = .003). On multivariate analysis, only female sex (OR 1.56; 95% CI 1.17-2.07) and older age (OR 1.04; 95% CI 1.03-1.05) were significantly associated with thyroid incidentalomas.
Conclusion
In COVID-19 patients, the prevalence of thyroid incidentalomas identified on chest CT was higher (20.2%) than in previous studies in the general population (<1% to 16.8%). Female sex and older age were independent factors associated with thyroid incidentalomas.
Keywords: adult, diagnostic imaging, elderly, neoplasm, COVID-19, SARS-CoV-2, thyroid nodule
Thyroid incidentaloma refers to an asymptomatic thyroid nodule serendipitously discovered during the investigation of an unrelated condition to thyroid disease. Thyroid incidentalomas are increasing due to the widespread use of high-resolution imaging studies. Thyroid incidentalomas in adults are very common with the prevalence ranging from 5% to 67% depending on the imaging methods used [1]. The prevalence of thyroid incidentalomas reported is up to 67% on ultrasound (US) [2], up to 25% for contrast-enhanced chest computed tomography (CT) examinations [3], 16% to 18% for neck CT and magnetic resonance scans [4-6], and 1% to 2% for fluorine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) scans [7-9]. Females, increasing age, and possibly obesity seem to increase the prevalence of thyroid nodules [10-12].
At the end of 2019, the emergence and rapid spread of Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has resulted in a global pandemic. SARS-CoV-2 utilizes its surface spike protein to specifically recognize the host cell surface angiotensin-converting enzyme 2 (ACE2) receptor facilitating viral entry and replication [13]. The host cell proteases, mainly transmembrane serine protease type 2 (TMPRSS2) cleavage the spike protein that is necessary for its binding to the ACE2 receptor [14]. Both the receptor bindings with membrane-bound ACE2 and TMPRSS2 are necessary for the entrance of the virus to the host cell [13, 14]. Several studies have demonstrated extensive ACE2 expression in various endocrine tissues including the hypothalamus, pituitary, thyroid, adrenal, pancreas, testis, ovaries, and adipose tissue [15]. Thyroid tissue has a high mRNA expression of ACE2 and TMPRSS2 regardless of sex [15-17]. This evidence suggests that the thyroid gland may be a vulnerable target for SARS-CoV-2 infection. COVID-19 infection can induce exaggerated systemic inflammatory immune responses in the thyroid gland [18], and severe thyroid follicular cell injury has been described in SARS-CoV infection [19]. Thyroid involvement in COVID-19 may present with euthyroid sick syndrome, subacute thyroiditis, Graves disease, Hashimoto thyroiditis, postpartum thyroiditis, and silent thyroiditis, but little is known about SARS-CoV-2 and its oncogenic potential in the thyroid gland [20]. To date, there are limited data about thyroid incidentalomas among patients with COVID-19 [21-23].
People of all ages including healthy people can be infected by SARS-CoV-2, largely depending on their high risk of exposure. Individuals infected with COVID-19 have a wide spectrum of symptoms ranging from mild symptoms to critical illness. At the beginning of the COVID-19 pandemic, most hospitalized patients with COVID-19 had pulmonary involvement and underwent CT. Prior to the COVID-19 pandemic, chest CTs were usually performed in older adults due to their illness. There are still gaps in the literature regarding the prevalence of thyroid incidentalomas in the younger population. Although thyroid cancer screening is not recommended in asymptomatic persons [24], thyroid ultrasonography is frequently used in health check-up programs. In healthy subjects who underwent voluntary cancer screening, the prevalence of thyroid incidentaloma identified by 18F-FDG PET was 1.2% to 3% [25, 26]. However, there are no data on thyroid incidentaloma identified by CT chest in healthy subjects. We aimed to investigate the prevalence of thyroid incidentalomas and associated factors among hospitalized patients with COVID-19.
Materials and Methods
Study Population and Design
This was a retrospective cohort study using electronic medical record data of all hospitalized COVID-19 patients aged ≥15 years who underwent chest CT with or without contrast at the Chakri Naruebodindra Medical Institute between April 2020 and October 2021. In Thailand, the first wave of COVID-19 began in March 2020. In response to the rapidly increased number of individuals infected with COVID-19, our institute was the referral center for patients with COVID-19. The study was approved by the Institutional Review Board, and the need to obtain informed patient consent was waived due to the retrospective study design.
