A machine-learning approach for pancreatic neoplasia classification based on plasma extracellular vesicles
By leveraging advanced analytical ML-driven approaches and integrating diverse data types, this study achieved significant accuracy, assisting patient's risk estimation and supporting the feasibility for early detection of pancreatic cancer. Going beyond currently used biomarkers such as CEA, or CA19.9, EV-based features represent an added value offering increased diagnostic capacity.
Authors
- Myrto Costopoulos (ORCID: https://orcid.org/0000-0003-3415-414X)
- Agapi Kataki (ORCID: https://orcid.org/0009-0008-8930-5383)
- Manousos M. Konstadoulakis
- Alexandros-Georgios Tzingounis
- Dimitrios I. Fotiadis (ORCID: https://orcid.org/0000-0002-5987-9350)
- Κωνσταντίνα Κούρου (ORCID: https://orcid.org/0000-0003-4310-2739)
- Ana‐Maria Iosif (ORCID: https://orcid.org/0000-0001-7283-2015)
- Ioanna Angelioudaki
- Nikolaos Memos (ORCID: https://orcid.org/0000-0001-6671-475X)
- Vassiliki I. Kigka (ORCID: https://orcid.org/0009-0005-0580-7334)
- Androniki-Maria Skreka
Institutions
- University of Ioannina (GR)
- National and Kapodistrian University of Athens (GR)
- Bioanalytica (Switzerland) (CH)
- Aretaeio Hospital (GR)
- Foundation for Research and Technology Hellas (GR)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-04-25
- DOI
- https://doi.org/10.3389/fonc.2025.1540195
- Citations
- 3
- Primary Topic
- Extracellular vesicles in disease
- Type
- article
- Field-Weighted Citation Impact
- 2.42
Funders
- University of West Attica
- HORIZON EUROPE Framework Programme