[Assessing BRAF status of localized and locally advanced papillary thyroid microcarcinomas using machine learning...

Papillary thyroid microcarcinomas (PTMCs) account for about 30% of papillary thyroid cancer cases, and the BRAF V600E mutation is one of the driver mutations in the development of papillary thyroid carcinomas. However, traditional molecular diagnostic methods are time-consuming and costly, highlighting the need for alternative approaches, such as machine learning-based analysis of histopathological images.

To evaluate the feasibility of using a Multiple-Instance Learning (MIL) model to predict BRAF status in PTMC patients based on histopathological whole-slide images without explicit region-of-interest annotation.

The study included histopathological scans from surgical specimens of 93 PTMC patients (age range 24-72 years, median 48 years) operated between 01.01.2022 and 01.09.2023. BRAF V600E status was determined by Real-Time PCR. For classification, an MIL model (CLAM) with pretrained feature extractors (CTransPath and Virchow) was used. Model performance was assessed using ROC-AUC with 5-fold cross-validation (10 repeats).

The BRAF V600E mutation was detected in 62 patients and absent in 31. The highest ROC-AUC (0.83; 95% CI: 0.61-1.00) was achieved using Virchow features with 20% top-attention region filtering. For CTransPath, ROC-AUC was 0.79 (95% CI: 0.60-0.95) with 5% region filtering.

The MIL model shows promise for predicting BRAF status in PTMCs from histopathological images, particularly when using Virchow-derived features.