# BRAF Detection in FNAC Combined with Semi-Quantitative 99mTc-MIBI Technique and AI Model, an Economic and Efficient Predicting Tool for Malignancy in Thyroid Nodules

## TL;DR

This study combines BRAF detection, 99mTc-MIBI imaging, and AI to create a cost-effective tool for predicting thyroid nodule malignancy.

## Contribution

A novel, economic diagnostic approach combining BRAF, 99mTc-MIBI, and AI for thyroid nodule malignancy prediction.

## Key findings

- BRAF in FNAC showed 87.5% sensitivity and 75% specificity for malignancy.
- 99mTc-MIBI wash-out index had 57.14% sensitivity and 50% specificity.
- The AI model aims to integrate these methods for improved diagnostic accuracy.

## Abstract

Accurate preoperative diagnosis of the nature of thyroid nodules remains a challenge and generates a constant demand for improving the diagnostic tools. The aim of the study was to create a more efficient and yet economic diagnostic tool by combining multiple diagnostic techniques prone to daily use. In our research, we combined ultrasound findings, BRAF mutations in fine needle aspiration cytology (FNAC) and functional imaging with a semi-quantitative technique: 99mTc-methoxy-isobutyl-isonitrile ([99mTc]MIBI) wash-out. Preoperative findings were correlated with a histological report from thyroidectomy, which wasable to assess a diagnostic pattern with the help of an artificial intelligence (AI) model for our small cohort.

Background: Technology allows us to predict a histopathological diagnosis, but the high costs prevent the large-scale use of these possibilities. The current liberal indication for surgery in benign thyroid conditions led to a rising frequency of incidental thyroid carcinoma, especially low-risk papillary micro-carcinomas. Methods: We selected a cohort of 148 patients with thyroid nodules by ultrasound characteristics and investigated them by fine needle aspiration cytology (FNAC)and prospective BRAF collection for 70 patients. Also, we selected 44 patients with thyroid nodules using semi-quantitative functional imaging with an oncological, 99mTc-methoxy-isobutyl-isonitrile (99mTc-MIBI) radiotracer. Results: Following a correlation with final histopathological reports in patients who underwent thyroidectomy, we introduced the results in a machine learning program (AI) in order to obtain a pattern. For semi-quantitative functional visual pattern imaging, we found a sensitivity of 33%, a specificity of 66.67%, an accuracy of 60% and a negative predicting value (NPV) of 88.6%. For the wash-out index (WOind), we found a sensitivity of 57.14%, a specificity of 50%, an accuracy of 70% and an NPV of 90.06%.The results of BRAF in FNAC included 87.50% sensitivity, 75.00% specificity, 83.33% accuracy, 75.00% NPV and 87.50% PPV. The prevalence of malignancy in our small cohort was 11.4%. Conclusions: We intend to continue combining preoperative investigations such as molecular detection in FNAC, 99mTc-MIBI scanning and AI training with the obtained results on a larger cohort. The combination of these investigations may generate an efficient and cost-effective diagnostic tool, but confirmation of the results on a larger scale is necessary.