
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

Source
EurekAlert
Related News

AI Time Series Model Boosts EEG-Based Seizure Prediction by 44%
UC Santa Cruz engineers' 'future-guided' deep learning improves seizure prediction accuracy using EEG data.

NTU Singapore to Launch Master's in AI in Medicine for Clinicians and Technologists
NTU Singapore will launch a new MSc in Artificial Intelligence in Medicine to train clinicians and technologists in clinical AI applications from 2026.

AI Accurately Predicts Lymph Node Extension in HPV-related Throat Cancer via CT
An AI pipeline automates lymph node segmentation and extranodal extension prediction from CT in HPV-positive oropharyngeal cancer, correlating with patient outcomes.