A total of 1566 chest CT studies were eligible for inclusion in this study. After reviews of electronic medical records, we excluded 240 patients with pre-existing thyroid diseases (n = 49), invisible thyroid gland on chest CT (n = 6), and duplicated studies (n = 185). For participants who underwent both noncontrast and contrast-enhanced chest CT, only contrast chest CT was selected for analysis.
Chest CT Scans
All images were obtained with a uCT 530 CT scanner (United Imaging Healthcare, China). Imaging parameters included 120 kVp, automatic mAs, 1-mm section thickness, 1.1250 pitch, 0.5 s rotation time, and slice collimation 0.55 mm × 40. CT imaging data sets were reviewed by a blinded experienced radiologist (P.H.). The following characteristics of thyroid incidentalomas were collected: number, size, homogeneity, calcification, and density. For participants with multiple thyroid incidentalomas, only the largest nodules were characterized.
Baseline Characteristics
Basic demographic data such as age, sex, body weight, height, history of neck radiation, and underlying diseases were included. The laboratory results including fasting plasma glucose (FPG), hemoglobin A1C (HbA1c), lipid profile, and thyroid function tests were also collected.
Statistical Analysis
Categorical variables are presented as frequency and percentage. Continuous variables which were normally distributed are presented as mean and SD, and variables with non-normal distribution are presented as median and interquartile range (IQR). Student's t-test or quantile regression was used to calculate differences in continuous variables. Chi-squared test or Fisher's exact test, where appropriate, was used to calculate differences in categorical variables. Simple and multiple logistic regressions were performed to assess the correlation between the associated factors and the presence of thyroid incidentalomas. The correlation is presented as OR and 95% CI. Statistical analyses were performed using STATA version 17.0 (StataCorp LLC, College Station, TX). P < .05 was considered to be statistically significant.
Results
Baseline Characteristics and Prevalence
A total of 1326 chest CT imaging (1181 CTs with contrast and 145 CTs without contrast) were included in the study. The mean age of the patients was 49.4± 18.5 years and 733 (55.3%) were female. The mean body mass index (BMI) was 27.1± 6.7 kg/m2. The demographic data of the 2 groups (CT with and without thyroid incidentaloma) are shown in Table 1.
Table 1.
Baseline characteristics of participants
| Characteristics | Total n = 1326 |
Thyroid incidentaloma n = 268 |
No thyroid incidentaloma n = 1058 |
P value |
|---|---|---|---|---|
| Age, years | 49.4 ± 18.5 (range 15-97) |
59.6 ± 18.4 (range 19-97) |
46.8 ± 17.7 (range 15-96) |
<.001 |
| Female, n (%) | 733 (55.3) | 170 (63.4) | 563 (53.2) | .003 |
| Body mass index, kg/m2 | 27.1 ± 6.7 | 26.0 ± 5.7 | 27.3 ± 6.8 | .001 |
| History of radiation, n (%) | 9 (0.7) | 1 (0.4) | 8 (0.8) | .500 |
| Hypertension, n (%) | 481 (36.3) | 136 (50.7) | 345 (32.6) | <.001 |
| Diabetes mellitus, n (%) | 347 (26.2) | 97 (36.2) | 250 (23.6) | <.001 |
| Dyslipidemia, n (%) | 419 (31.6) | 115 (42.9) | 304 (28.7) | <.001 |
| FPGa, mg/dL | 119.0 ± 56.9 | 125.0 ± 62.1 | 117.6 ± 55.4 | .101 |
| HbA1cb, % | 6.2 ± 1.6 | 6.4 ± 1.6 | 6.2 ± 1.7 | .077 |
| Triglyceridec, mg/dL | 113 (85, 151) | 113 (83, 151) | 112 (86, 151) | .880 |
| HDLc, mg/dL | 44.5 ± 14.4 | 45.2 ± 15.1 | 44.3 ± 14.2 | .370 |
| LDLd, mg/dL | 114.0 ± 38.8 | 108.7 ± 44.4 | 115.3 ± 37.2 | .017 |
| Total cholesterolc, mg/dL | 175.3 ± 42.4 | 171.8 ± 47.1 | 176.1 ± 41.1 | .160 |
| TSHe, mIU/L | 0.5 (0.2, 1.0) | 0.4 (0.1, 0.8) | 0.6 (0.3, 1.1) | .170 |
| FT3f, pg/mL | 1.5 ± 0.6 | 1.4 ± 0.6 | 1.5 ± 0.6 | .390 |
| FT4g, ng/dL | 1.1 ± 0.3 | 1.1 ± 0.3 | 1.1 ± 0.3 | .490 |
Abbreviations: FPG, fasting plasma glucose; FT3, free triiodothyronine; FT4, free thyroxine; HbA1c, hemoglobin A1C; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TC, total cholesterol; TSH, thyroid-stimulating hormone.
