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

Arshbir Aulakh1, Masih Sarafan1, Amardeep S Sekhon1

  • 1MD Undergraduate Program, Faculty of Medicine, University of British Columbia, Vancouver, Canada.

The Laryngoscope
|October 10, 2025
PubMed
Summary

Machine learning algorithms (MLAs) show superior accuracy in diagnosing extra-nodal extension (ENE) in head and neck squamous cell carcinoma (HNSCC) compared to radiologists. These MLAs can significantly aid clinicians in improving diagnostic performance for ENE detection.

Keywords:
ENE predictionextranodal extensionhead and neck squamous cell carcinomamachine learning algorithmsradiologists

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Extra-nodal extension (ENE) is a critical prognostic factor in head and neck squamous cell carcinoma (HNSCC).
  • Accurate detection of ENE is crucial for treatment planning and patient outcomes.

Purpose of the Study:

  • To evaluate the clinical utility and diagnostic performance of machine learning algorithms (MLAs) for detecting ENE in HNSCC using CT imaging.
  • To compare the diagnostic accuracy of MLAs against human radiologists.

Main Methods:

  • A systematic literature review and meta-analysis were conducted following PRISMA guidelines.
  • Studies reporting diagnostic accuracy of MLAs for ENE in HNSCC were selected from multiple databases.
  • Pooled estimates of diagnostic performance metrics, including AUC, sensitivity, and specificity, were calculated for MLAs and radiologists.

Main Results:

  • MLAs demonstrated a significantly higher pooled AUC (0.92) compared to radiologists (0.65) in detecting ENE.
  • MLAs achieved a pooled sensitivity of 66.9%-91.2% and specificity of 72%-96.2%.
  • Radiologists' performance showed a sensitivity range of 24%-96.0% and specificity of 43.0%-96.0%.

Conclusions:

  • Machine learning algorithms exhibit superior diagnostic performance in predicting ENE in HNSCC.
  • MLAs show potential as a valuable adjunct tool for radiologists, enhancing ENE detection in clinical practice.