Computational tools for classification and histologic grading of lung cancer
Principal Investigator
Name
Ruijiang Li
Degrees
PhD
Institution
Stanford University
Position Title
Assistant Professor
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-169
Initial CDAS Request Approval
Oct 15, 2015
Title
Computational tools for classification and histologic grading of lung cancer
Summary
Since the new 2011 IASLC/ATS/ERS classification of lung adenocarcinoma was introduced, there have been a growing number of studies demonstrating the utility of comprehensive histological subtyping in identifying significant prognostic subsets of patients. However, a major obstacle to its implementation in routine clinical practice is the inter-observer variations and limited reproducibility, where a medium to low kappa value was reported even among expert lung cancer pathologists. In addition, there is no established histologic grading system for most lung cancers. In this project, we propose to develop and validate computational tools for automated, reliable classification and histologic grading of lung cancer based on H&E images in the NLST cohort.
Aims
1. Develop computational tools to automatically extract quantitative image features from tissue microarray data.
2. Develop methods to classify and grade cancers based on the extracted image features.
3. Evaluate the algorithm’s classification accuracy as well as the prognostic value by using cross validation.