ArticleMolecular oncology2023
Building predictive disease models using extracellular vesicle microscale flow cytometry and machine learning.
Article in Molecular oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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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
15 citing papers in PubMed, 16 citations in OpenAlex.
- Engineered extracellular vesicles for combinatorial cancer therapy and imaging.Acta pharmaceutica Sinica. B · 2026Review
- Exosomes as Disease-Informed Nanoplatforms for Pulmonary Fibrosis: From Pathogenic Signaling to Precision Diagnosis and Therapy.Pharmaceutics · 2026Review
- Biomaterial Engineering for Spatiotemporal Regulation of Exosome Functions: From Design Principles to Key Applications in Regenerative Medicine.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Extracellular Vesicles in Obesity: From Pathophysiological Mediators to Therapeutic Tools.International journal of molecular sciences · 2026Review
- Current Applications and Future Directions of Artificial Intelligence in Prostate Cancer Diagnosis: A Narrative Review.Current oncology (Toronto, Ont.) · 2026Review
- Exploring the Role of Extracellular Vesicles in Pancreatic and Hepatobiliary Cancers: Advances Through Artificial Intelligence.International journal of molecular sciences · 2026Review
- Advancing Extracellular Vesicle Research: A Review of Systems Biology and Multiomics Perspectives.Proteomics · 2026Review
- Challenges and Opportunities in State-of-the-Art Proteomics Analysis for Biomarker Development From Plasma Extracellular Vesicles.Proteomics · 2026Review
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- Advanced Technologies in Extracellular Vesicle Biosensing: Platforms, Standardization, and Clinical Translation.Molecules (Basel, Switzerland) · 2026Review
- The new advance of exosome-based liquid biopsy for cancer diagnosis.Journal of nanobiotechnology · 2024Review
- A Current Synopsis of the Emerging Role of Extracellular Vesicles and Micro-RNAs in Pancreatic Cancer: A Forward-Looking Plan for Diagnosis and Treatment.International journal of molecular sciences · 2024Review
- Clinical applications and challenges in the field of extracellular vesicles.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2023Article
- Clinical analysis of EV-Fingerprint to predict grade group 3 and above prostate cancer and avoid prostate biopsy.Cancer medicine · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extracellular vesicles (EVs) are highly abundant in human biofluids, containing a repertoire of macromolecules and biomarkers representative of the tissue of origin. EVs released by tumours can communicate key signals both locally and to distant sites to promote growth and survival or impact invasive and metastatic progression. Microscale flow cytometry of circulating EVs is an emerging technology that is a promising alternative to biopsy for disease diagnosis. However, biofluid-derived EVs are highly heterogeneous in size and composition, making their analysis complex. To address this, we developed a machine learning approach combined with EV microscale cytometry using tissue- and disease-specific biomarkers to generate predictive models. We demonstrate the utility of this novel extracellular vesicle machine learning analysis platform (EVMAP) to predict disease from patient samples by developing a blood test to identify high-grade prostate cancer and validate its performance in a prospective 215 patient cohort. Models generated using the EVMAP approach significantly improved the prediction of high-risk prostate cancer, highlighting the clinical utility of this diagnostic platform for improved cancer prediction from a blood test.
Indexed as
Identifiers
What Socratic 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 Socratic graph.