BRAF V600E as an accurate marker to complement fine needle aspiration (FNA) cytology in the guidance of thyroid surgery in the Chinese population: evidence from over 1000 consecutive FNAs with follow-up

The Combined Use of Fine-Needle Aspiration (FNA) and BRAF V600E Gene Testing: Can it Increase the Definitive Diagnosis Rate of Nodules Categorized as Bethesda III for Papillary Thyroid Carcinoma?

OBJECTIVE

This study aimed to analyze the malignant probability of thyroid nodules diagnosed as indeterminate cytology, including atypia of undetermined significance or follicular lesion of undetermined significance (AUS/FLUS), and investigate the diagnostic value of combining BRAF V600E gene testing within this classification.

METHODS

We conducted a retrospective analysis of 126 patients who underwent fine-needle aspiration (FNA) examination of thyroid nodules and subsequent surgical treatment at Beijing Haidian Hospital between October 2021 and November 2022. Among them, there were 22 male and 104 female patients, aged between 18 and 75 years old. Surgical pathology results were considered the gold standard for diagnosing the nature of thyroid nodules, evaluating the malignant incidence of cytological results categorized as AUS/FLUS. Fisher's exact test and diagnostic test evaluation methods were used to analyze the discriminatory diagnostic efficacy of preoperative FNA combined with BRAF V600E gene testing for papillary thyroid carcinoma (PTC). Statistical analysis was performed using SPSS 22.0 software.

RESULTS

In PTC patients, the BRAF V600E gene mutation rate was 87.93% (102/116). Within the category of FNA results as AUS/FLUS, the proportion of PTC was 60.00% (15/25). The specificity, sensitivity, positive predictive value, and negative predictive value of the BRAF V600E gene mutation in diagnosing PTC within the AUS/FLUS category were 10/10, 6/15, 6/6, and 10/19, respectively. The BRAF V600E gene mutation significantly increased the detection rate of PTC in patients classified under this cytology (P = 0.028, <0.05).

CONCLUSION

Preoperative FNA combined with BRAF V600E gene mutation testing significantly enhances the malignant detection rate of thyroid nodules diagnosed cytologically as AUS/FLUS. This combined approach provides a potent tool to improve diagnostic accuracy in this indeterminate classification.

BRAF V600E mutation in thyroid carcinoma: a large-scale study in Han Chinese population

BACKGROUND

The prevalence of genetic mutations in thyroid cancer varies significantly among different ethnic backgrounds. The present study aimed to investigate the clinical potential of BRAF V600E in a large group of homogenous Han Chinese patients.

METHODS

From 2018 to 2021, 6232 thyroid disease patients who underwent thyroidectomy at our hospital were enrolled. We measured the diagnostic value of BRAF and plotted ROC curves. Patients with full clinical-pathological data were selected and divided into the BRAF mutation and wild type groups. We conducted univariate and multivariate analyses to quantify the differences in potential predictive factors of papillary thyroid carcinoma (PTC) patients between the groups. Kaplan-Meier survival analysis was used to estimate overall recurrence and recurrence rate.

RESULTS

The prevalence of BRAF V600E mutation was 86.0% in PTCs. The sensitivity and specificity of BRAF mutation for diagnosing PTC from suspicious lesions were 85.5% and 100%, respectively. The sensitivity and specificity of BRAF analysis in the indeterminate cytology group were 72.5% and 100%, respectively. BRAF mutation showed an independent association with older age, negative HT, larger tumor size, extrathyroidal extension, and multifocality in PTCs. In micro-PTCs (tumor size ≤ 1), the mutation was also positively correlated with progressive phenotypes of extrathyroidal extension and multifocality. BRAF mutation was associated with poorer recurrence-free probability in Kaplan-Meier survival analysis.

CONCLUSIONS

This large single-center study reveals that BRAF V600E is highly prevalent in the Han Chinese population and demonstrates BRAF V600E mutation testing has high diagnostic accuracy and its strong association with the progress of aggressiveness in PTCs and a higher probability of recurrence. BRAF mutation can serve as an accurate marker for diagnosis and decision-making with great value.

Comparative Cyto-Histological Genetic Profile in a Series of Differentiated Thyroid Carcinomas

INTRODUCTION

Molecular tests can contribute to improve the preoperative diagnosis of thyroid nodules. Tests available are expensive and not adapted to different populations.

