Skip to Main Content

An official website of the United States government

Government Funding Lapse

Because of a lapse in government funding, the information on this website may not be up to date, transactions submitted via the website may not be processed, and the agency may not be able to respond to inquiries until appropriations are enacted. The NIH Clinical Center (the research hospital of NIH) is open. For more details about its operating status, please visit  cc.nih.gov. Updates regarding government operating status and resumption of normal operations can be found at OPM.gov.

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
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)