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Updated: Jun 20, 2026

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

Cancer diagnosis method based on multi-spectral diffraction imaging for cell recognition.

Sizhe Dong¹, Yanfei Wang², Feiyang Jiang²

¹State-Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau, China; Faculty of Science and Technology - ECE, University of Macau, Macau, China.

Biosensors & Bioelectronics

|June 18, 2026

Summary

This summary is machine-generated.

This study introduces a novel lens-free imaging system for rapid, label-free cancer cell recognition. The technology achieves high accuracy for multiple cancer types, offering a cost-effective, automated solution for clinical applications.

Keywords:

Cancer diagnosis Diffraction imaging High-throughput Label-free Multi-spectral Portable

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Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing

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Published on: September 25, 2011

Area of Science:

Biomedical Engineering
Optical Imaging
Cancer Diagnostics

Background:

Traditional cancer cell detection methods often require biomarker labels, leading to high costs, low throughput, and complex workflows.
Limitations in automation and integration hinder the widespread clinical application of existing cell recognition technologies.

Purpose of the Study:

To develop an automated, high-throughput, and cost-effective online cancer cell recognition application.
To overcome the drawbacks of conventional methods by utilizing multi-spectral lens-free cell diffraction fingerprint features.
To enable accurate and real-time identification of various cancer cell types without chemical labeling.

Main Methods:

A high-throughput lens-free wide-field diffraction imaging platform was employed for multi-spectral imaging of cancer and normal cell types.
Cell diffraction fingerprints were extracted using Gray Level Co-occurrence Matrix (GLCM) after normalization by binary masking.
A Bayesian-Optimized Support Vector Machine (SVM) was utilized for model training and recognition, comparing multi-spectral vs. single-spectral modes.

Main Results:

The proposed system achieved high recognition accuracies for different cancer cell lines: 96.0% for HeLa, 98.0% for Huh-7, 95.2% for A549, and 96.0% for MCF-7.
Co-culture experiments validated the model's accuracy in distinguishing cancer from normal cells.
Multi-spectral illumination demonstrated superior recognition performance compared to single-spectral modes.

Conclusions:

The developed lens-free imaging application provides accurate, real-time, label-free recognition of multiple cancer cell types.
Key advantages include the elimination of chemical labeling, rapid imaging, and the use of a portable device.
This technology holds significant potential for reducing operational costs, simplifying workflows, and facilitating broader clinical applications in cancer diagnostics.