Additional file 1 of Letter to the Editor: An ultra-sensitive assay using cell-free DNA fragmentomics for multi-cancer early detection

Additional file 1 : Supplementary Table 1. Participant demographics and baseline characteristics. Supplementary Table 2. Performance of the first-level cancer detection model. Supplementary Table 3. Performances of the cancer detection model on different subgroups based on clinical characteristics. Supplementary Table 4. Cancer detection model robustness test using downsampled (4× to 1× coverage depths) WGS data. Each downsampled coverage depth was repeated five times. Supplementary Table 5. Performances of the cancer detection model on extra healthy volunteer and at-risk patient cohort. Supplementary Table 6. The performance of the second level cancer origin model shown in the confusion matrix table. Supplementary Table 7. Cancer origin model robustness test using downsampled (4× to 1× coverage depths) WGS data. Each downsampled coverage depth was repeated five times.

附加文件1：补充表1. 受试者人口统计学特征与基线临床特征。补充表2. 一级癌症检测模型的性能表现。补充表3. 癌症检测模型在基于临床特征划分的不同亚组中的性能。补充表4. 采用降采样（覆盖深度范围为4×至1×）的全基因组测序（WGS, Whole Genome Sequencing）数据开展的癌症检测模型稳健性检验，每组降采样覆盖深度均重复5次实验。补充表5. 癌症检测模型在额外健康志愿者与高风险患者队列中的性能表现。补充表6. 以混淆矩阵形式呈现的二级癌症溯源模型的性能。补充表7. 采用降采样（覆盖深度范围为4×至1×）的全基因组测序（WGS）数据开展的癌症溯源模型稳健性检验，每组降采样覆盖深度均重复5次实验。