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. 2021 Nov 24;148(2):1–12. doi: 10.1001/jamaoto.2021.3387

Diagnostic Test Accuracy of Ultrasonography vs Computed Tomography for Papillary Thyroid Cancer Cervical Lymph Node Metastasis

A Systematic Review and Meta-analysis

Mostafa Alabousi 1, Abdullah Alabousi 2, Sami Adham 1, Alex Pozdnyakov 1, Sherif Ramadan 3, Hanu Chaudhari 3, J Edward M Young 4, Michael Gupta 4, Srinivasan Harish 2,✉
PMCID: PMC8613701  PMID: 34817554

This systematic review and meta-analysis compares ultrasonography and computed tomography in the preoperative evaluation of papillary thyroid cancer for cervical lymph node metastasis.

Key Points

Question

What is the most effective preoperative imaging to detect regional metastasis in papillary thyroid cancer?

Findings

In this systematic review and meta-analysis of 31 942 individuals, no difference between ultrasonography (US) and computed tomography (CT) was found for detection of lateral compartment cervical node metastasis in individuals with papillary thyroid cancer. For detection of central compartment metastasis, CT was more sensitive, while US was more specific.

Meaning

Computed tomography in assessment of cervical lymph node metastasis for papillary thyroid cancer may be warranted, possibly as an adjunct to US; further study is necessary to refine the role of CT imaging in this clinical scenario.

Abstract

Importance

The use of ultrasonography (US) vs cross-sectional imaging for preoperative evaluation of papillary thyroid cancer is debated.

Objective

To compare thyroid US and computed tomography (CT) in the preoperative evaluation of papillary thyroid cancer for cervical lymph node metastasis (CLNM), as well as extrathyroidal disease extension.

Data Sources

MEDLINE and Embase were searched from January 1, 2000, to July 18, 2020.

Study Selection

Studies reporting on the diagnostic accuracy of US and/or CT in individuals with treatment-naive papillary thyroid cancer for CLNM and/or extrathyroidal disease extension were included. The reference standard was defined as histopathology/cytology or imaging follow-up. Independent title and abstract review (2515 studies) followed by full-text review (145 studies) was completed by multiple investigators.

Data Extraction and Synthesis

PRISMA guidelines were followed. Methodologic and diagnostic accuracy data were abstracted independently by multiple investigators. Risk of bias assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool independently and in duplicate. Bivariate random-effects model meta-analysis and multivariable meta-regression modeling was used.

Main Outcomes and Measures

Diagnostic test accuracy of US and CT of the neck for lateral and central compartment CLNM, as well as for extrathyroidal disease extension, determined prior to study commencement.

Results

A total of 47 studies encompassing 31 942 observations for thyroid cancer (12 771 with CLNM; 1747 with extrathyroidal thyroid extension) were included; 21 and 26 studies were at low and high risk for bias, respectively. Based on comparative design studies, US and CT demonstrated no significant difference in sensitivity (73% [95% CI, 64%-80%] and 77% [95% CI, 67%-85%], respectively; P = .11) or specificity (89% [95% CI, 80%-94%] and 88% [95% CI, 79%-94%], respectively; P = .79) for lateral compartment CLNM. For central compartment metastasis, sensitivity was higher in CT (39% [95% CI, 27%-52%]) vs US (28% [95% CI, 21%-36%]; P = .004), while specificity was higher in US (95% [95% CI, 92%-98%]) vs CT (87% [95% CI, 77%-93%]; P < .001). Ultrasonography demonstrated a sensitivity of 91% (95% CI, 81%-96%) and specificity of 47% (95% CI, 35%-60%) for extrathyroidal extension.

Conclusions and Relevance

The findings of this systematic review and meta-analysis suggest that further study is warranted of the role of CT for papillary thyroid cancer staging, possibly as an adjunct to US.

