Evidence map›Paper›PMID 40395668›Full record

ArticleiScience2025

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

Yiwen Yu, Chengqian Zhao, Ruohan Guo, Yafang Zhang, Xiaoxian Li, Naxiang Liu, Yun Lu, Xu Han, Xiaofeng Tang, Rushuang Mao and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. EnhancingQuantitative imaging in medicine and surgery · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Yiwen YuDepartment of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Chengqian ZhaoSchool of Information Science and Technology, Fudan University, Shanghai, China.
Ruohan GuoDepartment of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Yafang ZhangDepartment of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Xiaoxian LiDepartment of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Naxiang LiuDepartment of Ultrasonography, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian Branch of Fudan University Shanghai Cancer Center, Fuzhou, China.
Yun LuDepartment of Functional, Sun Yat-sen University Cancer Center Gansu Hospital, Gansu Provincial Cancer Hospital, Lanzhou, China.
Xu HanDepartment of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Xiaofeng TangDepartment of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Rushuang MaoDepartment of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Chuan PengDepartment of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Jinhua YuSchool of Information Science and Technology, Fudan University, Shanghai, China.
Jianhua ZhouDepartment of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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;

Indexed as

cancercomputer modelingin silico biology

Identifiers

PMID40395668
PMCPMC12090294

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.