机器学习在头颈癌临床诊断中的应用
Machine Learning in Clinical Diagnosis of Head and Neck Cancer.
作者信息
Black Hollie, Young David, Rogers Alexander, Montgomery Jenny
机构信息
Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow, UK.
Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK.
出版信息
Clin Otolaryngol. 2025 Jan;50(1):31-38. doi: 10.1111/coa.14220. Epub 2024 Sep 14.
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.
目的
机器学习在医学的其他领域已取得成效，本研究旨在针对头颈癌对此进行调查，并确定哪种算法在对恶性肿瘤患者进行分类方面效果最佳。
设计
一项观察性队列研究。
地点
伊丽莎白女王大学医院。
参与者
2019年1月至2021年5月期间通过美国肿瘤协作组（USOC）途径转诊的患者。
主要观察指标
利用人口统计学和症状数据预测患者的三类诊断，即良性、潜在恶性和恶性。
结果
有序逻辑回归的经典统计方法在数据上表现最佳，曲线下面积（AUC）为0.6697，平衡准确率为0.641。描述娱乐性药物使用史和生活状况的人口统计学特征与颈部肿块这一警示症状是最重要的变量。
结论
进一步的研究应旨在收集更大样本的恶性和癌前病变患者，以改善类别不平衡并提高机器学习模型的性能。
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