Machine Learning to Predict Extranodal Extension in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta‐Analysis

Objective To evaluate the clinical utility of machine learning algorithms (MLAs) in diagnosing extra‐nodal extension (ENE) using CT imaging in HNSCC. Data Sources A comprehensive literature search was conducted on MEDLINE (Ovid), EMBASE, Cochrane, Scopus, and Web of Science, from January 1, 2000, to February 12, 2025. Review Methods Two independent reviewers selected studies reporting the diagnostic accuracy of MLAs in detecting ENE in patients with HNSCC. The review followed PRISMA guidelines. Meta‐analysis was performed using MedCalc (23.0.2), with pooled estimates of the area under the curve (AUC) and corresponding 95% confidence intervals (CI) calculated. The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) was used to analyze the methodological quality of the included studies. Results Of 57 articles retrieved, six met inclusion criteria, encompassing 2870 lymph nodes from 1407 patients. MLAs achieved a pooled AUC of 0.92 (95% CI [0.915, 0.923], p < 0.001; fixed‐effects) and 0.91 (95% CI [0.882, 0.929], p < 0.001; random‐effects), outperforming radiologists who had pooled AUCs of 0.65 (95% CI [0.645–0.654], p < 0.001; fixed‐effects) and 0.65 (95% CI [0.591–0.708], p < 0.001; random‐effects). Furthermore, MLA achieved a sensitivity ranging from 66.9% to 91.2%, compared to 24% to 96.0% by radiologists. The specificity and accuracy of MLA ranged from 72% to 96.2% and 66% to 92.2%, respectively, compared to that of radiologists, which ranged from 43.0% to 96.0% and 51.5% to 88.6%, respectively. Conclusion MLAs demonstrate superior diagnostic performance in predicting ENE in HNSCC and may serve as a valuable adjunct to radiologists in clinical practice. Level of Evidence 1.