| Literature DB >> 35521998 |
Alessia Cozzolino1, Tiziana Filardi1, Ilaria Simonelli2,3, Giorgio Grani4, Camilla Virili5, Ilaria Stramazzo5, Maria Giulia Santaguida5, Pietro Locantore6, Massimo Maurici3, Daniele Gianfrilli1, Andrea M Isidori1, Cosimo Durante4, Carlotta Pozza1.
Abstract
Context: Significant uncertainty exists about the diagnostic accuracy of ultrasonographic (US) features used to predict the risk of thyroid cancer in the pediatric population. Moreover, there are no specific indications for thyroid nodule evaluation in patients during the transition age. Objective: The meta-analysis aimed to address the following question: which thyroid nodule US features have the highest accuracy in predicting malignancy in the transition age.Entities:
Keywords: fine needle aspiration; thyroid cancer; thyroid nodules; transition age; ultrasonography
Year: 2022 PMID: 35521998 PMCID: PMC9254313 DOI: 10.1530/ETJ-22-0039
Source DB: PubMed Journal: Eur Thyroid J ISSN: 2235-0640
Figure 1Flowchart of literature eligibility assessment process.
Details of selected studies.
| Study name | Country | Objective of study | Study type | Reference standard | Number of patients (no of casesa) |
|---|---|---|---|---|---|
| Lyshchik 2005 | Belarus | To prospectively analyze the accuracy of various diagnostic criteria for cancer in solid thyroid nodules in children on the basis of gray-scale and power Doppler ultrasonographic findings. | Prospective study | Histopathology or FNA with follow-up | 103 (103) |
| Corrias 2008 | Italy | To investigate the association between juvenile autoimmune thyroiditis (JAT) and thyroid cancer in pediatric patients | Retrospective study | Histopathology or FNA with follow-up | 115 (48) |
| Roy 2011 | USA | To investigate clinical factors that may predict malignancy in pediatric thyroid nodules | Retrospective study | Histopathology | 207 (72) |
| Saavedra 2011 | Canada | To assess whether the presence of criteria for malignancy on the initial thyroid ultrasonography was helpful in diagnosing thyroid cancer even when a fine-needle aspiration biopsy (FNAB) suggests a benign lesion | Retrospective study | Histopathology | 35 (21) |
| Goldfarb 2012 | USA | To determine whether the preoperative clinic-based ultrasound (CBUS) characteristics of pediatric thyroid nodules were able to help further guide management and treatment | Retrospective study | Histopathology | 50 (50) |
| Mussa 2015 | Italy | To evaluate the diagnostic accuracy of clinical, laboratory, and ultrasound imaging characteristics of thyroid nodules in assessing the likelihood of malignancy | Retrospective study | Histopathology or FNA with follow-up | 184 (129) |
| Papendieck 2015 | Argentina | To highlight the findings of each diagnostic tool likely to differentiate benign from malignant thyroid nodules in a large cohort of pediatric patients | Prospective study | Histopathology or FNA with follow-up | 75 (75) |
| Canfarotta 2017 | USA | To evaluate the clinical utility of a modified pediatric McGill Thyroid Nodule Score (MTNS) with children and adolescents | Retrospective review | Histopathology | 46 (46) |
| Lim-Dunham 2017 | USA | To evaluate the diagnostic performance of pediatric thyroid nodule risk stratification for predicting malignancy when applying the ultrasound criteria recommended | Retrospective study | Histopathology or FNA with follow-up | 33 (33) |
| Hammond 2017 | USA | To evaluate the risk of thyroid cancer in incidental thyroid nodules discovered on CT in patients with a history of pediatric cancer | Retrospective review | Histopathology | 20 (6) |
| Richman 2018 | USA | To determine the relationship between demographic and sonographic characteristics of thyroid nodules and malignancy in a pediatric population | Retrospective study | Histopathology or FNA with follow-up | 314 (314) |
| Uner 2019 | Turkey | To define the diagnostic power of the TI-RADS risk stratification method in pediatric thyroid nodules. | Retrospective study | Histopathology or FNA with follow-up | 64 (64) |
| Lim-Dunham 2019 | USA | To assess the diagnostic performance of the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TIRADS) for malignancy risk in pediatric thyroid nodules | Retrospective study | Histopathology or FNA with follow-up | 62 (62) |
| Suh 2020 | Korea | To identify predictive factors of thyroid cancer | Retrospective study | Histopathology or FNA with follow-up | 275 (145) |
aNumber of patients refers to the whole population included in the study, whereas number of cases refers to the patients finally included in the analysis.
Figure 2Forest plot of sensitivity and specificity estimates of diagnostic accuracy of suspicious lymph nodes in predicting malignancy. Single studies are identified by first authors and publication year.
Figure 3Forest plot of sensitivity and specificity estimates of diagnostic accuracy of microcalcifications in predicting malignancy. Single studies are identified by first authors and publication year.
Figure 4Forest plot of sensitivity and specificity estimates of diagnostic accuracy of irregular margins in predicting malignancy. Single studies are identified by first authors and publication year.
Figure 5Forest plot of sensitivity and specificity estimates of diagnostic accuracy of ‘taller than wide’ shape in predicting malignancy. Single studies are identified by first authors and publication year.
Meta-analysis of binary diagnostic test accuracy of US features.
| Eco score | Ecogenicity | Ecotexture | Margins | Shape | Microcalcifications | Vascularization | Suspicious lymph nodes | |
|---|---|---|---|---|---|---|---|---|
| 3 | 12 | 8 | 10 | 7 | 11 | 8 | 7 | |
| 181 | 1163 | 893 | 1072 | 640 | 1118 | 988 | 888 | |
| Sensibility | 91.9% (61–98.8%) | 58% (46–70%) | 76% (26–97%) | 54% (36–72%) | 30% (12–58%) | 66% (55–76%) | 52% (25–78%) | 59% (48–69%) |
| Specificity | 51.8% (18.6–83.4%) | 66% (56–74%) | 71% (42–89%) | 89% (78–94%) | 93% (77–98%) | 86% (68–95%) | 65% (38–85%) | 98% (96–99%) |
| LR+ | 1.9 (0.9–3.8) | 1.7 (1.3–2.2) | 2.6 (1.6–4.4) | 4.8 (2.9–7.9) | 4.3 (1.7–10.7) | 4.9 (2.1–11.4) | 1.5 (0.8–2.6) | 23.7 (12.8–43.9) |
| LR- | 0.16 (0.04–0.5) | 0.63 (0.49–0.82) | 0.34 (0.08–1.40) | 0.52 (0.36–0.74) | 0.75 (0.56–1.01) | 0.39 (0.31–0.49) | 0.74 (0.47–1.18) | 0.42 (0.33–0.55) |
| DOR | 12.75 (4.57–35.59) | 3 (2–4) | 8 (2–32) | 9 (5–17) | 6 (2–16) | 13 (6–29) | 2 (1–5) | 56 (26–119) |
| AUC | 83% (52–92%) | 66% (62–70%) | 79% (75–82%) | 82% (78–85%) | 74% (70–78%) | 78% (74–81%) | 61% (57–66%) | 98% (96–99%) |
| I2 | 94.1% | 94% | 99.00% | 96.00% | 93.00% | 99% | 98% | 0% |
| P | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | 0.26 |
| Pub bias |
LR+, the positive likelihood ratio; LR−, the negative likelihood ratio; DOR, diagnostic odds ratio; I2, heterogeneity among the studies.
Figure 6Risk of bias assessments.