Objective: To investigate the role of logistic prediction model based on two-dimensional ultrasound image features to assess the risk of BRAF V600E mutation in TI-RADS 4a thyroid nodules. Methods: Patients with TI-RADS category 4a thyroid nodules who underwent post-puncture BRAF V600E gene testing at Yijishan Hospital, Wannan Medical College from January 2021 to September 2021 were collected, and data on ultrasound, pathology and gender and age of selected patients were obtained for retrospective analysis. The patients were divided into the BRAF V600E positive group(n=130)and BRAF V600E negative group(n=104)according to the gene test results. Multiple logistic regression models of two-dimensional ultrasound were constructed based on ultrasound image characteristics. The predictive efficacies of the models were assessed by ROC(receiver operating characteristic)curves and calibration curves, and the area under curve AUC was compared between the models using the Delong test. Results: The AUC for the training set obtained from the logistic echo model was 0.885 5(95%CI: 0.833 8-0.937 2), with specificity, sensitivity and accuracy of 67.0%, 96.0% and 83.0% respectively. The AUC of the validation set was 0.859 3(95%CI: 0.771 9-0.946 7), with specificity, sensitivity and accuracy of 79.0%, 84.0% and 82.0% respectively. Delong test results showed no statistically significant difference in AUC between the training and validation set models(P＞0.05). good clinical efficacy of the prediction models(total point 0.9)was showed by column plots. The calibration curve showed high agreement between the actual and predicted probabilities(C-index 0.9). Conclusion: Logistic regression models constructed based on 2D ultrasound image features can be used as a simple and non-invasive quantitative tool to predict the risk of BRAF V600E mutation in TI-RADS 4a thyroid nodules.
HUANG Jinyan
,
ZHANG Liangxi
. Logistic model based on ultrasound imaging features in predicting the risk of BRAF V600E mutation in TI-RADS 4a thyroid nodules[J]. Journal of Baotou Medical College, 2025
, 41(5)
: 1
-5
.
DOI: 10.16833/j.cnki.jbmc.2025.05.001
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