Data Mining Techniques to predict Early Stage Lung Cancer in NLST population

Principal Investigator

Name
Jinglu Wang

Degrees
Bachelors of Arts

Institution
Weill Cornell Medical College

Position Title
Graduate Student

Email
jinglu@nyu.edu

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-229

Initial CDAS Request Approval
Jul 19, 2016

Title
Data Mining Techniques to predict Early Stage Lung Cancer in NLST population

Summary
I would like to use data mining techniques such as logistic regression, decision trees, and predictive modeling to assess the likelihood of a patient having a malignant lung cancer at stage 1 detected on annual CT scans.

The primary goal is to have a better measure of patients at high-risk for developing non-small cell lung carcinomas and to find malignancies at an earlier stage, where surgical resection is a possibility.

A secondary goal would be to create a data mining tool to rank patients who do not show signs of lung carcinoma to receive different levels of following up based on the initial and subsequent presentation of their nodules on radiologic imaging.

Aims

The primary goal is to have a better measure of patients at high-risk for developing non-small cell lung carcinomas and to find malignancies at an earlier stage, where surgical resection is a possibility.

A secondary goal would be to create a data mining tool to rank patients who do not show signs of lung carcinoma to receive different levels of following up based on the initial and subsequent presentation of their nodules on radiologic imaging.

Collaborators

Arian Jung, PhD (Weill Cornell Medical College)
Fei Wang, PhD (Weill Cornell Medical College)