# Label-Free Cancer Detection Methods Based on Biophysical Cell Phenotypes

## TL;DR

This review explores label-free methods for detecting cancer cells by analyzing their physical properties, offering faster and more reliable alternatives to traditional labeling techniques.

## Contribution

The paper provides a comprehensive review of recent interdisciplinary innovations in label-free cancer cell detection methods.

## Key findings

- Label-free methods reduce false results and improve data reliability by analyzing morpho-biophysical cell properties.
- Integration with artificial intelligence enhances the utility of label-free techniques for cancer diagnostics and monitoring.
- A comparative analysis of methods addresses throughput, resolution, and detection capabilities for biomedical applications.

## Abstract

Progress in clinical diagnosis increasingly relies on innovative technologies and advanced disease biomarker detection methods. While cell labeling remains a well-established technique, label-free approaches offer significant advantages, including reduced workload, minimal sample damage, cost-effectiveness, and simplified chip integration. These approaches focus on the morpho-biophysical properties of cells, eliminating the need for labeling and thus reducing false results while enhancing data reliability and reproducibility. Current label-free methods span conventional and advanced technologies, including phase-contrast microscopy, holographic microscopy, varied cytometries, microfluidics, dynamic light scattering, atomic force microscopy, and electrical impedance spectroscopy. Their integration with artificial intelligence further enhances their utility, enabling rapid, non-invasive cell identification, dynamic cellular interaction monitoring, and electro-mechanical and morphological cue analysis, making them particularly valuable for cancer diagnostics, monitoring, and prognosis. This review compiles recent label-free cancer cell detection developments within clinical and biotechnological laboratory contexts, emphasizing biophysical alterations pertinent to liquid biopsy applications. It highlights interdisciplinary innovations that allow the characterization and potential identification of cancer cells without labeling. Furthermore, a comparative analysis addresses throughput, resolution, and detection capabilities, thereby guiding their effective deployment in biomedical research and clinical oncology settings.