Artificial Intelligence–Driven MRI for Cervical Nodal Metastasis Detection in Oral Squamous Cell Carcinoma: A Hierarchical Meta‐Analysis of Diagnostic Accuracy

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Abstract
Background: Artificial intelligence (AI) applied to magnetic resonance imaging (MRI) may improve detection of cervical lymph node metastases in oral squamous cell carcinoma (OSCC) but is heterogeneous. Methods: A systematic review identified observational studies (from 2000) evaluating AI‐based MRI in adults with histopathologically confirmed OSCC. Risk of bias was assessed with QUADAS‐AI. Diagnostic performance was synthesized using a hierarchical bivariate model. Publication bias and certainty of evidence were assessed using Deeks' test and GRADE. Results: Twelve studies were included; seven datasets (548 participants) were meta‐analyzed. Pooled sensitivity was 0.72 (95% CI: 0.62–0.80) and specificity 0.79 (95% CI: 0.73–0.83), with AUC 0.82 and diagnostic odds ratio 9.42. Heterogeneity is mainly related to threshold effects. No significant publication bias was detected ( p = 0.536). Evidence certainty was low. Conclusions: AI‐assisted MRI shows moderate diagnostic performance. Multicenter validation is required before clinical implementation. Clinical Relevance: AI‐supported MRI may serve as an adjunctive tool to improve preoperative risk stratification of cervical lymph node metastasis in OSCC.

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