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Related Experiment Video

Updated: Jun 6, 2025

Semi-automatic PD-L1 Characterization and Enumeration of Circulating Tumor Cells from Non-small Cell Lung Cancer Patients by Immunofluorescence

Semi-automatic PD-L1 Characterization and Enumeration of Circulating Tumor Cells from Non-small Cell Lung Cancer Patients by Immunofluorescence

Published on: August 14, 2019

Antigen-independent single-cell circulating tumor cell detection using deep-learning-assisted biolasers.

Weishu Wu¹, Yu Zhang², Xiaotian Tan³

¹Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA; Center for Wireless Integrated MicroSensing and Systems (WIMS(2)), University of Michigan, Ann Arbor, MI, 48109, USA; Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI, 48109, USA.

Biosensors & Bioelectronics

|November 30, 2024

Summary

This summary is machine-generated.

This study introduces a novel, antigen-independent method for detecting circulating tumor cells (CTCs) using deep learning and single-cell lasers. This approach achieves high sensitivity and specificity, offering new possibilities for cancer diagnostics and research.

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Last Updated: Jun 6, 2025

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Detection and Isolation of Circulating Melanoma Cells using Photoacoustic Flowmetry

Detection and Isolation of Circulating Melanoma Cells using Photoacoustic Flowmetry

Published on: November 25, 2011

Area of Science:

Biomedical Engineering
Optical Physics
Cancer Research

Background:

Circulating tumor cells (CTCs) are crucial biomarkers for cancer prognosis.
Current CTC detection relies on antigen-dependent immuno-labeling, limiting its scope.
Need for antigen-independent methods for broader CTC identification.

Purpose of the Study:

To develop and validate an antigen-independent method for CTC detection using deep learning and single-cell lasers.
To assess the performance of the developed classifier in detecting pancreatic cancer CTCs.
To evaluate the model's generalization capabilities for different cancer types and enumeration.

Main Methods:

Utilized a deep-learning-assisted single-cell biolaser system.
Developed a Deep Cell-Laser Classifier (DCLC) analyzing unique single-cell lasing mode patterns.
Applied nucleic-acid-stained cells within optical cavities for laser measurement.

Main Results:

Achieved 94.3% sensitivity and 99.9% specificity in detecting pancreatic cancer cell lines.
Demonstrated zero-shot learning capability for classifying different pancreatic cancer cell lines.
Successfully enumerated CTCs in pancreatic and lung cancer patients with consistent trends to conventional methods.

Conclusions:

Single-cell lasers combined with deep learning offer a powerful, antigen-independent approach for CTC detection.
The DCLC model shows high accuracy and zero-shot generalization for cancer cell classification and enumeration.
This technology opens new avenues for rare cell identification and clinical applications in cancer diagnostics.