Data availability for evaluation: an = 1159; bn = 1304; cn = 1219; dn = 1220; en = 94; fn = 81; gn = 90
The prevalence of thyroid incidentaloma detected in chest CT was 20.2% (268/1326). Thyroid incidentaloma was found on chest CT with contrast more than without contrast (37.9%, 55/145 vs 18%, 213/1181, P < .001).
Characteristics of Incidentalomas
About half of the incidentalomas (52.6%) were solitary nodules. The median anteroposterior (AP) and transverse (T) diameter were 0.6 cm (0.4, 1.0) and 0.6 cm (0.4, 1.0), respectively. Median attenuation was 65.5 Hounsfield unit (41.5, 94.0). Over 80% of thyroid incidentalomas were homogeneous (83.2%) and had no calcifications (80.6%). More detailed CT characteristics of thyroid incidentaloma are shown in Table 2.
Table 2.
Computed tomography characteristics of thyroid incidentaloma
| Characteristics | n = 268 |
|---|---|
| Solitary lesion, n (%) | 141 (52.6) |
| Anteroposterior diameter, cm | 0.6 (0.4, 1.0) |
| Transverse diameter, cm | 0.6 (0.4,1.0) |
| Anteroposterior/transverse ratio | 1.1 ± 0.4 |
| Density, HU | 65.5 (41.5, 94.0) |
| Homogeneous density, n (%) | 223 (83.2) |
| Calcification, no. (%) | 52 (19.4) |
Abbreviation: HU, Hounsfield unit.
Factors Associated With Thyroid Incidentalomas
A univariate analysis was used to assess the association between participants’ baseline characteristics and the presence of thyroid incidentalomas. Factors including age, sex, BMI, hypertension, diabetes mellitus, and dyslipidemia were significantly associated with thyroid incidentalomas (Table 3). Other laboratory investigations including FPG, HbA1c, lipid profile, and thyroid function tests showed no significant association with thyroid incidentalomas.
Table 3.
Correlation between variables and the presence of thyroid incidentalomas
| Variables | Univariate analysis | Multivariate analysis | ||
|---|---|---|---|---|
| OR (95% CI) | P value | OR (95% CI) | P value | |
| Age, years | 1.04 (1.03, 1.05) | <.001 | 1.04 (1.03, 1.05) | <.001 |
| Female | 1.53 (1.16, 2.01) | .002 | 1.56 (1.17, 2.07) | .003 |
| BMI, kg/m2 | 0.97 (0.95, 0.99) | .003 | ||
| Hypertension | 2.13 (1.62, 2.79) | <.001 | ||
| Diabetes mellitus | 1.83 (1.38, 2.44) | <.001 | ||
| Dyslipidemia | 1.86 (1.41, 2.46) | <.001 | ||
Abbreviations: BMI, body mass index.
A multivariate analysis was performed to determine the variables that were independently associated with the prevalence of thyroid incidentalomas. Female sex and increased age were the only 2 factors significantly associated with thyroid incidentalomas. The prevalence of thyroid incidentalomas was increased by 4% in every 1-year increase in age (OR 1.04, 95% CI 1.03-1.05, P < .001). Females had a 1.56 times higher probability of developing thyroid incidentalomas than males (OR 1.56, 95% CI 1.17-2.07, P = .003).
