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机械学习用于预测头部和部状细胞癌的生存结果

Kevin Atsou¹, Anne Auperin², Jôel Guigay³

¹COMPutational Pharmacology and Clinical Oncology Department, Inria Sophia Antipolis - Méditerranée, Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105, Marseille, France.

CPT: pharmacometrics & systems pharmacology

|December 26, 2024

概括

整合瘤动力学 (TK) 的机器学习模型对预测头部和部状细胞癌 (HNSCC) 患者的整体存活率 (OS) 有希望. 虽然对于后进展生存 (PPS) 效果较差,但基于TK的模型在OS预测方面表现优于RECIST.

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:

预测晚期头部和部状细胞癌 (HNSCC) 的生存结果对于治疗计划至关重要.
像RECIST这样的传统方法可能无法完全捕捉治疗反应动态.
瘤动力学 (TK) 建模为评估治疗疗效提供了一种动态方法.

研究的目的:

开发和评估机器学习 (ML) 模型,集成TK参数,用于预测HNSCC的后进展生存率 (PPS) 和总生存率 (OS).
将TK衍生参数的预测性能与RECIST标准进行比较.
评估基于TK的ML模型与传统的生存模型的有效性.

主要方法:

采用了一种机械学习方法,将TK建模和ML结合起来.
分析了TPExtreme试验中526名晚期HNSCC患者的数据,这些患者接受了化疗和 cetuximab.
双指数模型用于TK参数提取 (TKL1和TK4) 并与基线参数结合,预测PPS和OS4.

主要成果:

ML算法显示的预测能力低于PPS的考克斯模型.
使用TK4参数的随机生存森林模型显示,与基于RECIST的指标相比,OS的预测优越.
基于TK4的随机生存森林模型提供了对循环后4 OS (OS4) 的公正预测.

结论:

将TK建模与ML集成显示了在HNSCC中改进OS预测的潜力.
来自TK的参数,特别是TK4,在HNSCC的生存结果评估中可能比RECIST具有优势.
基于TK的ML模型的进一步验证可以提高HNSCC的治疗评估和患者管理.

关键词:

深度学习是一种深度学习.头部和部状细胞癌瘤机器学习是机器学习.生存分析,生存分析.瘤动力学瘤动力学

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