Zhao Wang, Zhi Jingtai, Zheng Haowei, Du Jianqun, Wei Mei, Lin Peng, Li Li, Wang Wei
Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China.
Institute of Otolaryngology of Tianjin, Tianjin, China.
Acta Otolaryngol. 2025 Jan;145(1):72-80. doi: 10.1080/00016489.2024.2430613. Epub 2024 Dec 30.
The early diagnosis of glottic laryngeal cancer is the key to successful treatment, and machine learning (ML) combined with narrow-band imaging (NBI) laryngoscopy provides a new idea for the early diagnosis of glottic laryngeal cancer.
To explore the clinical applicability of the diagnosis of early glottic cancer based on ML combined with NBI.
A retrospective study was conducted on 200 patients diagnosed with laryngeal mass, and the general clinical characteristics and pathological results of the patients were collected. Chi-square test and multivariate logistic regression analysis were used to explore clinical and laryngoscopic features that could potentially predict early glottic cancer. Afterward, three classical ML methods, namely random forest (RF), support vector machine (SVM), and decision tree (DT), were combined with NBI endoscopic images to identify risk factors related to glottic cancer and to construct and compare the predictive models.
The RF‑based model was found to predict more accurately than other methods and have a significant predominance over others. The accuracy, precision, recall and F1 index, and AUC value of the RF model were 0.96, 0.90, 1.00, 0.95, and 0.97.
We developed a prediction model for early glottic cancer using RF, which outperformed other models.
声门型喉癌的早期诊断是治疗成功的关键，机器学习（ML）结合窄带成像（NBI）喉镜检查为声门型喉癌的早期诊断提供了新思路。
探讨基于ML结合NBI诊断早期声门癌的临床适用性。
对200例诊断为喉部肿物的患者进行回顾性研究，收集患者的一般临床特征和病理结果。采用卡方检验和多因素logistic回归分析，探索可能预测早期声门癌的临床和喉镜特征。随后，将三种经典的ML方法，即随机森林（RF）、支持向量机（SVM）和决策树（DT），与NBI内镜图像相结合，识别与声门癌相关的危险因素，并构建和比较预测模型。
基于RF的模型预测比其他方法更准确，且比其他方法具有显著优势。RF模型的准确率、精确率、召回率、F1指数和AUC值分别为0.96、0.90、1.00、0.95和0.97。
我们利用RF开发了一种早期声门癌预测模型，其性能优于其他模型。
