Modeling lung cancer risk with Gradient-Boosted Decision-Trees
Principal Investigator
Name
Jean-Emmanuel Bibault
Degrees
MD, PhD
Institution
INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
Position Title
Dr
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-814
Initial CDAS Request Approval
Jul 15, 2021
Title
Modeling lung cancer risk with Gradient-Boosted Decision-Trees
Summary
The aim of this project is to validate on NLST data a gradient-boosted model we created to predict 6-year lung cancer risk. Several models already exist: they are currently being used to guide patients and physicians on lung cancer screening. The state-of-the-art models, the PLCO m2012, the Lung Cancer Death Risk Assessment Tool and the Lung Cancer Risk Assessment Tool have been validated on PLCO, NLST and UK Biobank data.
Aims
- Calculate Accuracy, AUC, precision-recall of our XGBoost model on NLST data and validate our approach on an external dataset (the NLST dataset).
- Deploy the model online to guide lung screening strategies
Collaborators
Lei Xing (Stanford University)