Extended Data Fig. 9. DELFI detection of cancer and tissue of origin prediction.
a, Analyses of individual cancer types using the DELFI-combined approach had AUCs ranging from 0.86 to >0.99. b, Receiver operator characteristics for detection of cancer using cfDNA fragmentation profiles and other genome-wide features in a machine learning approach are depicted for a cohort of 215 healthy individuals and each stage of 208 patients with cancer with ≥ 95% specificity shaded in blue. c, Receiver operator characteristics for DELFI tissue prediction of bile duct, breast, colorectal, gastric, lung, ovarian, or pancreatic cancer are depicted. In order to increase sample sizes within cancer type classes, we included cases detected with a 90% specificity, and the lung cancer cohort was supplemented with the addition of baseline cfDNA data from 18 lung cancer patients with prior treatment36. d, DELFI tissue of origin prediction.