DGIST researchers developed a deep learning model that classifies lung cancer exosomes based on physical properties measured by atomic force microscopy.
Key Details
- 1DGIST team used AFM to measure nanomechanical properties (stiffness, height-to-radius) of exosomes from NSCLC cell lines with different genetic mutations.
- 2AI model (DenseNet-121) classified exosomes by origin, achieving 96% accuracy and AUC of 0.92.
- 3Exosome stiffness reflected KRAS and EGFR mutations in their respective lung cancer cell lines.
- 4The method enables high-precision, label-free, liquid biopsy-based lung cancer diagnosis.
- 5Study 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.
Source
EurekAlert
Related News
•EurekAlert
AI Model Improves Prediction of Knee Osteoarthritis Progression Using MRI and Biomarkers
A new AI-assisted model that combines MRI, biochemical, and clinical data improves predictions of worsening knee osteoarthritis.
•EurekAlert
AI Model Predicts Multiple Genetic Markers from Colorectal Pathology Slides
Researchers developed and validated an AI model that simultaneously detects multiple genetic markers in colorectal cancer tissue slides.
•EurekAlert
Photonic Chip Enables Versatile Neural Networks for Imaging and Speech AI
Chinese scientists have developed a reconfigurable integrated photonic chip capable of running diverse neural networks, including those for image and speech processing, with high efficiency.