ReviewCancers2022
Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma-A Systematic Review.
Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis.World journal of surgical oncology · 2025Pooled it
- Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews.Advances in therapy · 2023Pooled it
- Application of artificial intelligence in head and neck squamous cell carcinoma.Annals of medicine · 2026Review
- Article
- A Pilot Study of the Diagnosis of Oral Cancer Through the Development of an AI Application.Dentistry journal · 2026Article
- Review
- [Prevention in oncological follow-up].HNO · 2026Review
- Application of Digital Medicine to the Diagnosis and Treatment of Head and Neck Tumors.Cancer reports (Hoboken, N.J.) · 2026Review
- Artificial Intelligence Assurance in Head and Neck Surgery: Now and Next.Proceedings. IEEE International Symposium on Computer-Based Medical Systems · 2025Article
- Diagnostic Performance of Magnetic Resonance Sequences in Staging Lymph Node Involvement and Extranodal Extension in Head and Neck Squamous Cell Carcinoma.Diagnostics (Basel, Switzerland) · 2025Article
- Novel Preprocessing-Based Sequence for Comparative MR Cervical Lymph Node Segmentation.Journal of clinical medicine · 2025Article
- Nodal assessment and extranodal extension in head and neck squamous cell cancer: insights from computed tomography and magnetic resonance imaging.La Radiologia medica · 2025Article
- Using AI in Optimizing Oral and Dental Diagnoses-A Narrative Review.Diagnostics (Basel, Switzerland) · 2024Review
- Article
- Assessing the use of the novel tool Claude 3 in comparison to ChatGPT 4.0 as an artificial intelligence tool in the diagnosis and therapy of primary head and neck cancer cases.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
- Article
- Distribution and predictors of F-18-FDG uptake values of non-malignant cervical lymph nodes in pediatric patients.EJNMMI research · 2024Article
- Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma.Cancers · 2024Article
- Improved Cervical Lymph Node Characterization among Patients with Head and Neck Squamous Cell Carcinoma Using MR Texture Analysis Compared to Traditional FDG-PET/MR Features Alone.Diagnostics (Basel, Switzerland) · 2023Article
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Locally-advanced head and neck squamous cell carcinoma (HNSCC) is mainly defined by the presence of pathologic cervical lymph nodes (LNs) with or without extracapsular spread (ECS). Current radiologic criteria to classify LNs as non-pathologic, pathologic, or pathologic with ECS are primarily shape-based. However, significantly more quantitative information is contained within imaging modalities. This quantitative information could be exploited for classification of LNs in patients with locally-advanced HNSCC by means of artificial intelligence (AI). Currently, various reviews exploring the role of AI in HNSCC are available. However, reviews specifically addressing the current role of AI to classify LN in HNSCC-patients are sparse. The present work systematically reviews original articles that specifically explore the role of AI to classify LNs in locally-advanced HNSCC applying Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and the Study Quality Assessment Tool of National Institute of Health (NIH). Between 2001 and 2022, out of 69 studies a total of 13 retrospective, mainly monocentric, studies were identified. The majority of the studies included patients with oropharyngeal and oral cavity (9 and 7 of 13 studies, respectively) HNSCC. Histopathologic findings were defined as reference in 9 of 13 studies. Machine learning was applied in 13 studies, 9 of them applying deep learning. The mean number of included patients was 75 (SD ± 72; range 10-258) and of LNs was 340 (SD ± 268; range 21-791). The mean diagnostic accuracy for the training sets was 86% (SD ± 14%; range: 43-99%) and for testing sets 86% (SD ± 5%; range 76-92%). Consequently, all of the identified studies concluded AI to be a potentially promising diagnostic support tool for LN-classification in HNSCC. However, adequately powered, prospective, and randomized control trials are urgently required to further assess AI's role in LN-classification in locally-advanced HNSCC.
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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 OpenQuestion graph.