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Single Vesicle Surface Protein Profiling and Machine Learning-Based Dual Image Analysis for Breast Cancer Detection.

Mitchell Lee Taylor¹, Madhusudhan Alle¹, Raymond Wilson¹

¹Department of Chemistry, The University of Memphis, Memphis, TN 38152, USA.

Nanomaterials (Basel, Switzerland)

|November 8, 2024

Summary

This summary is machine-generated.

A new AI-powered method accurately analyzes extracellular vesicles (EVs) in blood, enabling early detection and monitoring of HER2-positive breast cancer. This technology quantifies specific EV markers, improving diagnostic capabilities.

Keywords:

breast cancer extracellular vesicle gold nanoparticles machine learning optical imaging single-vesicle technology

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Area of Science:

Biomedical Engineering
Oncology
Nanotechnology

Background:

Single-vesicle molecular profiling of extracellular vesicles (EVs) is crucial for cancer detection and monitoring.
Dual imaging single-vesicle technology (DISVT) offers a method to quantify targeted EVs, but analysis challenges persist due to false signals and large data volumes.

Purpose of the Study:

To develop a fully automatic, machine learning-based dual imaging analysis method for EVs.
To apply this AI-assisted DISVT for detecting and staging HER2-positive breast cancer.

Main Methods:

A convolutional neural network (Resnet34) with transfer learning was employed for image analysis.
The model was trained using a combination of experimental and synthetic data.
Fractions of EpCAM- and CD24-positive EVs were quantified in plasma samples from breast cancer patients and healthy donors.

Main Results:

EpCAM-positive and CD24-positive EVs were negligible in healthy donors and Stage I breast cancer patients.
EV fractions increased from Stage II (18%) to Stage III (29%) for EpCAM-positive EVs, with a similar trend for CD24-positive EVs.
Both markers detected HER2-positive breast cancer at Stages II, III, or IV, differentiating stages except III and IV.

Conclusions:

AI-assisted DISVT provides a simple, sensitive, and efficient platform for quantitative EV subtype characterization.
This technology holds significant potential for both basic research and clinical applications in cancer diagnostics.
The developed method accurately detects and stages HER2-positive breast cancer using EV marker analysis.