Construction of prediction model of early glottic cancer based on machine learning.
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| Title: | Construction of prediction model of early glottic cancer based on machine learning. |
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| Authors: | Zhao, Wang (AUTHOR), Zhi, Jingtai (AUTHOR), Zheng, Haowei (AUTHOR), Du, Jianqun (AUTHOR), Wei, Mei (AUTHOR), Lin, Peng (AUTHOR), Li, Li (AUTHOR), Wang, Wei (AUTHOR) |
| Source: | Acta Oto-Laryngologica. Jan2025, Vol. 145 Issue 1, p72-80. 9p. |
| Subjects: | Random forest algorithms, Prediction models, Research funding, Laryngeal tumors, Early detection of cancer, Logistic regression analysis, Endoscopic surgery, Retrospective studies, Cancer patients, Chi-squared test, Laryngoscopy, Medical records, Acquisition of data, Machine learning, Decision trees, Endoscopy |
| Abstract (English): | Background: 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. Objective: To explore the clinical applicability of the diagnosis of early glottic cancer based on ML combined with NBI. Material and methods: 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. Results: 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. Conclusions and significance: We developed a prediction model for early glottic cancer using RF, which outperformed other models. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): | 声门型喉癌的早期诊断是成功治疗的关键, 机器学习(ML)结合窄带成像(NBI)喉镜为声门型喉癌的早期诊断提供了新思路。 探讨用机器学习结合NBI来诊断早期声门型癌的临床适用性。 对200例确诊为喉肿块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。采用卡方检验和多变量逻辑回归分析, 探讨可能预测早期声门型癌的临床和喉镜特征。随后, 将三种经典机器学习方法, 即随机森林(RF)、支持向量机(SVM)和决策树(DT), 与NBI内窥镜图像相结合, 识别与声门型癌相关的危险因素, 并构建和比较预测模型。 基于 RF 的模型比其他方法预测更准确, 并且具有显著的优势。RF 模型的准确度、精确度、召回率和 F1 指数以及 AUC 值分别为 0.96、0.90、1.00、0.95 和 0.97。 我们使用 RF 开发了一个早期声门癌预测模型, 该模型的表现优于其它模型。 [ABSTRACT FROM AUTHOR] |
| Copyright of Acta Oto-Laryngologica is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Background: 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. Objective: To explore the clinical applicability of the diagnosis of early glottic cancer based on ML combined with NBI. Material and methods: 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. Results: 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. Conclusions and significance: We developed a prediction model for early glottic cancer using RF, which outperformed other models. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00016489 |
| DOI: | 10.1080/00016489.2024.2430613 |
