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开发和验证使用机器学习和dosiomics和CT radiomics特征的头癌患者的生存预后模型:一项多中心研究

Zahra Mansouri¹, Yazdan Salimi¹, Mehdi Amini¹

¹Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.

Radiation oncology (London, England)

|January 23, 2024

概括

使用临床数据,CT扫描中的放射学和剂量分布,可以预测头癌的生存率. 结合这些特征的机器学习模型显示了可接受的整体生存预测准确度.

科学领域:

放射学和医学成像技术
在瘤学瘤学.
医疗保健中的机器学习

背景情况:

头癌 (HNC) 患者的结果需要准确的预测方法.
临床数据,CT衍生的放射学和剂量分布 (剂量学) 是潜在的预测因素.

研究的目的:

为了评估临床特征的预测价值,Dosiomics,和融合CT-Dose图像对HNC患者的结果.
为了比较不同的融合策略和机器学习模型用于生存预测.

主要方法:

利用来自癌症成像档案的240名HNC患者的队列.
应用了包括非融合和融合算法 (LLRR,Wavelet,WLS) 在内的七种策略,用于CT图像和3D剂量图.
提取了215个放射性/剂量学特征和7个临床特征,采用5种特征选择方法和6种机器学习模型来预测整体生存 (OS).

主要成果:

在LLRR融合图像 (CI=0.73±0.15) 上使用最小深度 (MD) 功能选择的CoxBoost模型实现了最佳性能.
具有可变狩猎 (VH) 功能选择的glmnet模型在LLRR融合图像上也表现良好.
剂量图像与临床特征相结合,使用glmnet和Coxph分类器产生了0.72±0.14的CI.

结论:

临床特征,剂量学和CT-Dose图像的融合,当使用特定的机器学习和特征选择模型进行分析时,可以以可接受的准确度预测HNC患者的整体存活率.
机器学习方法的性能在评估的不同预测策略中是可比的.

关键词:

这就是dosiomics.头癌是头部和部的癌症.机器学习是机器学习.无线电学 (Radiomics) 是一种无线电学.对生存分析的分析.

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