ArticleNPJ precision oncology2025
An artificial intelligence-based model for prediction of clonal hematopoiesis variants in cell-free DNA samples.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Hematopoietic stem cell aging: a review of transcriptional and multi-omics insights and potential paths for AI integration.Experimental & molecular medicine · 2026Review
- Tumor-naïve ctDNA detection with deep learning-enhanced error suppression for sensitive mutation calling.Genome medicine · 2026Article
- Circulating Tumor DNA as Emerging Predictive and Prognostic Biomarker in Prostate Cancer.Cancers · 2026Review
- Clinical decisions in clonal hematopoiesis: a contemporary review for clinicians.Haematologica · 2026Review
- What's hidden in plain sight? Impact of clonal hematopoiesis on the risk and progression of nonhematologic cancers.Haematologica · 2026Review
- Liquid Biopsy in Colorectal Cancer: Future Perspectives Through the Lens of Artificial Intelligence-A Comprehensive Review of Novel Literature.International journal of molecular sciences · 2026Review
- Circulating tumor DNA-guided early detection and minimal residual disease monitoring in pancreatic and biliary cancers: evidence, barriers, and opportunities.World journal of surgical oncology · 2026Review
- From multi-omics to deep learning: advances in cfDNA-based liquid biopsy for multi-cancer screening.Biomarker research · 2025Review
- Enhanced patient selection with quantitative continuous scoring of PD-L1 expression for IO treatment in metastatic NSCLC.NPJ precision oncology · 2025Article
- Fragmentomic-based algorithm to computationally predict tumor-somatic, germline, and clonal hematopoiesis variant origin in liquid biopsy.The journal of liquid biopsy · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Circulating tumor DNA is a critical biomarker in cancer diagnostics, but its accurate interpretation requires careful consideration of clonal hematopoiesis (CH), which can contribute to variants in cell-free DNA and potentially obscure true tumor-derived signals. Accurate detection of somatic variants of CH origin in plasma samples remains challenging in the absence of matched white blood cells sequencing. Here we present an open-source machine learning framework (MetaCH) which classifies variants in cfDNA from plasma-only samples as CH or tumor origin, surpassing state-of-the-art classification rates.
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.