ArticleCancer discovery2023
Moving Forward Liquid Biopsy in Early Liver Cancer Detection.
Article in Cancer discovery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Neoadjuvant therapy for hepatocellular carcinoma: emerging combinations and biomarker challenges.Frontiers in immunology · 2026Review
- Artificial Intelligence (AI) and Liquid Biopsy Transforming Early Detection of Liver Metastases in Gastrointestinal Cancers.Current cancer drug targets · 2026Review
- Serum high mobility group box 1 as a potential biomarker for the progression of kidney disease in patients with type 2 diabetes.Frontiers in immunology · 2024Article
- Circulating tumor DNA in clinical trials for solid tumors: Challenges and current applications.The journal of liquid biopsy · 2023Review
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.