ReviewProteomes2025
In Search of Ideal Solutions for Cancer Diagnosis: From Conventional Methods to Protein Biomarkers in Liquid Biopsy.
Review in Proteomes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Nanomaterial -enabled flexible sensors for continuous biomedical monitoring and closed-loop intervention.Discover nano · 2026Review
- Revisiting the Lipid-Cancer Axis: PCSK9, ANGPTL3, and CETP as Emerging Biomarkers and Therapeutic Targets in Oncology.Biomolecules · 2026Review
- Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.Cancer cell international · 2026Review
- Detection of Breast Lesions Utilizing iBreast Exam: A Pilot Study Comparison with Clinical Breast Exam.Cancers · 2026Article
- Early multi-cancer detection using liquid biopsy: emerging biomarkers and clinical strategies.Frontiers in oncology · 2026Review
- Beyond Structure: The Dynamic Role of the Extracellular Matrix Components in Immune Evasion.Cancer communications (London, England) · 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
7 authors.
Funding
Abstract
Cancer detection has made significant progress, moving from conventional methods to innovative, non-invasive or minimally invasive approaches aimed at improving early diagnosis, precision, and treatment outcomes. This review examines current and emerging diagnostic technologies, including liquid biopsy (LB), molecular biomarkers, and artificial intelligence (AI). LB analyzes biomarkers in bodily fluids, showing promise in detecting tumors at molecular levels, monitoring cancer progression, and predicting treatment responses. The assignment of specific proteoforms, often linked to tumor subtype, stage, and therapy resistance, adds another layer of diagnostic precision, offering valuable insights for personalized oncology. However, the clinical application of LB faces challenges related to sensitivity, specificity, tumor heterogeneity, and a lack of standardized protocols. Relatively high costs, complex result interpretation, and privacy concerns also hinder its widespread adoption in clinical practice. Despite these challenges, advancements in AI, nanotechnology, and multi-omics strategies offer opportunities to enhance cancer diagnostic accuracy. Future developments, including wearable biosensors and lab-on-a-chip technologies, could lead to personalized, real-time cancer detection with improved patient outcomes, potentially redefining cancer care and fostering a more proactive, patient-centered healthcare approach.
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.