Introduction

Papillary thyroid cancer (PTC) accounts for 85% to 90% of well-differentiated thyroid cancers.1 Regional lymph node metastasis has been reported in up to 36% of adults with PTC.2 Preoperative evaluation of papillary thyroid cancer is a controversial and much-debated topic. The American Thyroid Association3 and the United Kingdom National Multidisciplinary Guidelines4 recommend the utilization of ultrasonography (US) for the preoperative imaging evaluation of primary thyroid tumors and cervical lymph node basins to assess for nodal metastasis. Cross-sectional imaging with computed tomography (CT) or magnetic resonance imaging (MRI) is only recommended in a small number of cases, including to assess extent of tumor invasion or bulky nodal metastasis, or if US expertise is not readily available.3,4,5

A study by Bongers et al6 recently demonstrated that CT changed the surgical management in 22.5% of patients, even with clinical low-risk differentiated thyroid cancer, owing to the detection of clinically significant lymph nodes not visualized on US. This suggests that CT may play a role in conjunction with US for better localization of cervical lymph node metastasis (CLNM) and consequently may improve surgical management of PTC.6 Our primary objective was to perform a diagnostic test accuracy (DTA) systematic review and meta-analysis to compare thyroid US vs CT in the preoperative evaluation of PTC for CLNM in treatment-naive patients, stratified by lateral (levels I-V) vs central (level VI) CLNM.5,7 Our secondary objective was to evaluate the diagnostic accuracy of US vs CT for extrathyroidal disease extension in individuals with PTC.

Methods

A protocol for this study was registered a priori on the International Prospective Register of Systematic Reviews (PROSPERO; CRD42020198421). A DTA systematic review and meta-analysis was performed based on relevant contemporary guidelines: the Cochrane Handbook for Screening and Diagnostic Tests and the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline for DTA systematic reviews.8,9,10,11 The institutional review board requirement was waived per institutional policy of the Hamilton Integrated Research Ethics Board (HIREB) because all data used were available in the public domain.

Literature Search

A systematic literature search of the electronic databases MEDLINE and Embase was performed to identify all relevant studies. The search encompassed studies within the databases from January 1, 2000, to July 18, 2020, the date the search was conducted. Details of the search strategy are included in eMethods in the Supplement. Results of the literature search were imported into a reference manager software (Reference Manager 11; Thomson Reuters).

Eligibility Criteria

Inclusion criteria were defined as the following: (a) the population assessed in the study consisted predominantly of individuals with treatment-naive PTC (>90% of the total sample); (b) the individuals included in each study underwent US and/or CT imaging of the neck to assess for CLNM and/or extrathyroidal disease extension; (c) the reference standard used in each study was histopathology/cytology or imaging follow-up; and (d) the results of each study reported sufficient data to determine sensitivity and specificity for CLNM (lateral compartment, central compartment, or unspecified) and/or extrathyroidal disease extension. Exclusion criteria were defined as the following: (a) the study investigated nonpapillary thyroid cancer subtypes making up at least 10% of the total population sample; (b) the study reported on multiple subtypes of thyroid cancer and did not stratify according by subtype to allow extraction of PTC-specific data; and (c) the study assessed individuals with PTC who had undergone previous therapy.

Study Selection

Independent title and abstract review was completed by multiple investigators independently (M.A., S.A., A.P., S.R., H.C.). A pilot screen was conducted for the first 50 studies in duplicate to improve familiarity and consistency for the remaining ones. The full texts of potentially eligible articles were retrieved and assessed for inclusion by 2 investigators independently (M.A., A.A.). Discrepancies were resolved by consensus. Persistent discrepancies were discussed with a third reviewer (S.H.).

Data Extraction

Data extraction was performed independently on included studies by multiple investigators (M.A., S.A., A.P., S.R., H.C.). A pilot phase was performed for the first 3 studies for all data extractors to improve consistency among reviewers. Discrepancies were resolved by consensus. Persistent discrepancies were discussed with a third reviewer.

The following data metrics were extracted into a spreadsheet program (Microsoft Excel 2016; Microsoft Corporation) using predefined forms: first author, title of study, publication year, country of corresponding author, journal of publication, study design, patient demographics (sample size, indication for imaging, number of thyroid cancer cases and subtype where applicable, mean age of population, setting of patient sample, and reasons for patient exclusion), proportion of CLNM (stratified by lateral and central compartment, where applicable), proportion of cases with extrathyroidal extension, type of imaging used, imaging protocol (where applicable), reference standard used (histopathology and/or imaging follow-up), and 2 × 2 contingency table data (true positives, false negatives, true negatives, and false positives) for CLNM and/or extrathyroidal extension of disease by imaging modality, where applicable.

