Survival analysis based on pathological images of lung cancer patients
Principal Investigator
Name
xiaofei xia
Degrees
master
Institution
Anhui University
Position Title
student
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-976
Initial CDAS Request Approval
Oct 19, 2022
Title
Survival analysis based on pathological images of lung cancer patients
Summary
Lung cancer is by far the leading cause of cancer death among both men and women worldwide, making up almost 25% of all cancer deaths. About 80% to 85% of lung cancers are non-small cell lung cancer (NSCLC). The two most prevalent histological types of NSCLC are lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), and each is associated with very different treatment guidelines. Specifically, the available treatment options differ for LUAD and LUSC. Therefore, reliable diagnostic decision support tools are highly demanded to empower pathologists’ efficiency and accuracy to ultimately provide better patient care.We hope to use pathological images (WSI) of NLST dataset in lung cancer survival analysis, and prove that pathological images can be used as an important support for survival analysis. We plan to develop a deep learning model for extracting biomarkers from lung cancer pathological images to better analyze the survival of patients.
Aims
1.Establish the relationship between the whole lung pathological image and the patient's survival information.
2.Fusion of the relationship between different patches in the whole pathological image.
Collaborators
xiaofei xia, Anhui University