Microcapillary-Derived Plasmonic-Enhanced Cluster through the Self-Assembly Process for Breast Cancer Diagnosis.

Area of Science:

• Nanotechnology and Spectroscopy
• Biomedical Engineering
• Artificial Intelligence in Diagnostics

Background:

• Current breast cancer detection methods using surface-enhanced Raman scattering (SERS) often require extensive sample pretreatment and skilled handling.
• Existing biomarker-based SERS approaches (exosomes, miRNA) show promise but are limited by complex, time-consuming preparation steps.

Purpose of the Study:

• To develop and validate a rapid, label-free SERS platform for breast cancer diagnosis directly from blood plasma.
• To eliminate the need for complex sample pretreatment, making the diagnostic process faster and more accessible.

Main Methods:

• Fabrication of a microcapillary-confined gold nanoparticle cluster to create numerous "hot spots" for enhanced electromagnetic fields.
• Development of a streamlined SERS measurement protocol involving mixing blood plasma with gold nanoparticles within the microcapillary.
• Application of a machine learning model for classifying breast cancer patients versus normal participants based on SERS spectral data.

Main Results:

• The fabricated gold nanoparticle cluster effectively amplified Raman signals, demonstrated by the detection of R6G molecules.
• The AI-powered SERS platform successfully differentiated between breast cancer patients and healthy individuals.
• Achieved a high diagnostic accuracy of 87.5% for breast cancer classification.

Conclusions:

• The label-free, AI-assisted SERS platform offers a promising, rapid, and simplified approach for breast cancer diagnosis.
• This method overcomes the limitations of traditional SERS techniques by removing the need for sample pretreatment.
• The technology holds potential for improving early cancer detection accessibility and efficiency.