Quality Assessment

A quality assessment of all included studies was conducted using the revised tool for the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2).12 Multiple investigators (M.A., S.A., A.P., S.R., H.C.) assessed all articles independently, in duplicate, for the following criteria: patient selection, index test, reference standard, and flow and timing.12 The following criteria were defined as being at high risk of bias for each respective category: (1) random or consecutive patient selection was not used (patient selection); (2) radiologists reviewing the index test were not blinded to previous clinical/imaging data (index test); (3) different imaging protocols were used across the sample population (index test); (4) a reference standard of imaging follow-up for less than 1 year was used (reference standard); (5) different reference standard protocols were used across a sample population (reference standard); (6) the index test and reference standard were performed more than 2 months apart (flow and timing); and (7) if more than 1 index test was performed in patients, the tests were performed more than 2 months apart (flow and timing). The overall risk of bias was considered high for a study if at least 1 category was considered at high risk for bias, or if at least 2 categories were at an unclear risk for bias. A pilot was performed by all reviewers in duplicate for the initial 3 studies to improve consistency. Discrepancies were resolved by consensus.

Outcomes

The primary outcomes were defined as estimates of the mean DTA for US and CT of the neck for CLNM in individuals with PTC, stratified by lateral vs central compartment, where applicable. The secondary outcomes were defined as estimates of the mean DTA for US and CT in assessing extrathyroidal disease extension in individuals with PTC. The following covariates were assessed within a meta-regression model when the data were sufficiently heterogeneous: imaging modality (CT vs US), study design (retrospective vs prospective design; single vs multicenter), compartment for CLNM (central vs lateral), study analysis method (per patient vs per lymph node), and risk of bias.

Data Synthesis and Statistical Methods

A bivariate random-effects model meta-analysis was performed to determine estimates of the mean for the sensitivity and specificity of US and CT for CLNM in individuals with PTC, stratified by lateral vs central compartment, with 95% CIs.8,13 Estimates of the mean sensitivity and specificity of US and CT for extrathyroidal disease extension in individuals with PTC were also determined if sufficient studies were available. Coupled forest plots and hierarchical summary receiver operating characteristic curves were created using the estimated model parameters. If separate data were provided for multiple readers within a study, reader data were averaged for analysis.14 Pooling and meta-regression was performed for each imaging modality and cervical compartment independently. In addition, combined multivariate comparative meta-regression models were used with the inclusion of imaging modality as a covariate for (1) lateral compartment, (2) central compartment, and (3) general CLNM. The general CLNM category (eResults in the Supplement) consisted of studies that did not stratify by cervical compartment, as well as studies that reported on both central and lateral compartment metastasis (combined for the purposes of this analysis). Furthermore, comparative meta-regression models were used for each imaging modality with the inclusion of cervical compartment as a covariate. For the comparative meta-regression models, only comparative design studies for the covariate of interest were used for the analysis. For example, when comparing the DTA of US vs CT for lateral compartment CLNM, only studies that performed both US and CT in the sample population were included in the analysis. Sources for variability in accuracy were explored through meta-regression. Per contemporary guidance for diagnostic accuracy systematic reviews, publication bias was not assessed.8,9,10 Analysis was performed using the “midas,” “metandi,” and “metaprop” packages in Stata, version 11.2 (StataCorp LLC), as well as the “mada” package in R, version 3.5.1 (R Foundation for Statistical Computing).13,15,16,17,18

Results

Study Demographics and Risk of Bias

A total of 47 studies were included, with 31 942 observations of individuals with thyroid cancer.19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65 A summary study flow diagram is shown in Figure 1. The summary of included studies is provided in Table 1. Of the 31 942 observations, 31 895 were for patients with PTC, while the remaining 47 observations were for other subtypes of thyroid cancer (follicular, medullary, and anaplastic subtypes). In total, 12 771 observations of CLNM and 1747 observations of extrathyroidal extension of disease were included. There were 44 studies for a total of 20 465 observations for US, 15 studies for a total of 11 055 observations for CT, and 2 studies for a total of 300 observations for MRI, which reported on the diagnostic accuracy of the imaging modality for CLNM or extrathyroidal extension of disease in thyroid cancer.

Figure 1. Study Flow Diagram.

Figure 1.

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Table 1. Characteristics of Included Studies.