Prevalence by Age and Gender
The prevalence of thyroid incidentalomas classified in each decade of life and gender are shown in Table 4. Both males and females had a higher prevalence of thyroid incidentalomas in older age. Due to the small number of participants in the nonagenarian (4 females and 2 males), the true prevalence of thyroid incidentalomas in this age group could not be precisely estimated. Among participants aged less than 30 years, the prevalence of thyroid incidentalomas was 9.8%. After matching with the same age period (Table 4 and Fig. 1), the prevalence of thyroid incidentaloma in males was comparable with females aged 15-19 years (7.7% vs 7.7%) and higher than female participants aged 20-29 years (12.8% vs 8.2%).
Table 4.
Prevalence of thyroid incidentalomas classified in each decade of life and gender
| Number of thyroid incidentaloma (% prevalence) | P-value | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Age, years | ||||||||||
| 15-19 | 20-29 | 30-39 | 40-49 | 50-59 | 60-69 | 70-79 | 80–89 | ≥90 | ||
| All chest CT (n = 1326) | ||||||||||
| All | 2/26 (7.7%) | 23/228 (10.1%) | 22/195 (11.3%) | 25/206 (12.1%) | 50/251 (19.9%) | 56/198 (28.3%) | 53/146 (36.3%) | 33/70 (47.1%) | 4/6 (66.7%) | <.001 |
| Female | 1/13 (7.7%) | 11/134 (8.2%) | 14/106 (13.2%) | 18/110 (16.4%) | 34/146 (23.3%) | 31/97 (32.0%) | 29/76 (38.2%) | 28/47 (59.6%) | 4/4 (100.0%) | <.001 |
| Male | 1/13 (7.7%) | 12/94 (12.8%) | 8/89 (9.0%) | 7/96 (7.3%) | 16/105 (15.2%) | 25/101 (24.8%) | 24/70 (34.3%) | 5/23 (21.7%) | 0/2 (0.0%) | <.001 |
| Chest CT without contrast (n = 1181) | ||||||||||
| All | 2/26 (7.7%) | 23/226 (10.2%) | 22/191 (11.5%) | 21/185 (11.4%) | 43/213 (20.2%) | 39/164 (23.8%) | 35/111 (31.5%) | 25/60 (41.7%) | 3/5 (60.0%) | <.001 |
| Female | 1/13 (7.7%) | 11/132 (8.3%) | 14/104 (13.5%) | 16/99 (16.2%) | 30/134 (22.4%) | 25/84 (29.8%) | 21/63 (33.3%) | 21/40 (52.5%) | 3/3 (100.0%) | <.001 |
| Male | 1/13 (7.7%) | 12/94 (12.8%) | 8/87 (9.2%) | 5/86 (5.8%) | 13/79 (16.5%) | 14/80 (17.5%) | 14/48 (29.2%) | 4/20 (20.0%) | 0/2 (0.0%) | .019 |
| Chest CT with contrast (n = 145) | ||||||||||
| All | − | 0/2 (0.0%) | 0/4 (0.0%) | 4/21 (19.0%) | 7/38 (18.4%) | 17/34 (50.0%) | 18/35 (51.4%) | 8/10 (80.0%) | 1/1 (100.0%) | <.001 |
| Female | − | 0/2 (0.0%) | 0/2 (0.0%) | 2/11 (18.2%) | 4/12 (33.3%) | 6/13 (46.2%) | 8/13 (61.5%) | 7/7 (100.0%) | 1/1 (100.0%) | 0.005 |
| Male | − | − | 0/2 (0.0%) | 2/10 (20%) | 3/26 (11.5%) | 11/21 (52.4%) | 10/22 (45.5%) | 1/3 (33.3%) | − | .018 |
Abbreviation: CT, computed tomography.
Figure 1.
Prevalence of thyroid incidentalomas in each decade of life.
Discussion
Our study is the first dedicated review of CT scans for thyroid incidentalomas in patients with COVID-19. The prevalence of thyroid incidentalomas was 20.2% in hospitalized patients with COVID-19 who underwent chest CT for investigation of pulmonary and other COVID-19-related complications. Most thyroid lesions detected on CT were small size and had no calcification. From multivariate analysis, female sex and increased age were independent factors associated with the presence of thyroid incidentalomas. The prevalence of thyroid incidentaloma significantly increased with age for CT chest scans with and without contrast.
