SynthesisNeuroradiology2025
Diagnostic accuracy of radiomics and artificial intelligence models in diagnosing lymph node metastasis in head and neck cancers: a systematic review and meta-analysis.
Synthesis in Neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 6 of them syntheses that pooled it.
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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
20 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Machine learning based prediction of recurrence in oral tongue cancer: a systematic review with quantitative synthesis.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 · 2026Pooled it
- MRI-Based Radiomics and Artificial Intelligence for Prediction of Recurrence and Prognostic Outcomes in Oral Tongue Squamous Cell Carcinoma: A Systematic Review with Functional Meta-Synthesis.Medical sciences (Basel, Switzerland) · 2026Pooled 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
- Emerging applications of artificial intelligence for risk stratification in head and neck cancer: a scoping review.Frontiers in oncology · 2026Pooled it
- Application of Deep Learning for Predicting Hematoma Expansion in Intracerebral Hemorrhage Using Computed Tomography Scans: A Systematic Review and Meta-Analysis of Diagnostic Accuracy.La Radiologia medica · 2025Pooled it
- Systematic review of artificial intelligence and radiomics for preoperative prediction of extranodal extension and lymph node metastasis in oropharyngeal cancer.Frontiers in oncology · 2025Pooled it
- Deep learning-based computed tomography detection of early lymph node metastasis in head and neck cancer.Quantitative imaging in medicine and surgery · 2026Article
- Article
- Artificial Intelligence-Based 18F-FDG PET/CT Radiomics for Mediastinal Lymph Node Staging in Non-Small Cell Lung Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Multiparametric MRI Assessment of Cervical Lymphadenopathy: Combined Diagnostic Performance of Morphological Features and Apparent Diffusion Coefficient.Healthcare (Basel, Switzerland) · 2026Article
- Review
- Artificial Intelligence for Preoperative Prediction of Lymph Node Metastasis and Depth of Invasion in Oral Tongue Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Level II (IIA/IIB) Lymph Node Evaluation in Head and Neck Cancer: A Retrospective Cohort Study from a Non-Endemic Region.Journal of clinical medicine · 2026Article
- Can we now save the neck of OSCC T1-T2 patients? A narrative review of whether experimental techniques are on the way to clinical application.Exploration of targeted anti-tumor therapy · 2026Review
- Article
- Enhancing lymphoma cancer detection using deep transfer learning on histopathological images.Scientific reports · 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
- Review
- Artificial Intelligence in the Diagnosis of Tongue Cancer: A Systematic Review with Meta-Analysis.Biomedicines · 2025Review
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionHead and neck cancers are the seventh most common globally, with lymph node metastasis (LNM) being a critical prognostic factor, significantly reducing survival rates. Traditional imaging methods have limitations in accurately diagnosing LNM. This meta-analysis aims to estimate the diagnostic accuracy of Artificial Intelligence (AI) models in detecting LNM in head and neck cancers.
methodsA systematic search was performed on four databases, looking for studies reporting the diagnostic accuracy of AI models in detecting LNM in head and neck cancers. Methodological quality was assessed using the METRICS tool and meta-analysis was performed using bivariate model in R environment.
results23 articles met the inclusion criteria. Due to the absence of external validation in most studies, all analyses were confined to internal validation sets. The meta-analysis revealed a pooled AUC of 91% for CT-based radiomics, 84% for MRI-based radiomics, and 92% for PET/CT-based radiomics. Sensitivity and specificity were highest for PET/CT-based models. The pooled AUC was 92% for deep learning models and 91% for hand-crafted radiomics models. Models based on lymph node features had a pooled AUC of 92%, while those based on primary tumor features had an AUC of 89%. No significant differences were found between deep learning and hand-crafted radiomics models or between lymph node and primary tumor feature-based models.
conclusionRadiomics and deep learning models exhibit promising accuracy in diagnosing LNM in head and neck cancers, particularly with PET/CT. Future research should prioritize multicenter studies with external validation to confirm these results and enhance clinical applicability.
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