Source Modality Metastasis type Findings, No. Age, mean or median, y Design Center Observation
Location TP FN TN FP
Abboud et al,65 2020 US Nodal Central 97 44 46 19 56 Retrospective Single Per patient
Lateral 29 5 15 8
Ahn et al,64 2008 CT Nodal Central 23 8 7 9 47 Retrospective Single Per lymph node
Lateral 66 18 39 11
US Central 17 14 11 5
Lateral 55 29 41 9
Chen et al,63 2020 US Nodal General 22 7 22 4 47 Retrospective Single Per lymph node
Choi et al,62 2015 US Nodal Central 47 158 395 25 44 Retrospective Single Per patient
Lateral 41 6 568 10
ETE NA 239 39 178 169
Chong et al,61 2017 CT Nodal Central 11 24 132 3 54 Retrospective Multiple Per lymph node
Lateral 11 25 286 6
Du et al,60 2015 US Nodal General 105 24 108 9 50 Retrospective Single Per lymph node
Eun et al,59 2018 US Nodal General 381 89 184 98 44 Retrospective Single Per lymph node
CT 210 25 26 115
Gao et al,45 2020 US Nodal Central 168 108 190 152 43 Prospective Single Per patient
González et al,27 2007 US Nodal NA 11 1 47 1 40 Prospective Single Per lymph node
Gweon et al,26 2014 US ETE NA 41 11 7 20 46 Retrospective Single Per patient
Hong et al,25 2017 US Nodal NA 140 22 116 41 50 Prospective Single Per lymph node
Hu et al,24 2020 US ETE NA 68 17 117 23 40 Retrospective Single Per patient
MRI 65 20 131 9
Hwang & Orloff,23 2011 US Nodal Central 9 21 33 5 48 Prospective Single Per lymph node
Lateral 26 2 32 13
Jeong et al,22 2006 US Nodal Central 5 15 30 2 44 Prospective Single Per lymph node
Lateral 14 12 229 5
CT Central 5 15 30 2
Lateral 11 15 226 8
Jiao & Zhang,36 2017 US ETE NA 126 11 21 44 46 Retrospective Single Per lymph node
Kamaya et al,35 2015 US ETE NA 16 0 12 34 49 Retrospective Single Per patient
Khokhar et al,34 2015 US Nodal Central 39 65 111 12 51 Retrospective Single Per patient
Kim et al,33 2008 US Nodal Central 26 26 74 7 48 Retrospective Single Per lymph node
Lateral 39 14 86 5
Kim et al,32 2014 US ETE NA 67 0 5 3 46 Retrospective Single Per patient
MRI 59 8 6 2
Kim et al,31 2017 US Nodal Central 864 2278 3295 140 48 Retrospective Single Per lymph node
CT 1221 1921 3129 306
Lee CY et al,48 2012 US Nodal Lateral 25 3 55 26 54 Retrospective Single Per patient
Lee CY et al,30 2014 US ETE NA 229 46 202 91 48 Retrospective Single Per patient
Lee DW et al,29 2013 US Nodal Central 31 102 267 10 49 Retrospective Single Per lymph node
Lateral 52 22 62 12
CT Central 55 78 249 28
Lateral 61 13 47 27
Lee DY et al,58 2014 US ETE NA 162 12 38 165 49 Retrospective Single Per patient
CT 150 24 61 142
Lee Y et al,57 2018 US Nodal Central 39 180 307 5 47 Prospective Multiple Per lymph node
Lateral 117 22 94 10
CT Central 58 161 263 49
Lateral 130 19 94 10
Lesnik et al,28 2014 US Nodal Central 10 27 90 5 NA Prospective Multiple Per lymph node
Lateral 28 7 25 4
CT Central 19 18 89 6
Lateral 28 7 24 5
Li et al,46 2019 US Nodal Lateral 55 15 19 10 42 Retrospective Single Per lymph node
CT 55 15 27 2
Liu et al,47 2017 US Nodal NA 37 1 2 4 47 Retrospective Single Per lymph node
Mizrachi et al,56 2014 US Nodal Central 20 1 4 39 42 Prospective Single Per patient
Na et al,43 2015 US Nodal Central 14 67 89 6 43 Retrospective Single Per lymph node
Lateral 9 5 148 14
CT Central 19 62 79 16
Lateral 10 4 150 12
Park et al,41 2009 US Nodal Central 7 24 70 1 47 Prospective Single Per lymph node
Lateral 16 5 18 6
ETE NA 29 5 42 18
Park et al,42 2017 CT Nodal Lateral 150 27 144 6 47 Retrospective Single Per lymph node
Patel et al,40 2016 US Nodal NA 15 8 41 7 47 Retrospective Single Per lymph node
Ramundo et al,55 2020 US ETE NA 44 0 31 53 50 Prospective Single Per patient
Roh et al,39 2009 US Nodal Central 68 51 254 6 48 Prospective Single Per lymph node
Lateral 73 7 114 7
Shim et al,38 2013 US Nodal Central 63 63 10 7 49 Retrospective Single Per lymph node
Lateral 227 92 182 71
Soler et al,54 2008 CT Nodal Lateral 11 13 23 9 47 Retrospective Single Per lymph node
Stulak et al,37 2006 US Nodal NA 198 33 433 38 44 Retrospective Single Per patient
Sugitani et al,53 2008 US Nodal Central 40 96 86 9 52 Prospective Single Per lymph node
Lateral 127 0 0 3
Tao et al,52 2020 US Nodal NA 51 76 132 16 46 Retrospective Single Per patient
Wei et al,21 2018 US Nodal NA 69 20 24 8 NA Retrospective Single Per lymph node
CT 34 20 24 16
Wei et al,19 2014 US ETE NA 120 122 70 57 NA Prospective Single Per patient
Xiang et al,20 2014 US Nodal Lateral 47 18 12 5 42 Retrospective Single Per patient
Yi et al,51 2016 US ETE NA 16 1 22 31 49 Retrospective Single Per lymph node
Yoo et al,50 2013 US Nodal Central 39 12 51 22 48 Retrospective Single Per lymph node
Yoo et al,44 2020 US Nodal NA 115 16 109 34 47 Retrospective Single Per lymph node
CT 123 2 116 27
Zhang et al,49 2020 US Nodal NA 161 169 274 61 41 Retrospective Single Per patient
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Abbreviations: CT, computed tomography; ETE, extrathyroidal extension; FN, false negative; FP, false positive; MRI, magnetic resonance imaging; NA, not applicable; TN, true negative; TP, true positive; US, ultrasound.

