Diagnostic value of FNAC combined with BRAFV600E mutation detection in Hashimoto's thyroiditis complicated with papillary thyroid carcinoma.

Background: This study aimed to analyze the effect of preoperative fine needle aspiration cytology (FNAC) combined with BRAFV600E mutation detection as compared to that of fine needle aspiration cytology alone on the diagnostic performance of papillary thyroid carcinoma (PTC) combined with Hashimoto's thyroiditis (HT). Method: Patients with thyroid nodules in Hashimoto's thyroiditis, who underwent fine-needle aspiration cytology examination and BRAFV600E mutation detection in the puncture eluate at the outpatient clinic, were selected. Finally, 122 patients received surgical treatment and were included in the study. We used postoperative pathological results as the gold standard. Accordingly, we compared the sensitivity, specificity and accuracy of preoperative FNAC alone and FNAC combined with BRAFV600E mutation detection in for the diagnosis of PTC combined with HT. Results: For PTC patients with HT, the sensitivity of FNAC diagnosis was 93.69%, the specificity was 90.90% and the accuracy was 93.44%. However, the sensitivity, specificity and accuracy of FNAC combined with BRAFV600E mutation detection were 97.30%, 90.90% and 96.72%, respectively. Therefore, combined detection can improve the sensitivity and accuracy of diagnosis (p<0.05). Conclusion: FNAC combined with eluent BRAFV600E mutation detection can improve the sensitivity and accuracy of diagnosis of PTC in the background of HT.

Preoperative prognostic risk stratification model for papillary thyroid carcinoma based on clinical and ultrasound characteristics.

Background: The preoperative risk stratification for patients with papillary thyroid carcinoma (PTC) plays a crucial role in guiding individualized treatment. We aim to construct a predictive model that aids in distinguishing between patients with low-risk and high-risk PTC based on preoperative clinical and ultrasound characteristics. Materials and methods: Patients who underwent open surgery and were diagnosed with PTC via a postoperative pathological report between January 2020 and December 2020 were retrospectively reviewed. Data including basic information, preoperative ultrasound characteristics, thyroid function, and postoperative pathology characteristics were obtained. Univariate logistic regression analysis and least absolute shrinkage and selection operator regression analysis were performed to screen candidate variables. Finally, the preoperative predictive model for PTC was established based on the results of the multivariate logistic regression analysis. Results: A total of 1,875 patients with PTC were enrolled. Eight variables (sex, age, number of foci, maximum tumor diameter on ultrasound, calcification, capsule, lymph node status on ultrasound, and thyroid peroxidase (TPO) antibody level) significantly associated with risk stratification were included in the predictive model. A nomogram was constructed for clinical utility. The model showed good discrimination, and the area under the curve was 0.777 [95% confidence interval (CI): 0.752-0.803] and 0.769 (95% CI: 0.729-0.809) in the training set and validation set, respectively. The calibration curve exhibited a rather good consistency with the perfect prediction. Furthermore, decision curve analysis and clinical impact curve showed that the model had good efficacy in predicting the prognostic risk of PTC. Conclusions: The nomogram model based on preoperative indicators for predicting the prognostic stratification of PTC showed a good predictive value. This could aid surgeons in deciding on individualized precision treatments.