基于人工智能的实时多式联络模型,用于预测头癌复发和生存率:一个多中心,跨国研究
H A Jung1, R Merkin2, A L Feng3
1Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
ESMO open
|January 28, 2026
概括
一个人工智能模型使用纵向数据准确预测头癌复发和生存率. 该工具通过提供精确的无复发生存率 (RFS) 和整体生存率 (OS) 预测来帮助治疗决策.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 医学预测 医学预测
背景情况:
- 头部和部状细胞癌 (HNSCC) 的死亡率和复发率很高.
- 对于复发性或转移性HNSCC存在有限的治疗选择.
- 准确的预后模型对于指导治疗强度和避免过度治疗至关重要.
研究的目的:
- 开发和验证用于HNSCC的AI驱动的预后模型.
- 预测治疗意图手术后的无复发生存率 (RFS) 和整体生存率 (OS).
- 将临床病理学变量与纵向实验室数据相结合,以提高预测准确度.
主要方法:
- 一项多中心预后研究,涉及975名HNSCC患者.
- 使用80/20数据分割创建的开发和内部验证队列.
- 使用XGBoost算法训练模型,预测RFS和OS在1至5年间隔.
主要成果:
- 人工智能模型在预测RFS和OS方面表现出很高的准确性,曲线下的面积 (AUC) 值通常在0.77到0.83.3之间.
- 在预测人类乳头瘤病毒 (HPV) 阳性口腔癌的生存状况方面观察到强的表现 (AUC高达0.943).
- 该模型在各种HNSCC子站点和HPV状态中显示出强大的预测能力.
结论:
- 一个利用多式联络和纵向数据的AI模型准确地预测HNSCC手术后的RFS和OS.
- 开发的模型为HNSCC管理中的个性化治疗策略提供了有价值的工具.
- 这种人工智能方法提高了预后准确性,有可能改善患者的治疗结果并优化治疗选择.
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