The risk of bias for each study is summarized in Table 2. In total, 21 studies were at a low risk for bias, while the remaining 26 studies were at a high risk for bias. Contributors to a high risk for study bias included the following: convenience sample used or lack of reporting of sampling method; inconsistent imaging protocols used for a sample population; lack of blinding of radiologists to prior imaging/clinical history when reporting imaging examinations; nonradiologist clinicians performing/interpreting the US examinations; inconsistent histopathological reference standard used for a sample population; and lack of reporting or greater than 2-month time period between imaging index tests, or between the imaging index test and histopathology reference standard.

Table 2. Summary of Risk of Bias Based on Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) Tool for Each Included Study.

Source Risk of bias Sampling Index Reference Flow
Abboud et al,65 2020 Low Low Unclear Low Low
Ahn et al,64 2008 Low Low Low Low Low
Chen et al,63 2020 Low Low Low Low Low
Choi et al,62 2015 High High High Low Low
Chong et al,61 2017 High Low Unclear High Low
Du et al,60 2015 Low Low Unclear Low Low
Eun et al,59 2018 High Low High Low Unclear
Gao et al,45 2020 High Unclear High Low Unclear
González et al,27 2007 High Low Unclear High Unclear
Gweon et al,26 2014 Low Low Low Low Low
Hong et al,25 2017 Low Low Low Low Low
Hu et al,24 2020 Low Low Low Low Low
Hwang & Orloff,23 2011 High Unclear High Low Unclear
Jeong et al,22 2006 Low Low Low Low Low
Jiao & Zhang,36 2017 High Low Unclear Low Unclear
Kamaya et al,35 2015 Low Unclear Low Low Low
Khokhar et al,34 2015 High Unclear Unclear Low Unclear
Kim et al,33 2008 Low Low Low Low Low
Kim et al,32 2014 Low Unclear Low Low Low
Kim et al,31 2017 High Unclear High Low High
Lee CY et al,48 2012 High High High Low Low
Lee CY et al,30 2014 Low Low Low Low Unclear
Lee DW et al,29 2013 High Unclear Unclear Low Unclear
Lee DY et al,58 2014 High Low Unclear Unclear Unclear
Lee Y et al,57 2018 Low Low Low Low Low
Lesnik et al,28 2014 Low Unclear Low Low Low
Li et al,46 2019 Low Low Low Low Unclear
Liu et al,47 2017 High High Unclear Low Unclear
Mizrachi et al,56 2014 Low Low Unclear Low Low
Na et al,43 2015 High Unclear Unclear Low Low
Park et al,41 2009 Low Low Low Low Low
Park et al,42 2017 High High Low Low Unclear
Patel et al,40 2016 High Unclear Unclear Low Unclear
Ramundo et al,55 2020 Low Low Low Low Low
Roh et al,39 2009 High Low Unclear High Unclear
Shim et al,38 2013 Low Low Low Low Unclear
Soler et al,54 2008 Low Low High Low Low
Stulak et al,37 2006 High Unclear Unclear High Unclear
Sugitani et al,53 2008 High High Low High Low
Tao et al,52 2020 High High Low Low Unclear
Wei et al,19 2014 Low Low Low Low Low
Wei et al,21 2018 High Low Unclear Low Unclear
Xiang et al,20 2014 High Low Unclear Low Unclear
Yi et al,51 2016 High Unclear High High Low
Yoo et al,50 2013 High Unclear Low High Unclear
Yoo et al,44 2020 High High High Low Low
Zhang et al,49 2020 High Low High High Unclear
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Data Pooling and Meta-regression

