Machine-learning-based recurrence prediction in lung cancer
Principal Investigator
Name
Pawel Badura
Degrees
Ph.D., D.Sc.
Institution
Silesian University of Technology
Position Title
Associate Professor
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-916
Initial CDAS Request Approval
May 23, 2022
Title
Machine-learning-based recurrence prediction in lung cancer
Summary
The project is related to the analysis of volumetric computed tomography images and the application of machine learning techniques to predict clinical outcomes. The main goals are to combine radiomics tumor features with pathology and clinical patient metadata to predict recurrence and identify correlative radiomics and pathomics tumor features.
Aims
- to perform segmentation of lung nodules in LDCT,
- to explore segmented data for radiomics features,
- to combine microscopic and radiomics tumor features,
- to design machine learning tools for recurrence prediction
- to validate correlative radiomics and pathomics tumor features using external datasets.
Collaborators
Dr. Arkadiusz Gertych, Ph.D., Department of Surgery, Cedars Sinai Medical Center, Los Angeles