ArticleEuropean radiology2019
Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT.
Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 3 of them syntheses that pooled it.
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Who cites it
57 citing papers in PubMed, 3 syntheses or guidelines pooled it, 124 citations in OpenAlex.
- Diagnostic performance of machine learning and deep learning algorithms for thyroid cancer metastasis: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Prediction of cervical lymph node metastasis in differentiated thyroid cancer based on radiomics models.The British journal of radiology · 2024Pooled it
- Pooled it
- Deep learning combined with radiomics for the classification of enlarged cervical lymph nodes.Journal of cancer research and clinical oncology · 2022Trial
- Extrathyroidal Extension Prediction of Papillary Thyroid Cancer With Computed Tomography Based Radiomics Nomogram: A Multicenter Study.Frontiers in endocrinology · 2022Trial
- Development and Validation of a Picture Archiving and Communication System-Integrated Artificial Intelligence for Predicting Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma.Journal of imaging informatics in medicine · 2026Article
- Automated FDG uptake/PET-CT fused scan diagnosis of various lymph node tumors using object detection AI techniques.Scientific reports · 2026Article
- Deep Learning-Based Detection of Malignant and Equivocal Cervical Lymph Nodes on CT Imaging Using a 2D U-Net++ Model.Maedica · 2026Article
- Application of Digital Medicine to the Diagnosis and Treatment of Head and Neck Tumors.Cancer reports (Hoboken, N.J.) · 2026Review
- CT-based multiple instance and ensemble learning for lymph node metastasis prediction in esophageal squamous cell carcinoma: a multicentre, retrospective study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Artificial intelligence in the diagnosis of thyroid diseases: applications and challenges.Frontiers in radiology · 2026Review
- Non-contrast computed tomography radiomics model to predict benign and malignant thyroid nodules with lobe segmentation: A dual-center study.World journal of radiology · 2025Article
- Prediction of lymph node metastasis in papillary thyroid carcinoma using non-contrast CT-based radiomics and deep learning with thyroid lobe segmentation: A dual-center study.European journal of radiology open · 2025Article
- Nomograms for predicting cervical central lymph node metastases and high-volume cervical central lymph node metastases in papillary thyroid carcinoma.Gland surgery · 2025Article
- Epidemiological study of thyroid cancer at global, regional, and national levels from 1990 to 2021: an analysis derived from the Global Burden of Disease Study 2021.Frontiers in endocrinology · 2025Article
- Automated Segmentation of Lymph Nodes on Neck CT Scans Using Deep Learning.Journal of imaging informatics in medicine · 2024Article
- Deep learning-based automatic pipeline system for predicting lateral cervical lymph node metastasis in patients with papillary thyroid carcinoma using computed tomography: A multi-center study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2024Article
- A Transfer Learning-Based Framework for Classifying Lymph Node Metastasis in Prostate Cancer Patients.Biomedicines · 2024Article
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- A nomogram for risk stratification of central cervical lymph node metastasis in patients with papillary thyroid carcinoma.Quantitative imaging in medicine and surgery · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo develop a deep learning-based computer-aided diagnosis (CAD) system for use in the CT diagnosis of cervical lymph node metastasis (LNM) in patients with thyroid cancer.
methodsA total of 995 axial CT images that included benign (n = 647) and malignant (n = 348) lymph nodes were collected from 202 patients with thyroid cancer who underwent CT for surgical planning between July 2017 and January 2018. The datasets were randomly split into training (79.0%), validation (10.5%), and test (10.5%) datasets. Eight deep convolutional neural network (CNN) models were used to classify the images into metastatic or benign lymph nodes. Pretrained networks were used on the ImageNet and the best-performing algorithm was selected. Class-specific discriminative regions were visualized with attention heatmap using a global average pooling method.
resultsThe area under the ROC curve (AUROC) for the tested algorithms ranged from 0.909 to 0.953. The sensitivity, specificity, and accuracy of the best-performing algorithm were all 90.4%, respectively. Attention heatmap highlighted important subregions for further clinical review.
conclusionA deep learning-based CAD system could accurately classify cervical LNM in patients with thyroid cancer on preoperative CT with an AUROC of 0.953. Whether this approach has clinical utility will require evaluation in a clinical setting. KEY POINTS: • A deep learning-based CAD system could accurately classify cervical lymph node metastasis. The AUROC for the eight tested algorithms ranged from 0.909 to 0.953. • Of the eight models, the ResNet50 algorithm was the best-performing model for the validation dataset with 0.953 AUROC. The sensitivity, specificity, and accuracy of the ResNet50 model were all 90.4%, respectively, in the test dataset. • Based on its high accuracy of 90.4%, we consider that this model may be useful in a clinical setting to detect LNM on preoperative CT in patients with thyroid cancer.
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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.