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: