Abstract
BACKGROUND: Conflicting evidence exists on the predictive value of ultrasound characteristics for BRAFV600E gene expression in thyroid cancer. This study aimed to determine the predictive value of ultrasound features for BRAFV600E gene expression status in thyroid cancer. METHODS: A systematic review of studies published before December 31, 2023, was conducted in the PubMed, Web of Science, and Cochrane Library databases. Studies evaluating the ultrasonographic features for predicting BRAFV600E gene mutations in thyroid cancer were included. The relevant data were extracted, and the quality of eligible studies was independently assessed by two reviewers. Statistical analysis was performed using RevMan 5.4 and Stata 12.0 software. RESULTS: The meta-analysis included 13 studies involving a total of 2,250 thyroid cancer patients. Ultrasound features significantly associated with BRAFV600E gene expression status in thyroid cancer (P<0.05) comprised hypoechogenicity, absence of halo, irregular borders, and vertical orientation. Contrastingly, no significant differences were observed in solid composition, irregular shape, and microcalcifications (P>0.05). Among the seven ultrasound features, the ones with superior combined sensitivity for nodules were hypoechogenicity, solid composition, absence of halo, and irregular borders, with sensitivities of 0.93 [95% confidence interval (CI): 0.87-0.96], 0.93 (95% CI: 0.86-0.97), 0.83 (95% CI: 0.72-0.91), and 0.74 (95% CI: 0.64-0.83), respectively. Finally, the areas under the summary receiver operating characteristic (SROC) curve with the highest diagnostic performance were the absence of halo and hypoechogenicity, with area under the curve (AUC) of 0.84 (95% CI: 0.80-0.87) and 0.81 (95% CI: 0.77-0.84), respectively. CONCLUSIONS: The expression status of the BRAFV600E gene in thyroid cancer correlates with nodules exhibiting hypoechogenicity, absence of halo, irregular borders, and taller-than-wide shape. Notably, the absence of a halo and hypoechogenicity were identified as the most predictive ultrasonic features. However, due to the limited sample size, there may be bias in the meta-analysis results, and more extensive research is necessary.