Search
Abstract
Background: Liquid biopsy is a minimally invasive collection of a patient body fluid sample. In oncology, they offer several advantages compared to traditional tissue biopsies. However, the potential of this method in endometrial cancer (EC) remains poorly explored. We studied the utility of tumor educated platelets (TEPs) and circulating tumor DNA (ctDNA) for preoperative EC diagnosis, including histology determination. Methods: TEPs from 295 subjects (53 EC patients, 38 patients with benign gynecologic conditions, and 204 healthy women) were RNA-sequenced. DNA sequencing data were obtained for 519 primary tumor tissues and 16 plasma samples. Artificial intelligence was applied to sample classification. Results: Platelet-dedicated classifier yielded AUC of 97.5% in the test set when discriminating between healthy subjects and cancer patients. However, the discrimination between endometrial cancer and benign gynecologic conditions was more challenging, with AUC of 84.1%. ctDNA-dedicated classifier discriminated primary tumor tissue samples with AUC of 96% and ctDNA blood samples with AUC of 69.8%. Conclusions: Liquid biopsies show potential in EC diagnosis. Both TEPs and ctDNA profiles coupled with artificial intelligence constitute a source of useful information. Further work involving more cases is warranted.
Citations
-
1 4
CrossRef
-
0
Web of Science
-
1 6
Scopus
Authors (13)
-
Marta Łukasiewicz
-
Sylwia Łapińska-Szumczyk dr
-
Robert Różański
-
Sjors In ’t Veld
-
Michał Bieńkowski
-
Tomasz Stokowy dr inż
-
Magdalena Ratajska
-
Myron G Best dr
-
Thomas Würdinger prof.
-
Anna Żaczek dr hab.
-
Anna Supernat dr
-
Jacek Jassem Prof.
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/cancers13225731
- License
-
open in new tab
Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Cancers
no. 13,
ISSN: 2072-6694 - Language:
- English
- Publication year:
- 2021
- Bibliographic description:
- Łukasiewicz M., Pastuszak K., Łapińska-Szumczyk S., Różański R., Veld S. I. ’., Bieńkowski M., Stokowy T., Ratajska M., Best M. G., Würdinger T., Żaczek A., Supernat A., Jassem J.: Diagnostic Accuracy of Liquid Biopsy in Endometrial Cancer// Cancers -Vol. 13,iss. 22 (2021), s.5731-
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/cancers13225731
- Verified by:
- Gdańsk University of Technology
seen 157 times
Recommended for you
Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
- M. A. Jopek,
- K. Pastuszak,
- S. Cygert
- + 5 authors
imPlatelet classifier: image‐converted RNA biomarker profiles enable blood‐based cancer diagnostics
- K. Pastuszak,
- A. Supernat,
- M. G. Best
- + 8 authors
Artificial Intelligence in the Diagnosis of Onychomycosis—Literature Review
- B. Bulińska,
- M. Mazur-Milecka,
- M. Sławińska
- + 2 authors