# Arterial Enhancement Fraction-Spectral CT-Based Model as Part of Prediction Model in BRAFV600E-Positive Papillary Thyroid Carcinoma

## TL;DR

This study shows that a model using CT scan data and a thyroid condition can accurately predict a common cancer mutation in thyroid tumors before surgery.

## Contribution

A novel non-invasive prediction model combining arterial enhancement fraction, spectral CT, and Hashimoto’s thyroiditis for BRAFV600E mutation detection in PTC.

## Key findings

- The AEF + DLCT + HT model achieved an AUC of 0.896 in training and 0.853 in validation for predicting BRAFV600E mutations.
- The model demonstrated good calibration and net benefit across all probability thresholds in decision curve analysis.
- Hashimoto’s thyroiditis combined with CT parameters improved prediction accuracy for the BRAFV600E mutation.

## Abstract

Objectives: The BRAFV600E is the most common oncogene in thyroid cancer and is associated with the aggressiveness of papillary thyroid carcinoma (PTC). The aim of this study was to investigate the effectiveness of the arterial enhancement fraction (AEF) and dual-layer detector spectral computed tomography (DLCT) parameters for predicting the BRAFV600E mutation in PTC. Methods: A total of 237 patients with PTC who underwent DLCT and BRAFV600E mutation detection (mutant group: n = 187; wild group: n = 50) were retrospectively reviewed. The receiver operating characteristic curves evaluated the effectiveness of the prediction models based on the significantly different variables using logistic regression analysis. The nomogram of the prediction model with the highest AUC in the validation cohort was constructed. Results: The AUCs of the DLCT+ Hashimoto’s thyroiditis (HT) and AEF + DLCT + HT prediction models were 0.901 and 0.896, respectively, in the training cohort and 0.801 and 0.853 in the validation cohort. The calibration curve revealed the good agreement between the prediction results and the actual observations using the AEF + DLCT + HT model. The DCA demonstrated that the model can provide net benefit for all threshold probabilities. Conclusions: As an effective and visually noninvasive prediction tool, the AEF + DLCT + HT-based nomogram presented satisfactory effectiveness in preoperatively predicting the BRAFV600E mutation in PTC.