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Microwave-driven Synthesis of Iron Oxide Nanoparticles for Fast Detection of Atherosclerosis

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Predicting Plaque Vulnerability Using Machine Learning-Enabled Nanoagents Sensitized Molecular High-Resolution

Yan Gong^1,2, Menglin Wu³, Xiang Zhang²

¹Department of Radiology, Medical Imaging Institute of Tianjin, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin 300192, China.

|February 24, 2026

Summary

A new nano-agent assisted machine learning (nano-AML) technology accurately predicts atherosclerotic plaque vulnerability. This approach, using molecular high-resolution vessel wall MR imaging, offers a reliable method for assessing plaque risk.

Keywords:

atherosclerotic plaque vulnerability foamy macrophage-targeted nanoagents high-resolution vessel wall imaging machine learning molecular imaging radiomics

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Published on: August 4, 2011

Area of Science:

Biomedical imaging
Nanotechnology
Machine learning

Background:

Molecular imaging with paramagnetic nanoagents shows promise for detecting atherosclerotic plaque destabilization.
Current magnetic resonance (MR) imaging lacks quantitative descriptors for precise plaque risk stratification.

Purpose of the Study:

To develop a novel nano-agent assisted machine learning (nano-AML) technology for direct plaque vulnerability assessment.
To improve the noninvasive prediction of plaque vulnerability using molecular high-resolution vessel wall MR imaging (HR-VWI).

Main Methods:

Developed synergistic nanoagents (tFM-Nanoagents) for enhanced MR imaging.
Applied a machine learning (ML) approach to decode tFM-Nanoagents sensitized HR-VWI data.
Generated and validated an imaging-derived risk score (nano-AML score) in a preclinical model.

Main Results:

The nano-AML score effectively classified plaques as "vulnerable" or "stable" with high accuracy (AUC training: 0.871, validation: 0.870).
The nano-AML score significantly outperformed the commercial contrast agent Gadovist in predicting plaque vulnerability (AUC training: 0.560, validation: 0.538).

Conclusions:

The developed nano-AML technology provides a robust and reliable method for predicting vulnerable plaques.
This approach has the potential to significantly advance the noninvasive assessment of atherosclerotic plaque risk.