Predicting EGFR Expression from Lung Cancer Pathology images Using DNN

Abstract Number:

1455 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Yibo ZHAI (1), Tong Wang (2), Shuangge Ma (1)

Institutions:

(1) N/A, N/A, (2) Yale University, N/A

Co-Author(s):

Tong Wang  
Yale University
Shuangge Ma  
N/A

First Author:

Yibo ZHAI  
N/A

Presenting Author:

Yibo ZHAI  
N/A

Abstract Text:

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.

Keywords:

Feature images extraction|Deep Neural Network|Gene expression prediction| | |

Sponsors:

Biometrics Section

Tracks:

Model/Variable Selection

Can this be considered for alternate subtype?

Yes

Are you interested in volunteering to serve as a session chair?

Yes

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 3, 2025. The registration fee is non-refundable.

I understand