ArticleCancers2021
Deep Learning for the Preoperative Diagnosis of Metastatic Cervical Lymph Nodes on Contrast-Enhanced Computed ToMography in Patients with Oral Squamous Cell Carcinoma.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 5 of them syntheses 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.
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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.
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Who cites it
19 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Application of artificial intelligence and radiomics in the prediction of lymph node metastasis and tumour grading of oral cancer - a systematic review and meta analysis.BMC oral health · 2026Pooled it
- Diagnostic accuracy of radiomics and artificial intelligence models in diagnosing lymph node metastasis in head and neck cancers: a systematic review and meta-analysis.Neuroradiology · 2025Pooled it
- Deep learning in oral cancer- a systematic review.BMC oral health · 2024Pooled it
- Application of CT and MRI images based on artificial intelligence to predict lymph node metastases in patients with oral squamous cell carcinoma: a subgroup meta-analysis.Frontiers in oncology · 2024Pooled it
- Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review.Frontiers in oral health · 2021Pooled it
- Automated FDG uptake/PET-CT fused scan diagnosis of various lymph node tumors using object detection AI techniques.Scientific reports · 2026Article
- Glutamine-mediated crosstalk between M2 macrophages and tumor cells via the SLC38A5/FOXM1/CNIH4 axis promotes oral squamous cell carcinoma progression.Journal of translational medicine · 2026Article
- Advancements in Image-Based Artificial Intelligence in the Diagnosis and Treatment of Head and Neck Squamous Cell Carcinoma: A Narrative Review.International journal of general medicine · 2026Review
- Article
- Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications.Diagnostics (Basel, Switzerland) · 2025Review
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma.Cancers · 2024Article
- Can deep learning replace histopathological examinations in the differential diagnosis of cervical lymphadenopathy?European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Article
- A deep learning and radiomics fusion model based on contrast-enhanced computer tomography improves preoperative identification of cervical lymph node metastasis of oral squamous cell carcinoma.Clinical oral investigations · 2023Article
- Initial experience of a deep learning application for the differentiation of Kikuchi-Fujimoto's disease from tuberculous lymphadenitis on neck CECT.Scientific reports · 2022Article
- Nuclear morphology is a deep learning biomarker of cellular senescence.Nature aging · 2022Article
- Radiomics analysis for differentiating of cervical lymphadenopathy between cancer of unknown primary and malignant lymphoma on unenhanced computed tomography.Nagoya journal of medical science · 2022Article
- Segmentation of metastatic cervical lymph nodes from CT images of oral cancers using deep-learning technology.Dento maxillo facial radiology · 2022Article
- Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine.Frontiers in oral health · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
We investigated the value of deep learning (DL) in differentiating between benign and metastatic cervical lymph nodes (LNs) using pretreatment contrast-enhanced computed tomography (CT). This retrospective study analyzed 86 metastatic and 234 benign (non-metastatic) cervical LNs at levels I-V in 39 patients with oral squamous cell carcinoma (OSCC) who underwent preoperative CT and neck dissection. LNs were randomly divided into training (70%), validation (10%), and test (20%) sets. For the validation and test sets, cervical LNs at levels I-II were evaluated. Convolutional neural network analysis was performed using Xception architecture. Two radiologists evaluated the possibility of metastasis to cervical LNs using a 4-point scale. The area under the curve of the DL model and the radiologists' assessments were calculated and compared at levels I-II, I, and II. In the test set, the area under the curves at levels I-II (0.898) and II (0.967) were significantly higher than those of each reader (both,
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