Previous studies revealed a wide range of prevalence of thyroid incidentalomas detected from CT scans, varying from <1% to 16% [4, 5, 27-29]. The higher prevalence is due to the dedicated imaging reviews of thyroid lesions [4, 5], similar to our study. In contrast, the larger database studies (∼100 000 imaging studies) identified thyroid incidentaloma with a <1% reported rate due to routine radiology reports in clinical practice [27, 29]. The different rate of incidental detection of a thyroid nodule is 10% when comparing routine reports and dedicated reviews [29]. In clinical practice, thyroid incidentaloma was reported less in the impression section than the findings section of the radiology report, leading to fewer further diagnostic work-ups [30]. This is predominantly influenced by the American College of Radiology's recommendations [31]. The 3-tiered system, which categorizes patients based on CT and magnetic resonance imaging findings, suspicious imaging features (suspicious lymph nodes, local invasion, PET-avid nodule), thyroid nodule size ≥15 mm, and patient age ≥35 years, could reduce unnecessary diagnostic work-up of thyroid incidentaloma without missed malignancies [30, 32]. These recommendations are against the American Thyroid Association guidelines that recommend using US for all thyroid nodules seen on CT scans [24].
Thyroid incidentalomas are commonly detected during the interpretation of CT chest examinations. Thyroid nodules can be easily detected even without contrast because the normal thyroid gland has a high CT attenuation value due to its high iodine content [33]. While US is the preferred imaging modality for thyroid evaluation due to superior spatial resolution, there is no reliable CT finding to distinguish benign from malignant lesions [34].
Cystic lesions, lobulated/irregular margins, absence of halo, and microcalcifications can be better seen on US than CT. Previous studies showed CT findings matched US findings in only 53% to 57.4% of patients, and CT missed other thyroid nodules or multinodularity in 40% to 40.7% of patients [5, 34]. CT also underestimates the size of thyroid nodules compared with US [35, 36]. Thyroid incidentalomas detected on CT may be difficult to characterize because the CT chest scan was not performed specifically with the intention of assessing the thyroid gland, and the entire gland may not be fully visualized. CT artifacts from high contrast material in the subclavian veins may obscure or simulate pathology [37]. In our study, the size of most nodules was <1 cm with an AP/T ratio of 1.1, and around 80% had no calcification. The attenuation value was 65.5 Hounsfield units. Despite no reliable CT findings to predict malignancy, Yoon et al reported that malignant lesions frequently demonstrated calcifications, AP/T ratio >1, and attenuation values >130 Hounsfield units on contrast-enhanced CT [5]. Small (<1 cm) thyroid incidentalomas have a high probability of being benign [34].
Patient age is considered to be an important clinical risk factor in the management of thyroid incidentalomas [31]. In our study, the prevalence of thyroid incidentalomas in young patients with COVID-19 (<30 years) was 9.8%. Previous studies reported a higher rate of malignancy in patients aged ≤35 years who had a thyroid incidentaloma detected on CT [6, 34]. According to the American College of Radiology's recommendations, further evaluation with US for patients ≤35 years with a nodule measuring ≥1 cm is suggested due to the high prevalence of malignant lesions [31].
To the best of our knowledge, there is no evidence yet associating SARS-CoV-2 with the development or alteration of a thyroid nodule. However, US changes in the echostructure and increased size of benign thyroid nodules after COVID-19 infection have been preliminarily reported [38]. In our study, the prevalence of thyroid incidentalomas detected on CT was higher than in previous reports (20.2% vs ∼16%) [4, 5]. The higher prevalence of thyroid incidentalomas in patients with COVID-19 could be due to the following reasons: (1) a dedicated review of thyroid incidentaloma, (2) thinner CT slide thickness (1 mm vs 3-5 mm in the previous studies [4, 5], and (3) potential mechanisms of thyroid nodule formation caused by SARS-CoV-2 infection.
