In one paragraphArticle in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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.
2 · The registryThe 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.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
24 authors.
Haichao WangCentre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK.ORCID 0000-0002-7648-916X Paulius D MenneaCentre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK.ORCID 0000-0002-6967-1975 Grainne McAndrewCentre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK.
Ozge SonmezlerCentre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK.ORCID 0000-0002-2757-718X Dmitry S ShcherboCentre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK.ORCID 0000-0002-0266-7015 Emma-Jane DitterCancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK.ORCID 0000-0003-0625-7264 Sarah Østrup JensenCentre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK.ORCID 0000-0001-6044-3362 Alessandra I G BumaDepartment of Respiratory Medicine, Radboud University Medical Center, Nijmegen, Netherlands.
Christopher G SmithCancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK.
Zhao ChengCentre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK.ORCID 0000-0002-3514-240X Clare HarrisVictor Philip Dahdaleh Heart and Lung Research Institute, Department of Medicine, University of Cambridge, Cambridge CB2 0BB, UK.
Rosalind J CuttsBreast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK.
Sarah HrebienBreast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK.ORCID 0009-0000-9121-8299 Philip A J CrosbieDivision of Immunology, Immunity to Infection and Respiratory Medicine, Faculty of Biology Medicine and Health, University of Manchester, Manchester M13 9PT, UK.
Pippa G CorrieDepartment of Oncology, Cambridge University Hospitals NHS Trust, Cambridge CB2 0QQ, UK.
Michel M van den HeuvelDepartment of Respiratory Medicine, Radboud University Medical Center, Nijmegen, Netherlands.ORCID 0000-0002-6372-2153 Amit RoshanCentre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK.ORCID 0000-0002-2034-2759 Frank McCaughanVictor Philip Dahdaleh Heart and Lung Research Institute, Department of Medicine, University of Cambridge, Cambridge CB2 0BB, UK.ORCID 0000-0002-8012-7524 Robert C RintoulCancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK.ORCID 0000-0003-3875-3780 Florian MarkowetzCancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK.ORCID 0000-0002-2784-5308 Tommy KaplanCentre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK.ORCID 0000-0002-1892-5461 Wendy N CooperCentre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK.ORCID 0000-0003-3416-9982 Hui ZhaoCentre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK.ORCID 0009-0009-2818-2911 Nitzan RosenfeldCentre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK.ORCID 0000-0002-2825-4788 Funding
No grant is acknowledged in the PubMed record.
6 · The paper itselfAbstract
Cell-free DNA (cfDNA) in body fluids enables noninvasive cancer detection. Multifeature artificial intelligence (AI) can improve sensitivity by integrating diverse biomarkers when cancer signals are sparse. Tumor-informed assays that rely on mutations have limited practicality for early cancer detection. Emerging fragmentomic and epigenetic features underpin tumor-naive approaches to screening for individuals with low tumor burden. Here, we designed UNITE-a universal cfDNA feature ensemble framework that provides scalable cancer detection methods based on "genomic bin-fragment length" matrices derived from shallow whole-genome sequencing (sWGS) data at 0.1× depth. Using sWGS data from 2063 plasma samples (631 controls and 1432 cases from 26 cancer types), we systematically evaluated both XGBoost (UNITE-XGB) and convolutional neural networks (UNITE-CNN) across multiple feature spaces and cancer stages. In stage I-II cancer, UNITE-XGB and UNITE-CNN achieved 31 and 21% sensitivity, respectively, at 95% specificity. These findings provide roadmaps for developing multifeature AI beyond plasma biopsies.
Indexed as
Cell-Free Nucleic AcidsDeep LearningNeoplasmsWhole Genome SequencingBiomarkers, TumorConvolutional Neural NetworksHumansBiomarkers, TumorCell-Free Nucleic Acids
Identifiers
PMID42430497
PMCPMC13353424
What OpenQuestion holds
Textmetadata
LicenceCC BY
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