Authors
D D Lysukhin, A V Varlamov, A A Matrosova, N V Pachuashvili, A Yu Abrosimov, V E Vanushko, T A Demura, L S Urusova
Published in
Arkhiv patologii. Volume 88. Issue 4. Pages 41-45.
Abstract
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.
PMID:
42531241
Bibliographic data and abstract were imported from PubMed on 31 Jul 2026.
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