Lateral Compartment CLNM

For US, there were 17 studies that reported on 3162 observations of individuals with thyroid cancer, 1244 of which demonstrated lateral compartment CLNM. The pooled estimates of the mean sensitivity, specificity, and area under the curve (AUC) of US for lateral compartment CLNM were 82% (95% CI, 74%-88%), 84% (95% CI, 74%-91%), and 0.90 (95% CI, 0.87-0.92), respectively (Figure 2A and B). For CT, there were 10 studies that reported on 1845 observations of individuals with thyroid cancer, 689 of which demonstrated lateral compartment CLNM. The pooled estimates of the mean sensitivity, specificity, and AUC of CT for lateral compartment CLNM were 72% (95% CI, 59%-82%), 90% (95% CI, 83%-95%), and 0.89 (95% CI, 0.86-0.92), respectively (Figure 2C and D).

Figure 2. Pooled Sensitivity and Specificity Forest Plots for US (A) and CT (B), as Well as hsROC Curves for US (C) and CT (D) for Lateral Compartment CLNM in Thyroid Cancer.

Figure 2.

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AUC indicates area under the curve; CLNM, cervical lymph node metastasis; CT, computed tomography; hsROC, hierarchical summary receiver operating characteristic; US, ultrasonography.

There were 8 studies identified that directly compared the diagnostic accuracy of US and CT for lateral compartment CLNM in thyroid cancer. Based on comparative meta-regression modeling of these studies, shown in eTable 1 in the Supplement, there was no significant difference in sensitivity (P = .11) or specificity (P = .79) between US and CT. Of note, the pooled sensitivity and specificity for the comparative design studies was 73% (95% CI, 64%-80%) and 89% (95% CI, 80%-94%), respectively, for US, and 77% (95% CI, 67%-85%) and 88% (95% CI, 79%-94%), respectively, for CT. Sensitivity was highest in retrospective (P < .001) and multicenter (P < .001) studies, while specificity was highest in prospective (P < .001) and single center (P = .02) studies within this model. Meanwhile, risk of bias was not associated with sensitivity (P = .75) or specificity (P = .41) (supporting data in eTable 1 in the Supplement).

Central Compartment CLNM

For US, there were 19 studies that reported on 10 845 observations of individuals with thyroid cancer, 4955 of which demonstrated central compartment CLNM. The pooled estimates of the mean sensitivity, specificity, and AUC of US for central compartment CLNM were 41% (95% CI, 30%-53%), 89% (95% CI, 80%-94%), and 0.69 (95% CI, 0.65-0.73), respectively (Figure 3A and B). For CT, there were 8 studies that reported on 8095 observations of individuals with thyroid cancer, 3698 of which demonstrated central compartment CLNM. The pooled estimates of the mean sensitivity, specificity, and AUC of CT for central compartment CLNM were 38% (95% CI, 27%-49%), 89% (95% CI, 80%-94%), and 0.66 (95% CI, 0.61-0.70), respectively (Figure 3C and D).

Figure 3. Pooled Sensitivity and Specificity Forest Plots for US (A) and CT (B), as Well as hsROC Curves for US (C) and CT (D) for Central Compartment CLNM in Thyroid Cancer.

Figure 3.

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AUC indicates area under the curve; CLNM, cervical lymph node metastasis; CT, computed tomography; hsROC, hierarchical summary receiver operating characteristic; US, ultrasonography.