The strength of this study is the wide range of the study population, from 15 to 97 years. About one-third of participants in this study (33.86%) were aged under 40 years. Thyroid incidentalomas are usually described in people undergoing imaging studies due to nonthyroid-related health conditions. Our study population might represent the prevalence of thyroid incidentalomas in assumedly healthy people who are not normally seeking medical care. To date, our study is the first and largest study reporting prevalence of thyroid incidentalomas in patients with COVID-19. We acknowledge several limitations of our study. First, this was a retrospective study at a single center. Second, we also did not further evaluate for thyroid incidentalomas via US or fine-needle aspiration biopsy because our participants with COVID-19 were referred to primary care physicians after discharge from the hospital. The rate of malignancy remained undetermined.
Conclusions
The prevalence of thyroid incidentalomas identified on chest CT scans was 20.2% in patients with COVID-19. The prevalence in our study was higher than in previous studies (<1% to 16.8%). The increased prevalence of thyroid incidentalomas was associated with female sex and increased age.
Acknowledgments
We thank Sukanya Siriyotha from Department of Clinical Epidemiology and Biostatistics for statistical analysis suggestion. The abstract will be presented at the ENDO 2023, in Chicago, Illinois.
Abbreviations
- 18F-FDG
fluorine-18 fluorodeoxyglucose
- ACE2
angiotensin-converting enzyme 2
- AP
anteroposterior
- BMI
body mass index
- CT
computed tomography
- FPG
fasting plasma glucose
- PET
positron emission tomography
- T
transverse
- TMPRSS2
transmembrane serine protease type 2
- US
ultrasound
Contributor Information
Methus Jantarapootirat, Faculty of Medicine Ramathibodi Hospital, Ramathibodi School of Medicine, Chakri Naruebodindra Medical Institute, Mahidol University, Samut Prakan 10540, Thailand.
Sirinapa Traiwanatham, Faculty of Medicine Ramathibodi Hospital, Ramathibodi School of Medicine, Chakri Naruebodindra Medical Institute, Mahidol University, Samut Prakan 10540, Thailand.
Pornrujee Hirunpat, Faculty of Medicine Ramathibodi Hospital, Ramathibodi School of Medicine, Chakri Naruebodindra Medical Institute, Mahidol University, Samut Prakan 10540, Thailand.
Woranan Boonsomsuk, Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand.
Somnuek Sungkanuparph, Faculty of Medicine Ramathibodi Hospital, Ramathibodi School of Medicine, Chakri Naruebodindra Medical Institute, Mahidol University, Samut Prakan 10540, Thailand.
Chutintorn Sriphrapradang, Email: chutintorn.sri@mahidol.ac.th, Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand.
Funding
There was no funding for this work.
Author Contributions
M.J., S.T., P.H., and C.S. designed the study. M.J., S.T., P.H., and W.B. were responsible for data collection. M.J., S.T., P.H., and W.B. conducted statistical analyses, interpreted the initial results, and drafted the manuscript. S.S. and C.S. contributed to discussion and revised the manuscript. All authors participated in the data interpretation, contributed to the manuscript writing with important intellectual content, and approved the final version of the manuscript.
Disclosures
The authors have nothing to disclose.
Data Availability
Some or all datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.
Ethics Approval
The study was approved by the Human Research Ethics Committee, Faculty of Medicine Ramathibodi Hospital, Mahidol University (ethics approval number: MURA2021/981).
