SynthesisBMC oral health2026
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.
Synthesis in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled 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
- 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
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRadiomics investigation strategies can be applied to head and neck tumours, including lesion segmentation, tumour grading and staging prediction. Texture features from PET/CT radiomics, particularly those reflecting metabolic heterogeneity within the primary tumour, have shown substantial predictive value for lymph node metastasis in oral cancer. Accurate prediction of cervical lymph node metastasis in oral cancer is crucial, as it is the most significant prognostic factor influencing treatment planning and patient survival.
methodAn extensive search across PubMed, Scopus, and Wiley Online Library, adhering to PRISMA guidelines, was carried out. The present review included 40 studies, of which 33 were included in the meta-analysis of the prediction of lymph node metastasis and tumour grading.
resultsThe pooled sensitivity, specificity and Diagnostic Odds Ratio (DOR) of the AI models for the prediction of LN metastases were 0.86 (95% CI 0.80-0.90), 0.91 (95% CI 0.87-0.93), and 56.58 (95% CI 21.68-91.48), respectively. The pooled sensitivity, specificity and DOR of the AI models for the grading of OSCC were 0.88 (95% CI 0.54-0.98), 0.82 (95% CI 0.76-0.87), and 34.38 (95% CI 24.24-103), respectively.
conclusionTo mitigate the elevated misinterpretation rate of lymph node metastasis (LNMs), it is prudent to incorporate ML/DL into the imaging identification of LNMs in oral cancer. Radiomic CT characteristics of oral cancer indicate tumour heterogeneity and can forecast histopathologic attributes. These exploratory investigations suggest that the AI and radiomics prediction framework may function as an additional non-invasive diagnostic tool for oral cancer, enhancing the objectivity and accuracy of tumour staging and grading and providing guidance for future therapies.
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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.