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基于通过机器学习进行特征选择的局部高级喉癌的生存预后预测模型

Jiangmiao Li¹, Feng Zhao¹, Junkun He¹

¹Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.

Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery

|July 28, 2025

概括

一个新的预后模型准确地预测了局部晚期喉癌 (LALC) 患者的生存率. 该工具识别高风险个体,指导个性化治疗策略,以改善结果.

科学领域:

在瘤学瘤学.
医学统计医学统计
手术瘤学手术瘤学

背景情况:

局部晚期喉癌 (LALC) 提出了复杂的生存挑战.
准确的预后对于定制治疗策略至关重要.
现有的分阶段系统可能无法完全捕捉个人风险.

研究的目的:

为了确定与LALC患者的生存相关的高风险因素.
为LALC开发和验证一个强大的预后预测模型.
帮助选择最佳的,个性化的治疗计划.

主要方法:

利用LASSO,XGBoost和随机森林从283名LALC患者中进行特征选择.
开发了一个基于COX回归模型的nomogram,具有内部验证 (bootstrap).
使用ROC,AUC,C指数和DCA评估模型性能;与AJCC第8阶段TNM相比.

主要成果:

确定了LALC存活率的七个显著预测因素.
开发的名图表显示出优异的区分 (AUC 0.852-0.829) 和校准.
在预测5年生存期方面,COX模型的表现优于AJCC第8次TNM分期.

结论:

结合年龄,治疗,手术,DAA,K+,LNR和TCIS的COX回归模型有效预测LALC的整体存活率 (OS).
该模型成功地将患者分为低风险和高风险组.
对于高风险的LALC患者,建议进行手术,可能还有辅助性放射治疗.

关键词:

生物化学标记物生物化学标记物局部发达的喉癌.机器学习是机器学习.预测模型的预测模型.

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