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
phuang12@jhmi.edu

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-214

Initial CDAS Request Approval
Apr 21, 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

• Lung Cancer Recurrence Risk Prediction through Integrated Deep Learning Evaluation.
  Huang P, Illei PB, Franklin W, Wu PH, Forde PM, Ashrafinia S, Hu C, Khan H, Vadvala HV, Shih IM, Battafarano RJ, Jacobs MA, Kong X, Lewis J, Yan R, Chen Y, Housseau F, Rahmim A, Fishman EK, Ettinger DS, ...show more Pienta KJ, Wirtz D, Brock MV, Lam S, Gabrielson E
  Cancers (Basel). 2022 Aug 27; Volume 14 (Issue 17) PUBMED
• Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method.
  Huang P, Lin CT, Li Y, Tammemagi MC, Brock MV, Atkar-Khattra S, Xu Y, Hu P, Mayo JR, Schmidt H, Gingras M, Pasian S, Stewart L, Tsai S, Seely JM, Manos D, Burrowes P, Bhatia R, Tsao MS, Lam S
  Lancet. 2019 Oct 17; Volume 1 (Issue 7): Pages E353-E362 PUBMED
• Added Value of Computer-aided CT Image Features for Early Lung Cancer Diagnosis with Small Pulmonary Nodules: A Matched Case-Control Study.
  Huang P, Park S, Yan R, Lee J, Chu LC, Lin CT, Hussien A, Rathmell J, Thomas B, Chen C, Hales R, Ettinger DS, Brock M, Hu P, Fishman EK, Gabrielson E, Lam S
  Radiology. 2018; Volume 286 (Issue 1): Pages 286-295 PUBMED