AI Deep Learning Model Classifies Lung Cancer Exosomes via Nanomechanics

DGIST researchers developed a deep learning model that classifies lung cancer exosomes based on physical properties measured by atomic force microscopy.

Key Details

• DGIST team used AFM to measure nanomechanical properties (stiffness, height-to-radius) of exosomes from NSCLC cell lines with different genetic mutations.
• AI model (DenseNet-121) classified exosomes by origin, achieving 96% accuracy and AUC of 0.92.
• Exosome stiffness reflected KRAS and EGFR mutations in their respective lung cancer cell lines.
• The method enables high-precision, label-free, liquid biopsy-based lung cancer diagnosis.
• Study published July 8, 2025, in Analytical Chemistry.

Why It Matters

This approach could offer a rapid, non-invasive way to diagnose lung cancer genetic mutations, reducing the need for tissue biopsies. Leveraging AI with nanomechanical imaging pushes boundaries in early cancer detection and personalized medicine.

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