Development of a personalized lung cancer risk prediction model
Principal Investigator
Name
Pinchas Akiva
Degrees
PhD
Institution
Medial-Research
Position Title
Bioinformatics Senior Scientist
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-86
Initial CDAS Request Approval
Aug 26, 2014
Title
Development of a personalized lung cancer risk prediction model
Summary
Reducing the number of individuals needed to screen in order to prevent lung cancer mortality is a challenge. The purpose of this study is to develop and validate a lung cancer risk prediction model for identifying individuals at high risk of having lung cancer. The model should aid optimizing the selection criteria of individuals to be screened for lung cancer, helping to increase the positive predictive value of lung cancer screening.
In the development process, we will utilize machine learning based algorithmic tools (such as decision tree algorithms) that analyze the characteristics of lung cancer patients, and produce a model that enables defining the probability of a specific individual to harbor lung cancer. The model will take into account smoking behavior as well as other data types such as demographics and disease history.
To increase the validity of the results, the study datasets will include the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) and the National Lung Screening Trial (NLST) datasets.
In the development process, we will utilize machine learning based algorithmic tools (such as decision tree algorithms) that analyze the characteristics of lung cancer patients, and produce a model that enables defining the probability of a specific individual to harbor lung cancer. The model will take into account smoking behavior as well as other data types such as demographics and disease history.
To increase the validity of the results, the study datasets will include the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) and the National Lung Screening Trial (NLST) datasets.
Aims
Develop a model that generates an individual risk score to enable identifying individuals at high risk for having lung cancer.