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Pleural effusion as a sample matrix for laboratory analyses in cancer management: a perspective

  • Martina Spisarová , Hana Študentová , Kateřina Holá and Bohuslav Melichar EMAIL logo
Published/Copyright: March 3, 2025

Abstract

Malignant effusions, pleural effusion or ascites, represent a common problem in cancer patients. Pleural effusion in a cancer patient may be caused also by non-neoplastic conditions, and the diagnosis of malignant pleural effusion is established by the demonstration of tumor cells in pleural fluid. Microscopical detection of tumor cells in pleural fluid often fails, and there is an unmet medical need for more sensitive methods. New approaches, including isolation using magnetic beads coated with monoclonal antibodies targeting antigens expressed on tumor cells not only increase the diagnostic sensitivity, but also provide material for the analysis of predictive biomarkers. The advent of new technologies illustrates the incremental role of laboratory medicine in the management of patients with malignant effusions.


Corresponding author: Bohuslav Melichar, MD, PhD, Department of Oncology, Faculty of Medicine and Dentistry, Palacký University and University Hospital, Zdravotníků 248, 77900 Olomouc, Czech Republic; and Department of Oncology and Radiotherapy, Charles University Medical School and Teaching Hospital, Hradec Králové, Czech Republic, E-mail:

Funding source: Grant of Palacky University

Award Identifier / Grant number: IGA_LF_2024_004

Funding source: Conceptual development of research organization supported by the Ministry of Health, Czech Republic

Award Identifier / Grant number: FNOL, 00098892

  1. Research ethics: Not applicable for an opinion paper.

  2. Informed consent: Not applicable for an opinion paper.

  3. Author contributions: MS: Conceptualization, writing the original draft, review, and editing; HS: Writing the original draft, review and editing; KH: Writing the original draft; BM: Conceptualization, writing the original draft, review, and editing. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: This work was supported by the Ministry of Health, Czech Republic – conceptual development of research organization (FNOL, 00098892) and Palacky University’s IGA_LF_2024_004.

  7. Data availability: Not relevant for an opinion paper.

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Received: 2025-02-19
Accepted: 2025-02-19
Published Online: 2025-03-03
Published in Print: 2025-05-26

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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