Deep learning model based on ultrasound images predicts BRAF V600E mutation in papillary thyroid carcinoma

BRAF V600E mutation status detection facilitates prognosis prediction in papillary thyroid carcinoma (PTC). We developed a deep-learning model to determine the BRAF V600E status in PTC. PTC from three centers were collected as the training set (1341 patients), validation set (148 patients), and external test set (135 patients). After testing the performance of the ResNeSt-50, Vision Transformer, and Swin Transformer V2 (SwinT) models, SwinT was chosen as the optimal backbone. An integrated BrafSwinT model was developed by combining the backbone with a radiomics feature branch and a clinical parameter branch. BrafSwinT demonstrated an AUC of 0.869 in the external test set, outperforming the original SwinT, Vision Transformer, and ResNeSt-50 models (AUC: 0.782-0.824; <i>p</i> value: 0.017-0.041). BrafSwinT showed promising results in determining BRAF V600E mutation status in PTC based on routinely acquired ultrasound images and basic clinical information, thus facilitating risk stratification.

Authors

Institutions

Publication Details

Journal
iScience
Published
2025-04-18
DOI
https://doi.org/10.1016/j.isci.2025.112482
Citations
1
Primary Topic
Thyroid Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
1.29

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Deep learning model based on ultrasound images predicts BRAF V600E mutation in papillary thyroid carcinoma

Rushuang Mao, Naxiang Liu, Jinhua Yu, Chuan Peng et al.
1 citations
iScience
Thyroid Cancer Diagnosis and Treatment
1.29
article

Deep learning model based on ultrasound images predicts BRAF V600E mutation in papillary thyroid carcinoma

Rushuang Mao, Naxiang Liu, Jinhua Yu, Chuan Peng, Jianhua Zhou, Xiaofeng Tang, Ruohan Guo, Chengqian Zhao, Xu Han, Yiwen Yu, Xiaoxian Li, Yun Lu, Yafang Zhang
article en
1 citations

Abstract

BRAF V600E mutation status detection facilitates prognosis prediction in papillary thyroid carcinoma (PTC). We developed a deep-learning model to determine the BRAF V600E status in PTC. PTC from three centers were collected as the training set (1341 patients), validation set (148 patients), and external test set (135 patients). After testing the performance of the ResNeSt-50, Vision Transformer, and Swin Transformer V2 (SwinT) models, SwinT was chosen as the optimal backbone. An integrated BrafSwinT model was developed by combining the backbone with a radiomics feature branch and a clinical parameter branch. BrafSwinT demonstrated an AUC of 0.869 in the external test set, outperforming the original SwinT, Vision Transformer, and ResNeSt-50 models (AUC: 0.782-0.824; <i>p</i> value: 0.017-0.041). BrafSwinT showed promising results in determining BRAF V600E mutation status in PTC based on routinely acquired ultrasound images and basic clinical information, thus facilitating risk stratification.

iScienceVol. 28(5)
Fujian Medical University (CN), Sun Yat-sen University (CN), Fudan University (CN), Gansu Provincial Hospital (CN), Sun Yat-sen University Cancer Center (CN), Fujian Provincial Cancer Hospital (CN), State Key Laboratory of Oncology in South China
National Natural Science Foundation of China
Openalex Percentile: Top 20%
Thyroid Cancer Diagnosis and Treatment
1.29
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.