结果预测在老年人与头部和部癌症接受化学辐射的老年人
Sebastian N Marschner1,2, Elia Lombardo1, Erik Haehl1
1Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
JAMA otolaryngology-- head & neck surgery
|November 6, 2025
概括
人工神经网络 (ANN) 能够有效地预测头部和部状细胞癌 (HNSCC) 的老年人的生存结果. 这些模型将患者分为不同风险群体,帮助该群体做出个性化治疗决策.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 老年医学 老年医学
背景情况:
- 在临床试验中,患有头角状细胞癌 (HNSCC) 的老年人代表性不足.
- 对于这一群体,基于证据的治疗决定有限.
- 人工神经网络 (ANN) 提供了个性化治疗建议的潜力.
研究的目的:
- 开发和外部验证ANN以预测整体生存 (OS) 和无进展生存 (PFS).
- 专注于老年人 (≥65岁) 患有HNSCC,经历了最终的化学辐射.
主要方法:
- 使用来自SENIOR注册 (19个学术癌症中心) 数据的回顾性国际队列研究.
- 开发和验证的ANN模型是用最终化学辐射治疗的患者.
- 模型性能使用ROC-AUC和精度回忆AUC进行评估;可通过Shapley添加式解释来解释.
主要成果:
- OS ANN将患者分为两层,分为高风险/低风险组 (ROC-AUC 0.68).
- PFS ANN也表现出类似的歧视 (ROC-AUC为0.64).
- 关键的预测特征包括HPV状态,功能,ECOGPS和节点分类.
结论:
- 使用常规临床数据的ANN模型有效地将HNSCC的老年人分为预后组.
- 整合ANN可以支持针对这个弱势群体的个性化治疗决策.
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