There were 7 studies that directly compared the diagnostic accuracy of US and CT for central compartment CLNM in thyroid cancer. Based on comparative meta-regression modeling of these studies, shown in eTable 2 in the Supplement, sensitivity was significantly higher for CT (P = .004), while specificity was significantly higher for US (P < .001). Of note, the pooled sensitivity and specificity for the comparative design studies was 28% (95% CI, 21%-36%) and 95% (95% CI, 92%-98%), respectively, for US, and 39% (95% CI, 27%-52%) and 87% (95% CI, 77%-93%), respectively, for CT. Sensitivity was highest in retrospective (P = .002) studies at low risk for bias (P < .001), while specificity was highest in prospective (P < .001) studies at high risk for bias (P < .001). A single or multicenter trial setting was not associated with sensitivity (P = .80) or specificity (P = .97) (supporting data in eTable 2 in the Supplement).

Extrathyroidal Disease Extension

For US, there were 12 studies that reported on 2874 observations of individuals with thyroid cancer, 1421 of which demonstrated extrathyroidal thyroid extension. The pooled estimates of the mean sensitivity, specificity, and AUC of US for extrathyroidal extension were 91% (95% CI, 81%-96%), 47% (95% CI, 35%-60%), and 0.76 (95% CI, 0.72-0.80), respectively (eFigure 1 in the Supplement). For CT, only 1 study reported on extrathyroidal disease extension, with a total of 377 individuals with thyroid cancer, 174 of which demonstrated extrathyroidal extension, with a reported sensitivity of 86% and specificity of 30%. For MRI, 2 studies reported on extrathyroidal disease extension, with a total of 300 individuals with thyroid cancer, 152 of which demonstrated extrathyroidal extension, with sensitivities of 76% to 88% and specificities of 75% to 93%.

Imaging modalities were not compared by meta-regression because there was an insufficient number of studies. A meta-regression model assessing multiple covariates for US found that study design, observation type, and risk of bias was not associated with diagnostic accuracy (P = .27-.97; supporting data in eTable 3 in the Supplement).

General Compartment CLNM

Findings for the general CLNM category are available in eResults in the Supplement. The pooled estimates of the mean sensitivity, specificity, and AUC for general compartment CLNM for US and CT are shown in eFigure 2 in the Supplement. Comparative meta-regression modeling findings of the general compartment CLNM studies are shown in eTables 4-6 in the Supplement.

Discussion

We performed a systematic review and meta-analysis to assess the diagnostic accuracy of US and CT in the detection of CLNM and extrathyroidal disease extension for 31 942 observations of individuals with thyroid cancer (predominantly papillary subtype) in 47 studies. We found no significant difference in sensitivity and specificity between US and CT for lateral compartment CLNM. Although both US and CT demonstrated a low overall sensitivity for central compartment CLNM, CT demonstrated a higher sensitivity, while US demonstrated a higher specificity.

The American Thyroid Association and the United Kingdom National Multidisciplinary Guidelines recommend US for preoperative assessment of CLNM, particularly the lateral compartment, in individuals with PTC undergoing thyroidectomy.3,4,5 Our study demonstrated that CT may be used in specific cases to assess lateral compartment CLNM, and further study of the role of CT for CLNM is warranted, including as a potential adjunct to US. Moreover, we were limited in the assessment of the optimal CT technique for CLNM, but Park et al42 found that arterial phase CT improved the diagnostic accuracy for lateral compartment CLNM in PTC, which may be further assessed.

We found that CT was more sensitive and less specific than US for the central compartment CLNM, but it may provide an added benefit for staging. Bongers et al6 found that CT changed the surgical management in 22.5% of individuals with clinically low-risk differentiated thyroid cancer based on the identification of clinically significant lymph nodes not visualized on US. The majority of the 22.5% of individuals with changes in surgical management were based on central compartment CLNM not seen on US.6 Furthermore, the added value of CT to US as an adjunct for central compartment CLNM in individuals with thyroid cancer was assessed in 2 studies, which found an improved sensitivity compared with US and CT alone.44,57 Computed tomography may also provide the added benefit of imaging the mediastinum and lungs for treatment planning. Based on these findings, further study of the role of CT, as well as the combination of US and CT for central compartment CLNM, is warranted.

