ReviewCancer cell international2026
Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.
Review in Cancer cell international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Circulating Tumor Function: A Systems Biology Framework for Liquid Biopsy in Genitourinary Cancers.Genes · 2026Review
- Chasing the FoxO in Metabolic Disorders: Novel Considerations for Oxidative Stress, Programmed Cell Death, Wnt, and the Gut Microbiome.Antioxidants (Basel, Switzerland) · 2026Review
- Integrating multi-omics data for next-generation cancer research and precision medicine.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- AI-driven CRISPR strategies in breast cancer: Organoid modeling, adaptive editing, and precision delivery.Iranian journal of basic medical sciences · 2026Review
- Running out the clock: Circadian rhythm dysfunction in cognitive disease.International review of neurobiology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liquid biopsy has evolved as a transformative strategy revolutionizing the oncology field. It encompasses the detection of circulating biomarkers derived from tumors including circulating tumor DNA, extracellular vesicles, tumor educated platelets, circulating tumor cells, and circulating RNAs within body fluids. Nonetheless, its application in the clinical settings continues to be constrained by issues related to limited sensitivity, specificity, and lack of standardization. This review uniquely examines the convergence of artificial intelligence (AI) and machine learning (ML) algorithms with liquid biopsy to overcome these barriers and advance precision oncology. Focusing on four major malignancies; breast, lung, colorectal cancers, and hepatocellular carcinoma; we critically evaluate how AI-powered liquid biopsy improves early detection of cancer, prognosis prediction, and monitoring treatment response, while also forecasting recurrence and enabling patient stratification. We further highlight emerging algorithmic innovations, translational challenges, and ethical considerations, emphasizing the urgent need for harmonized validation frameworks to ensure reproducibility and clinical adoption. By leveraging AI-driven molecular insights, liquid biopsy can transition from a research concept to a routine clinical assay, enabling individualized therapeutic strategies, improving long-term survival, and ultimately transforming cancer care into a more predictive, personalized, and patient-centered paradigm.
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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.