42: Predicting EGFR Expression from Lung Cancer Pathology images Using DNN

Music City Center 

In this project, we develop a computational framework to predict Epidermal Growth Factor Receptor (EGFR) expression levels using pathology images. The workflow begins with cell segmentation and image feature extraction, performed using the scikit-image library in Python, to derive quantitative features from each patient's pathology images. These features are then utilized in a deep neural network model for variable selection and EGFR expression prediction. Our models demonstrate strong predictive performance and two professional pathologists validate the extracted features, ensuring their clinical interpretation. This approach has the potential to significantly reduce the costs of gene expression sequencing and provide valuable guidance for pathologists in clinical trial analysis. By integrating computational pathology with deep learning, our work offers an efficient workflow for EGFR expression prediction, bridging the gap between traditional pathology and advanced statistical models.

Feature images extraction

Deep Neural Network

Gene expression prediction 

Biometrics Section