Likewise, both US and CT were fairly sensitive for extrathyroidal disease extension in individuals with thyroid cancer (86%-91%), but they demonstrated a fairly low specificity (30%-47%). In contrast, 2 studies reporting on the utility of MRI for extrathyroidal disease extension reported comparable sensitivities (76%-88%), with much higher specificities (75%-93%) compared with US and CT.24,32 Furthermore, a study assessing the utility of MRI in differentiated thyroid cancer reported a promising sensitivity of 75% for central compartment CLNM.66 If there is a future role for MRI in preoperative staging for PTC, rigorous prospective studies to assess the diagnostic accuracy of MRI vs US and CT are warranted.

Limitations

This study had multiple limitations. The studies included assessed diagnostic accuracy and do not necessarily reflect changes in surgical management. We were limited in the number of studies reporting on the diagnostic accuracy of extrathyroidal disease extension for thyroid cancer on CT. A search of the gray literature was not performed. Although studies were excluded if the full text was not available in English, this did not result in the exclusion of any relevant studies identified during the title and abstract screening phase. Moreover, multiple included studies were considered at high risk for bias, but this was accounted for via meta-regression.

Conclusions

In summary, this systematic review and meta-analysis found no significant difference in the diagnostic accuracy between US and CT for lateral compartment CLNM in PTC. Meanwhile, CT was more sensitive for central compartment CLNM, while US was more specific. These findings support further study of the role of CT for CLNM, including as a possible adjunct to US.

Supplement.

eResults.

eTable 1: Comparative multivariate meta-regression model evaluating the impact of different covariates on the diagnostic accuracy of lateral compartment cervical lymph node metastasis in thyroid cancer for US vs CT

eTable 2: Comparative multivariate meta-regression model evaluating the impact of different covariates on the diagnostic accuracy of central compartment cervical lymph node metastasis in thyroid cancer for US vs CT

eTable 3: Multivariate meta-regression model evaluating the impact of different covariates on the diagnostic accuracy of US for extrathyroidal disease extension in thyroid cancer

eTable 4: Comparative multivariate meta-regression model evaluating the impact of different covariates on the diagnostic accuracy of general cervical lymph node metastasis in thyroid cancer for US vs CT

eTable 5: Comparative multivariate meta-regression model evaluating the impact of different covariates on the diagnostic accuracy of US for lateral vs central compartment cervical lymph node metastasis in thyroid cancer

eTable 6: Comparative multivariate meta-regression model evaluating the impact of different covariates on the diagnostic accuracy of CT for lateral vs central compartment cervical lymph node metastasis in thyroid cancer

eFigure 1: Pooled Sensitivity and Specificity Forest Plots (A) and hsROC Curves (B) for US in the detection of extrathyroidal disease extension for thyroid cancer

eFigure 2: Pooled Sensitivity and Specificity Forest Plots for US (A) and CT (B), as well as hsROC Curves for US (C) and CT (D) for general cervical lymph node metastasis in thyroid cancer

eMethods: Search Strategies

Click here for additional data file. (301KB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement.

eResults.

eTable 1: Comparative multivariate meta-regression model evaluating the impact of different covariates on the diagnostic accuracy of lateral compartment cervical lymph node metastasis in thyroid cancer for US vs CT

eTable 2: Comparative multivariate meta-regression model evaluating the impact of different covariates on the diagnostic accuracy of central compartment cervical lymph node metastasis in thyroid cancer for US vs CT

eTable 3: Multivariate meta-regression model evaluating the impact of different covariates on the diagnostic accuracy of US for extrathyroidal disease extension in thyroid cancer

eTable 4: Comparative multivariate meta-regression model evaluating the impact of different covariates on the diagnostic accuracy of general cervical lymph node metastasis in thyroid cancer for US vs CT

eTable 5: Comparative multivariate meta-regression model evaluating the impact of different covariates on the diagnostic accuracy of US for lateral vs central compartment cervical lymph node metastasis in thyroid cancer

eTable 6: Comparative multivariate meta-regression model evaluating the impact of different covariates on the diagnostic accuracy of CT for lateral vs central compartment cervical lymph node metastasis in thyroid cancer

eFigure 1: Pooled Sensitivity and Specificity Forest Plots (A) and hsROC Curves (B) for US in the detection of extrathyroidal disease extension for thyroid cancer

eFigure 2: Pooled Sensitivity and Specificity Forest Plots for US (A) and CT (B), as well as hsROC Curves for US (C) and CT (D) for general cervical lymph node metastasis in thyroid cancer

eMethods: Search Strategies

Click here for additional data file. (301KB, pdf)

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