AIM

This study aimed to compare the cyto-histological genetic profile and to evaluate the reliability of molecular tests using ultrasound-guided fine needle aspiration cytology (US-FNAC) in accurately diagnosing differentiated thyroid carcinomas (DTCs) and predicting biologic behavior of papillary thyroid carcinomas (PTCs).

MATERIALS AND METHODS

The series included 259 patients with paired cyto-histological samples totaling 518 samples. The genetic alterations were analyzed via PCR/Sanger sequencing. The association with clinicopathologic features was evaluated in PTCs.

RESULTS/DISCUSSION

From the 259 patients included, histologies were 50 (19.3%) benign controls and 209 (80.7%) DTC cases, from which 182 were PTCs; cytologies were 5.8% non-diagnostic, 18.2% benign, 39% indeterminate, and 37.1% malignant. In histology, indeterminate nodules (n = 101) were 22.8% benign and 77.2% malignant. Mutation frequencies in cytology and histology specimens were, respectively, TERTp: 3.7% vs. 7.9%; BRAF: 19.5% vs. 25.1%; and RAS: 11% vs. 17.5%. The overall cyto-histological agreement of the genetic mutations was 94.9%, with Cohen's k = 0.67, and in indeterminate nodules agreement was 95.7%, k = 0.64. The identified mutations exhibited a discriminative ability in diagnosing DTC with a specificity of 100% for TERTp and BRAF, and of 94% for RAS, albeit with low sensitivity. TERTp and BRAF mutations were associated with aggressive clinicopathological features and tumor progression in PTCs (p < 0.001). The obtained good cyto-histological agreement suggests that molecular analysis via US-FNAC may anticipate the genetic profile and the behavior of thyroid tumors, confirming malignancy and contributing to referring patients to surgery.

Machine learning for identifying benign and malignant of thyroid tumors: A retrospective study of 2,423 patients

Thyroid tumors, one of the common tumors in the endocrine system, while the discrimination between benign and malignant thyroid tumors remains insufficient. The aim of this study is to construct a diagnostic model of benign and malignant thyroid tumors, in order to provide an emerging auxiliary diagnostic method for patients with thyroid tumors. The patients were selected from the Chongqing General Hospital (Chongqing, China) from July 2020 to September 2021. And peripheral blood, BRAFV600E gene, and demographic indicators were selected, including sex, age, BRAFV600E gene, lymphocyte count (Lymph#), neutrophil count (Neu#), neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), red blood cell distribution width (RDW), platelets count (PLT), red blood cell distribution width-coefficient of variation (RDW-CV), alkaline phosphatase (ALP), and parathyroid hormone (PTH). First, feature selection was executed by univariate analysis combined with least absolute shrinkage and selection operator (LASSO) analysis. Afterward, we used machine learning algorithms to establish three types of models. The first model contains all predictors, the second model contains indicators after feature selection, and the third model contains patient peripheral blood indicators. The four machine learning algorithms include extreme gradient boosting (XGBoost), random forest (RF), light gradient boosting machine (LightGBM), and adaptive boosting (AdaBoost) which were used to build predictive models. A grid search algorithm was used to find the optimal parameters of the machine learning algorithms. A series of indicators, such as the area under the curve (AUC), were intended to determine the model performance. A total of 2,042 patients met the criteria and were enrolled in this study, and 12 variables were included. Sex, age, Lymph#, PLR, RDW, and BRAFV600E were identified as statistically significant indicators by univariate and LASSO analysis. Among the model we constructed, RF, XGBoost, LightGBM and AdaBoost with the AUC of 0.874 (95% CI, 0.841-0.906), 0.868 (95% CI, 0.834-0.901), 0.861 (95% CI, 0.826-0.895), and 0.837 (95% CI, 0.802-0.873) in the first model. With the AUC of 0.853 (95% CI, 0.818-0.888), 0.853 (95% CI, 0.818-0.889), 0.837 (95% CI, 0.800-0.873), and 0.832 (95% CI, 0.797-0.867) in the second model. With the AUC of 0.698 (95% CI, 0.651-0.745), 0.688 (95% CI, 0.639-0.736), 0.693 (95% CI, 0.645-0.741), and 0.666 (95% CI, 0.618-0.714) in the third model. Compared with the existing models, our study proposes a model incorporating novel biomarkers which could be a powerful and promising tool for predicting benign and malignant thyroid tumors.