Machine Learning in Clinical Diagnosis of Head and Neck Cancer

IF 1.7 4区医学 Q2 OTORHINOLARYNGOLOGY Clinical Otolaryngology Pub Date : 2024-09-14 DOI:10.1111/coa.14220

Hollie Black, David Young, Alexander Rogers, Jenny Montgomery

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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.

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头颈癌临床诊断中的机器学习

目标机器学习在其他医学领域非常有效，本研究旨在对HNC进行研究，并确定哪种算法最适合对恶性患者进行分类.设计观察性队列研究.设置伊丽莎白女王大学医院.参与者2019年1月至2021年5月期间通过USOC路径转诊的患者.主要结果测量利用人口统计学和症状数据，预测良性、潜在恶性和恶性三类患者的诊断结果经典统计方法序数逻辑回归在数据上效果最佳，AUC 为 0.6697，平衡准确率为 0.641。描述娱乐性吸毒史和生活状况的人口统计学特征是最重要的变量，同时也是颈部肿块这一红色标志症状的重要变量。

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来源期刊

Clinical Otolaryngology 医学-耳鼻喉科学

CiteScore

4.00

自引率

4.80%

发文量

106

审稿时长

>12 weeks

期刊介绍： Clinical Otolaryngology is a bimonthly journal devoted to clinically-oriented research papers of the highest scientific standards dealing with: current otorhinolaryngological practice audiology, otology, balance, rhinology, larynx, voice and paediatric ORL head and neck oncology head and neck plastic and reconstructive surgery continuing medical education and ORL training The emphasis is on high quality new work in the clinical field and on fresh, original research. Each issue begins with an editorial expressing the personal opinions of an individual with a particular knowledge of a chosen subject. The main body of each issue is then devoted to original papers carrying important results for those working in the field. In addition, topical review articles are published discussing a particular subject in depth, including not only the opinions of the author but also any controversies surrounding the subject. • Negative/null results In order for research to advance, negative results, which often make a valuable contribution to the field, should be published. However, articles containing negative or null results are frequently not considered for publication or rejected by journals. We welcome papers of this kind, where appropriate and valid power calculations are included that give confidence that a negative result can be relied upon.