While the utility of circulating cell‐free DNA (cfDNA) in cancer screening and early detection have recently been investigated by testing genetic and epigenetic alterations, here, an original approach by examining cfDNA quantitative and structural features is developed. First, the potential of cfDNA quantitative and structural parameters is independently demonstrated in cell culture, murine, and human plasma models. Subsequently, these variables are evaluated in a large retrospective cohort of 289 healthy individuals and 983 patients with various cancer types; after age resampling, this evaluation is done independently and the variables are combined using a machine learning approach. Implementation of a decision tree prediction model for the detection and classification of healthy and cancer patients shows unprecedented performance for 0, I, and II colorectal cancer stages (specificity, 0.89 and sensitivity, 0.72). Consequently, the methodological proof of concept of using both quantitative and structural biomarkers, and classification with a machine learning method are highlighted, as an efficient strategy for cancer screening. It is foreseen that the classification rate may even be improved by the addition of such biomarkers to fragmentomics, methylation, or the detection of genetic alterations. The optimization of such a multianalyte strategy with this machine learning method is therefore warranted.

中文翻译：

---

用于癌症筛查的循环 DNA 定量分析的机器学习辅助评估

虽然最近通过测试遗传和表观遗传改变来研究循环游离 DNA (cfDNA) 在癌症筛查和早期检测中的效用，但本文开发了一种通过检查 cfDNA 定量和结构特征的原始方法。首先，cfDNA 定量和结构参数的潜力在细胞培养、小鼠和人血浆模型中得到独立证明。随后，在由 289 名健康个体和 983 名患有各种癌症类型的患者组成的大型回顾性队列中对这些变量进行了评估；年龄重采样后，该评估是独立完成的，并使用机器学习方法组合变量。用于检测和分类健康患者和癌症患者的决策树预测模型的实施显示了 0、I 和 II 期结直肠癌的前所未有的性能（特异性为 0.89，敏感性为 0.72）。因此，强调使用定量和结构生物标志物的概念方法证明以及机器学习方法的分类，作为癌症筛查的有效策略。可以预见，通过将此类生物标志物添加到片段组学、甲基化或遗传改变的检测中，甚至可以提高分类率。因此，使用这种机器学习方法优化这种多分析物策略是有必要的。