Machine Learning in Clinical Diagnosis of Head and Neck Cancer

Hollie Black; David Young; Alexander Rogers; Jenny Montgomery

doi:10.1111/coa.14220

Machine Learning in Clinical Diagnosis of Head and Neck Cancer

Clin Otolaryngol. 2024 Sep 14. doi: 10.1111/coa.14220. Online ahead of print.

Authors

Hollie Black¹, David Young², Alexander Rogers³, Jenny Montgomery³

Affiliations

¹ Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow, UK.
² Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK.
³ Department of Otolaryngology, Head and Neck Surgery, Queen Elizabeth University Hospital, Glasgow, UK.

PMID: 39275960
DOI: 10.1111/coa.14220

Abstract

Objective: Machine learning has been effective in other areas of medicine, this study aims to investigate this with regards to HNC and identify which algorithm works best to classify malignant patients.

Design: An observational cohort study.

Setting: Queen Elizabeth University Hospital.

Participants: Patients who were referred via the USOC pathway between January 2019 and May 2021.

Main outcome measures: Predicting the diagnosis of patients from three categories, benign, potential malignant and malignant, using demographics and symptoms data.

Results: The classic statistical method of ordinal logistic regression worked best on the data, achieving an AUC of 0.6697 and balanced accuracy of 0.641. The demographic features describing recreational drug use history and living situation were the most important variables alongside the red flag symptom of a neck lump.

Conclusion: Further studies should aim to collect larger samples of malignant and pre-malignant patients to improve the class imbalance and increase the performance of the machine learning models.

Keywords: area under the receiver operator curve; head and neck cancer; machine learning; up‐sampling.