References
- 1. Dean DS, Gharib H. Epidemiology of thyroid nodules. Best Pract Res Clin Endocrinol Metab. 2008;22(6):901‐911. [DOI] [PubMed] [Google Scholar]
- 2. Ezzat S, Sarti DA, Cain DR, Braunstein GD. Thyroid incidentalomas. Prevalence by palpation and ultrasonography. Arch Intern Med. 1994;154(16):1838‐1840. [DOI] [PubMed] [Google Scholar]
- 3. Ahmed S, Horton KM, Jeffrey RB Jr, Sheth S, Fishman EK. Incidental thyroid nodules on chest CT: review of the literature and management suggestions. AJR Am J Roentgenol. 2010;195(5):1066‐1071. [DOI] [PubMed] [Google Scholar]
- 4. Youserm DM, Huang T, Loevner LA, Langlotz CP. Clinical and economic impact of incidental thyroid lesions found with CT and MR. AJNR Am J Neuroradiol. 1997;18(8):1423‐1428. [PMC free article] [PubMed] [Google Scholar]
- 5. Yoon DY, Chang SK, Choi CS, et al. The prevalence and significance of incidental thyroid nodules identified on computed tomography. J Comput Assist Tomogr. 2008;32(5):810‐815. [DOI] [PubMed] [Google Scholar]
- 6. Nguyen XV, Choudhury KR, Eastwood JD, et al. Incidental thyroid nodules on CT: evaluation of 2 risk-categorization methods for work-up of nodules. AJNR Am J Neuroradiol. 2013;34(9):1812‐1817. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Shie P, Cardarelli R, Sprawls K, Fulda KG, Taur A. Systematic review: prevalence of malignant incidental thyroid nodules identified on fluorine-18 fluorodeoxyglucose positron emission tomography. Nucl Med Commun. 2009;30(9):742‐748. [DOI] [PubMed] [Google Scholar]
- 8. Soelberg KK, Bonnema SJ, Brix TH, Hegedüs L. Risk of malignancy in thyroid incidentalomas detected by 18F-fluorodeoxyglucose positron emission tomography: a systematic review. Thyroid. 2012;22(9):918‐925. [DOI] [PubMed] [Google Scholar]
- 9. de Leijer JF, Metman MJH, van der Hoorn A, et al. Focal thyroid incidentalomas on 18F-FDG PET/CT: a systematic review and meta-analysis on prevalence, risk of malignancy and inconclusive fine needle aspiration. Front Endocrinol (Lausanne). 2021;12:723394. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Kwong N, Medici M, Angell TE, et al. The influence of patient age on thyroid nodule formation, multinodularity, and thyroid cancer risk. J Clin Endocrinol Metab. 2015;100(12):4434‐4440. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Moon JH, Hyun MK, Lee JY, et al. Prevalence of thyroid nodules and their associated clinical parameters: a large-scale, multicenter-based health checkup study. Korean J Intern Med. 2018;33(4):753‐762. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Li Y, Jin C, Li J, et al. Prevalence of thyroid nodules in China: A health examination cohort-based study. Front Endocrinol (Lausanne). 2021;12:676144. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Jackson CB, Farzan M, Chen B, Choe H. Mechanisms of SARS-CoV-2 entry into cells. Nat Rev Mol Cell Biol. 2022;23(1):3‐20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Parmar MS. TMPRSS2: An equally important protease as ACE2 in the pathogenicity of SARS-CoV-2 infection. Mayo Clin Proc. 2021;96(11):2748‐2752. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Lazartigues E, Qadir MMF, Mauvais-Jarvis F. Endocrine significance of SARS-CoV-2's reliance on ACE2. Endocrinology. 2020;161(9):bqaa108. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Rotondi M, Coperchini F, Ricci G, et al. Detection of SARS-COV-2 receptor ACE-2 mRNA in thyroid cells: a clue for COVID-19-related subacute thyroiditis. J Endocrinol Invest. 2021;44(5):1085‐1090. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Koehler VF, Knosel T, Hasmann SE, et al. Thyroidal ACE2 protein expression and thyroid function tests in patients with COVID-19: results from a retrospective case series and a prospective cohort study. Thyroid. 2022;33(2):177‐185. [DOI] [PubMed] [Google Scholar]
