Arkhiv patologii · 2026
[Assessing BRAF status of localized and locally advanced papillary thyroid microcarcinomas using machine learning methods].
Аннотация на английскомСтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Lysukhin D D, Varlamov A V, Matrosova A A, Pachuashvili N V, Abrosimov A Yu, Vanushko V E, Demura T A, Urusova L S
Аннотация
UNLABELLED: 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.
OBJECTIVE: 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.
MATERIAL AND METHODS: 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).
RESULTS: 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.
CONCLUSION: The MIL model shows promise for predicting BRAF status in PTMCs from histopathological images, particularly when using Virchow-derived features.
OBJECTIVE: 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.
MATERIAL AND METHODS: 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).
RESULTS: 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.
CONCLUSION: The MIL model shows promise for predicting BRAF status in PTMCs from histopathological images, particularly when using Virchow-derived features.