Journal
JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM
Volume 105, Issue 5, Pages -Publisher
ENDOCRINE SOC
DOI: 10.1210/clinem/dgz170
Keywords
thyroid nodule; ultrasound; systematic review; meta-analysis; diagnostic performance
Categories
Ask authors/readers for more resources
Context. Ultrasound (US) risk stratification systems (RSSs) have been developed to reduce the number of unnecessary fine-needle aspiration procedures (FNA) in patients with thyroid nodules. Objective. We conducted a systematic review and meta-analysis evaluating the ability of the 5 most common US RSSs for the appropriate selection of thyroid nodules for FNA. Data sources. This systematic review and meta-analysis was registered on PROSPERO (CRD42019131771). PubMed, CENTRAL, Scopus, and Web of Science were searched until March 2019. Study selection. Original articles reporting data on the performance of AACE/ACE/AME, ACR TI-RADS, ATA, EU-TIRADS, and K-TIRADS were included. Data extraction. The number of nodules classified as true negative, true positive, false negative, and false positive was extracted. Summary operating points were estimated using a random-effects model. Interobserver agreement was also assessed. Data synthesis. Twelve studies evaluating 18 750 thyroid nodules were included. Participants were adult outpatients with thyroid nodules submitted to either FNA or core-needle biopsy or surgery and with available US images. The final diagnosis for malignant nodules was generally based on histology, while cytology was used for benign nodules. Diagnostic odds ratio (DOR) ranged from 2.2 to 4.9. A head-to-head comparison showed a higher relative DOR for ACR-TIRADS versus ATA (P = .002) or K-TIRADS (P = .002), due to a higher relative likelihood ratio for positive results. Conclusions. The present meta-analysis found a higher performance of ACR TI-RADS in selecting thyroid nodules for FNA. However, the comparison across the most common US RSSs was limited by the data available. Further studies are needed to confirm this finding.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available