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Quantitative feature analysis in pathology

Principal Investigator

Name
Daniel Rubin

Institution
Stanford University

Position Title
Asst. Professor

Email
dlrubin@stanford.edu

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-40

Initial CDAS Request Approval
Oct 30, 2013

Title
Quantitative feature analysis in pathology

Summary
Our goal is to develop and to identify quantitative image features in digital pathology images that stratify survival in the NLST patients. We will develop quantitative image features, extract them from the pathology images, and develop machine learning methods to predict survival from those features or stratify survival groups using those features. We will also look to develop quantitative image features correlations with the radiology images.

Aims

1. develop quantitative image features characterizing cancers on digital pathology images
2. build machine learning methods to predict survival or survival groups from the iamges features
3. evaluate our methods in a held-out dataset from NLST.