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A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
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Label-Free Cancer Detection Methods Based on Biophysical Cell Phenotypes.

Isabel Calejo1,2, Ana Catarina Azevedo1,3, Raquel L Monteiro1,2

  • 1Department of Biomedicine, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal.

Bioengineering (Basel, Switzerland)
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

Label-free methods offer advanced, cost-effective cell analysis by focusing on biophysical properties. These innovative techniques enhance cancer diagnostics and monitoring, improving data reliability and reducing sample damage.

Keywords:
biophysical biomarkerscancer cellscancer diagnosticscell sortinglabel-free methods

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Area of Science:

  • Biomedical Engineering
  • Clinical Diagnostics
  • Cell Biology

Background:

  • Cell labeling is standard but has limitations like sample damage and cost.
  • Label-free approaches leverage intrinsic cell properties, enhancing reliability and efficiency.
  • Technological advancements are crucial for improving clinical diagnosis and biomarker detection.

Purpose of the Study:

  • To review recent advancements in label-free cancer cell detection technologies.
  • To highlight the benefits of label-free methods for clinical and biotechnological applications.
  • To analyze the capabilities of various label-free techniques for cancer diagnostics.

Main Methods:

  • Review of current label-free technologies including microscopy, cytometry, microfluidics, and spectroscopy.
  • Integration of artificial intelligence with label-free methods for enhanced analysis.
  • Focus on biophysical alterations and their relevance to liquid biopsy.

Main Results:

  • Label-free methods offer advantages like reduced workload, minimal sample damage, and cost-effectiveness.
  • These techniques enable non-invasive cell identification and monitoring of cellular interactions.
  • AI integration improves the speed and accuracy of cancer cell detection and analysis.

Conclusions:

  • Label-free approaches provide a reliable and efficient alternative to traditional cell labeling for cancer diagnostics.
  • Interdisciplinary innovations are key to characterizing and identifying cancer cells without labeling.
  • Comparative analysis guides the deployment of these technologies in oncology and biomedical research.