机器学习驱动的风险分层和助剂治疗指导在口腔癌的辅助治疗
Andrea Costantino1, Nir Tsur2, Daniel Uralov3
1Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.
JCO precision oncology
|March 2, 2026
概括
机器学习模型有效地将口腔状细胞癌 (OCSCC) 患者分为低风险,中等风险和高风险组,以整体存活 (OS). 这种分层指导辅助治疗决策,加强高风险患者的治疗,并可能降低低风险个体的升级.
科学领域:
- 在瘤学瘤学.
- 机器学习 机器学习
- 生物统计学 生物统计学
背景情况:
- 口腔状细胞癌 (OCSCC) 在预测患者的结果方面提出了重大挑战.
- 准确的术后风险分层对于定制辅助疗法和改善整体存活率至关重要.
研究的目的:
- 在OCSCC中开发和验证用于术后风险分层的机器学习 (ML) 模型.
- 评估ML衍生的风险组是否影响辅助疗法的有效性.
主要方法:
- 利用国家癌症数据库识别OCSCC患者进行初级手术治疗.
- 在一个单独的手术队列上开发并测试了DeepSurv,NMTLR和RSF ML模型.
- 为更大的队列生成风险得分,将患者分类为低风险,中等风险和高风险组,以分析辅助治疗效果.
主要成果:
- DeepSurv模型获得了最高的性能 (C指数为0.73),NMTLR/RSF的结果也相似.
- 由ML衍生的风险组显示出明显的5年OS率:77.6% (低),53.0% (中等) 和29.3% (高).
- 辅助疗法 (RT/CRT) 在中期和高风险组显著改善了OS,但在低风险组没有. 与RT相比,CRT提供了适度的优势.
结论:
- 基于ML的风险分层准确地识别了OCSCC患者,这些患者从辅助治疗中获益最多.
- 支持加强中级/高风险患者的治疗,并考虑降低低风险患者的治疗强度.
- 建议对临床实施进行外部前性验证.
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