Observational studyClinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery2025
Machine Learning in Clinical Diagnosis of Head and Neck Cancer.
Observational study in Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Artificial intelligence in head and neck cancer: a bibliometric and visualization analysis (1995-2025).Discover oncology · 2025Article
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4 authors.
Funding
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Abstract
objectiveMachine 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.
designAn observational cohort study.
settingQueen Elizabeth University Hospital.
participantsPatients who were referred via the USOC pathway between January 2019 and May 2021.
main outcome measuresPredicting the diagnosis of patients from three categories, benign, potential malignant and malignant, using demographics and symptoms data.
resultsThe 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.
conclusionFurther 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.
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