Machine Learning to Predict Extranodal Extension in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".