Skip to Main Content

COVID-19 is an emerging, rapidly evolving situation.

What people with cancer should know: https://www.cancer.gov/coronavirus

Get the latest public health information from CDC: https://www.coronavirus.gov

Get the latest research information from NIH: https://covid19.nih.gov/

  1. Home
  2. Approved Projects
  3. Predicting overall survival using CT and patholog…

Predicting overall survival using CT and pathological image features

Principal Investigator
Name
Peng Huang
Degrees
PhD
Institution
Johns Hopkins University
Position Title
Associate Professor
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-214
Initial CDAS Request Approval
Jun 17, 2016
Title
Predicting overall survival using CT and pathological image features
Summary
We propose to extract texture features from CT and pathological images. The association between these two platforms of image markers will be investigated. Two models will be developed using machine learning for high dimensional data. One is the diagnostic model to predict time to cancer diagnosis using clinical and CT image markers; the other one is the overall survival model to predict time to death using clinical, CT image, and pathological image markers adjusting for treatments received.
Aims

Aim 1. To extract CT image texture features and to test the hypothesis that combining CT image texture features with clinical and epidemiological risk factors can reduce the false positive rate of CT image diagnosis.

Aim 2. To extract pathological image features and to identify markers from CT and pathological image features that are associated with overall survival.

Collaborators

Elliot Fishman
Edward Gabrielson
Junghoon Lee

Related Publications