• Mashup Score: 1

    Summary:. Early cancer detection is an attractive and promising application for liquid biopsy that might revolutionize cancer screenings. In this issue of Cancer Discovery, Foda and colleagues expand the potential utility of a machine learning fragmentome-based model, called DELFI, for detecting liver cancer in high-risk patients.See related article by Foda et al., p. 616 (5).

    Tweet Tweets with this article
    • In the Spotlight— Moving forward liquid biopsy in early #livercancer detection, by @ChristianRolfo and @Al3ssandroRusso. https://t.co/G6orwEMuz1 @TischCancer @MountSinaiNYC #LiquidBiopsy #cfDNA https://t.co/Ia8W9TiLbD

  • Mashup Score: 6

    Abstract. Liver cancer is a major cause of cancer mortality worldwide. Screening individuals at high-risk, including those with cirrhosis and viral hepatitis, provides an avenue for improved survival, but current screening methods are inadequate. In this study, we used whole-genome cell-free DNA fragmentome analyses to evaluate 724 individuals from the US, EU, or Hong Kong with hepatocellular…

    Tweet Tweets with this article
    • Detecting #LiverCancer Using Cell-free DNA Fragmentomes, by @ZHFodaMDPhD, @av_annapragada, Amy Kim, @velculescu et al. https://t.co/UU4wjYQiZE @hopkinskimmel @HopkinsMedicine #LiquidBiopsy #cfDNA https://t.co/QKDwRm4aqe

  • Mashup Score: 0

    Jamshidi et al. compare several approaches for circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED) tests. A whole-genome methylation-based approach has the best performance among those evaluated. In addition, they define a metric—clinical limit of detection (LOD)—based on tumor fraction to enable future comparison of cfDNA-based tests.

    Tweet Tweets with this article
    • New #cfDNA paper: 1) Clinical LOD a useful benchmark to assess cfDNA-based test performance 2) cTAF accounts for cfDNA cancer signal variation across cancer types and stages 3) cfDNA methylation most promising #genomic feature for cancer signal detection https://t.co/noOh2qlbZk