- 18. Duntas LH, Jonklaas J. COVID-19 and thyroid diseases: A bidirectional impact. J Endocr Soc. 2021;5(8):bvab076. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Wei L, Sun S, Xu CH, et al. Pathology of the thyroid in severe acute respiratory syndrome. Hum Pathol. 2007;38(1):95‐102. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Giannoula E, Iakovou I, Giovanella L, Vrachimis A. Updated clinical management guidance during the COVID-19 pandemic: thyroid nodules and cancer. Eur J Endocrinol. 2022;186(4):G1‐G7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Kilsdonk ID, de Roos MP, Bresser P, Reesink HJ, Peringa J. Frequency and spectrum of incidental findings when using chest CT as a primary triage tool for COVID-19. Eur J Radiol Open. 2021;8:100366. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Canivet P, Desir C, Thys M, et al. The role of imaging in the detection of non-COVID-19 pathologies during the massive screening of the first pandemic wave. Diagnostics (Basel). 2022;12(7):1567. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Valluri S, Lakshmi HN, Sunkavalli C. Incidental findings in CT scans on screening for COVID-19. Indian J Surg Oncol. Published online October 26, 2022. Doi: 10.1007/s13193-022-01669-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Haugen BR, Alexander EK, Bible KC, et al. 2015 American thyroid association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: the American thyroid association guidelines task force on thyroid nodules and differentiated thyroid cancer. Thyroid. 2016;26(1):1‐133. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Kang KW, Kim SK, Kang HS, et al. Prevalence and risk of cancer of focal thyroid incidentaloma identified by 18F-fluorodeoxyglucose positron emission tomography for metastasis evaluation and cancer screening in healthy subjects. J Clin Endocrinol Metab. 2003;88(9):4100‐4104. [DOI] [PubMed] [Google Scholar]
- 26. Chen YK, Ding HJ, Chen KT, et al. Prevalence and risk of cancer of focal thyroid incidentaloma identified by 18F-fluorodeoxyglucose positron emission tomography for cancer screening in healthy subjects. Anticancer Res. 2005;25(2B):1421‐1426. [PubMed] [Google Scholar]
- 27. Davies L, Pichiotino E, Black WC, Tosteson AN. Developing a registry for thyroid incidentalomas: lessons learned and the path forward. Thyroid. 2016;26(5):650‐656. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Drake T, Gravely A, Westanmo A, Billington C. Prevalence of thyroid incidentalomas from 1995 to 2016: a single-center, retrospective cohort study. J Endocr Soc. 2020;4(1):bvz027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Uppal A, White MG, Nagar S, et al. Benign and malignant thyroid incidentalomas are rare in routine clinical practice: a review of 97,908 imaging studies. Cancer Epidemiol Biomarkers Prev. 2015;24(9):1327‐1331. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Tanpitukpongse TP, Grady AT, Sosa JA, Eastwood JD, Choudhury KR, Hoang JK. Incidental thyroid nodules on CT or MRI: discordance between what we report and what receives workup. AJR Am J Roentgenol. 2015;205(6):1281‐1287. [DOI] [PubMed] [Google Scholar]
- 31. Hoang JK, Langer JE, Middleton WD, et al. Managing incidental thyroid nodules detected on imaging: White Paper of the ACR incidental thyroid findings committee. J Am Coll Radiol. 2015;12(2):143‐150. [DOI] [PubMed] [Google Scholar]
- 32. Bahl M, Sosa JA, Eastwood JD, Hobbs HA, Nelson RC, Hoang JK. Using the 3-tiered system for categorizing workup of incidental thyroid nodules detected on CT, MRI, or PET/CT: how many cancers would be missed? Thyroid. 2014;24(12):1772‐1778. [DOI] [PubMed] [Google Scholar]
- 33. Bin Saeedan M, Aljohani IM, Khushaim AO, Bukhari SQ, Elnaas ST. Thyroid computed tomography imaging: pictorial review of variable pathologies. Insights Imaging. 2016;7(4):601‐617. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Shetty SK, Maher MM, Hahn PF, Halpern EF, Aquino SL. Significance of incidental thyroid lesions detected on CT: correlation among CT, sonography, and pathology. AJR Am J Roentgenol. 2006;187(5):1349‐1356. [DOI] [PubMed] [Google Scholar]
- 35. Lee C, Chalmers B, Treister D, et al. Thyroid lesions visualized on CT: sonographic and pathologic correlation. Acad Radiol. 2015;22(2):203‐209. [DOI] [PubMed] [Google Scholar]
- 36. Ní Mhuircheartaigh JM, Siewert B, Sun MR. Correlation between the size of incidental thyroid nodules detected on CT, MRI or PET-CT and subsequent ultrasound. Clin Imaging. 2016;40(6):1162‐1166. [DOI] [PubMed] [Google Scholar]
- 37. Gupta A, Obmann VC, Jordan M, et al. CT Artifacts after contrast media injection in chest imaging: evaluation of post-processing algorithms, virtual monoenergetic images and their combination for artifact reduction. Quant Imaging Med Surg. 2021;11(1):226‐239. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Zubov AD, Zubov AA, Zyatieva AP. Changes in thyroid nodules caused by COVID-19, according to ultrasound. First observations. Ultrasound Med Biol. 2022;48:S56. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Some or all datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.