ArticleEndocrine2025
Deep learning based analysis of dynamic video ultrasonography for predicting cervical lymph node metastasis in papillary thyroid carcinoma.
Article in Endocrine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07391514 (Deep Learning-Based Intraoperative Dual-tracer Video Analysis of Sentinel Lymph Node Mapping for Metastasis Prediction in cN0 Papillary Thyroid Carcinoma), which is not on this map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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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.
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.
Deep Learning-Based Intraoperative Dual-tracer Video Analysis of Sentinel Lymph Node Mapping for Metastasis Prediction in cN0 Papillary Thyroid Carcinoma: A Prospective Cohort Study
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ultrasound-based artificial intelligence for predicting cervical lymph node metastasis in papillary thyroid cancer: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Article
- Predicting lateral cervical lymph node involvement in papillary thyroid carcinoma patients: development of a nomogram model based on contrast-enhanced ultrasound images.Gland surgery · 2026Article
- Dual-Tracer Imaging and Deep Learning for Real-Time Prediction of Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma.Cancers · 2026Article
- Predictive value of real-time shear wave elastography combined with serum thyroglobulin in thyroid cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Hyper-Thyro Vision: An Integrated Framework for Hyperthyroidism Diagnostic Facial Image Analysis Based on Deep Learning.Biomimetics (Basel, Switzerland) · 2026Article
- Malignancy prediction for calcified thyroid nodules using deep learning based on ultrasound dynamic videos.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Development and validation of deep learning models for qualitative classification of benign and malignant enlarged cervical lymph nodes based on ultrasound images.Gland surgery · 2025Article
- A novel deep learning model based on multimodal contrast-enhanced ultrasound dynamic video for predicting occult lymph node metastasis in papillary thyroid carcinoma.Frontiers in endocrinology · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCervical lymph node metastasis (CLNM) is the most common form of thyroid cancer metastasis. Accurate preoperative CLNM diagnosis is of more importance in patients with papillary thyroid cancer (PTC). However, there is currently no unified methods to objectively predict CLNM risk from ultrasonography in PTC patients.This study aimed to develop a deep learning (DL) model to help clinicians more accurately determine the existence of CLNM risk in patients with PTC and then assist them with treatment decisions.
methodsUltrasound dynamic videos of 388 patients with 717 thyroid nodules were retrospectively collected from Zhejiang Cancer Hospital between January 2020 and June 2022. Five deep learning (DL) models were investigated to examine its efficacy for predicting CLNM risks and their performances were also compared with those predicted using two-dimensional ultrasound static images.
resultsIn the testing dataset (n = 78), the DenseNet121 model trained on ultrasound dynamic videos outperformed the other four DL models as well as the DL model trained using the two-dimensional (2D) static images across all metrics. Specifically, using DenseNet121, the comparison between the 3D model and 2D model for all metrics are shown as below: AUROC: 0.903 versus 0.828, sensitivity: 0.877 versus 0.871, specificity: 0.865 versus 0.659.
conclusionsThis study demonstrated that the DenseNet121 model has the greatest potential in distinguishing CLNM from non-CLNM in patients with PTC. Dynamic videos also offered more information about the disease states which have proven to be more efficient and robust in identifying CLNM compared to statis images.
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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.