Fig. 1

Schematic diagram of the study design. A The training cohort (N = 608) included 191 primary liver cancer (PLC), 149 colorectal cancer (CRC), 146 lung adenocarcinoma (LUAD) patients, and 122 healthy controls, which were used to train the cancer detection and cancer origin models. The test cohort (N = 606), which included 190 PLC, 149 CRC, 146 LUAD, and 121 healthy controls, was used to evaluate model performances. B Plasma samples were collected from PLC, CRC, LUAD patients, and healthy volunteers. The cfDNA was extracted from the participant's plasma sample and subject to whole-genome sequencing (WGS). Five different feature types, including Fragment Size Coverage (FSC), Fragment Size Distribution (FSD), EnD Motif (EDM), BreakPoint Motif (BPM), and Copy Number Variation (CNV), were calculated. For each feature type, a base model was constructed based on the ensemble learning of five algorithms- GLM, GBM, Random Forest, Deep Learning, and XGBoost. The base model predictions were then ensembled into a large matrix, subsequently used to train the final ensemble stacked model