ArticleBMC cancer2026
Predicting central lymph node metastasis in papillary thyroid carcinoma using a fusion model of vision transformer and traditional radiomics based on dynamic dual-modality ultrasound.
Article in BMC cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Artificial intelligence in the diagnosis of thyroid diseases: applications and challenges.Frontiers in radiology · 2026Review
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Authors and funding
8 authors.
Funding
Abstract
objectiveThis study aimed to develop a novel fusion model based on dynamic dual-modality with B-mode ultrasound and superb microvascular imaging (SMI), combining Vision Transformer (ViT) and radiomics features to predict central lymph node metastasis (CLNM) in thyroid cancer patients.
methodIn this retrospective diagnostic study, 310 patients with pathologically confirmed papillary thyroid carcinoma from two hospitals were included. We trained ViT models for B-mode and SMI, then extracted ViT and radiomics features from their video images. Initially, Single-modality models were developed, including the B-mode radiomics model (BMUS_RAD) and the B-mode ViT model (BMUS_ViT). Subsequently, Dual-modality models were constructed, encompassing the Dual-modality radiomics model (DMU_RAD), the Dual-modality ViT model (DMU_ViT), and finally, the integrated model DMU_RAD_ViT, to enhance the prediction of CLNM. The performance of each model was compared, and SHAP was utilized for the visual interpretation of the novel fusion model.
resultsAmong all the models, the fusion model DMU_RAD_ViT performed the best (AUC = 0.901, p < 0.05). At the same time, the dual-modality model DMU_RAD(AUC = 0.856) and DMU_ViT(AUC = 0.832) is also higher than the single-modal model BMUS_RAD (AUC = 0.837) and BMUS_ViT (AUC = 0.789), respectively. SHAP analysis revealed that 16 radiomics and ViT features from both modalities contributed to the DMU_RAD_ViT model.
conclusionsThe Dual-modality fusion model, integrating both radiomics and ViT features, can be utilized to predict CLNM.
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Registered trials
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 Socratic graph.