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Updated: Sep 11, 2025

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Electrochemical-assisted scattering imaging system for lymphoma cell classification using machine learning.

Linyan Xie^1,2, Ning Zhang¹, Kai Yang¹

¹School of Medical Engineering and School of Mathematical Medicine, Xinxiang Medical University, Xinxiang 453003, China.

Biomedical Optics Express

|August 14, 2025

Summary

This summary is machine-generated.

This study presents an electrochemical-assisted scattering imaging system (ESIS) for enhanced lymphoma cell classification. The novel dual-modality approach significantly improves diagnostic accuracy, aiding in personalized cancer therapies.

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Last Updated: Sep 11, 2025

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

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Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Published on: January 9, 2019

Area of Science:

Biomedical Engineering
Oncology
Analytical Chemistry

Background:

Lymphoma is a prevalent global malignancy, underscoring the need for accurate and early diagnostic methods.
Current diagnostic techniques for lymphoma can be improved for better patient outcomes and personalized treatment strategies.

Purpose of the Study:

To develop and validate an electrochemical-assisted scattering imaging system (ESIS) for precise lymphoma cell classification.
To enhance the accuracy of lymphoma subtype differentiation using a multi-modal approach.

Main Methods:

Integration of scattering imaging with electrochemical measurements using a fiber-optic probe and a 3D rGO-Ti3C2-MWCNTs composite electrode.
Simultaneous monitoring of hydrogen peroxide (H2O2) release from lymphoma cells.
Application of Support Vector Machine (SVM) algorithm for data analysis and classification.

Main Results:

The ESIS achieved a significant improvement in classification performance, with the Area Under the Curve (AUC) for HMy2.CIR cells increasing from 0.79 to 0.97.
The dual-modality approach demonstrated 90% accuracy, outperforming scattering imaging alone.
Enhanced differentiation capabilities for lymphoma subtypes were observed.

Conclusions:

The developed ESIS offers a promising tool for accurate lymphoma cell classification and subtype differentiation.
This dual-modality system holds potential for advancing personalized cancer therapies through improved diagnostics.