Unsupervised feature extraction for benign and malignant pulmonary nodules

Principal Investigator

Name
Matthew Stephens

Degrees
MD, MS

Institution
University of Cincinnati

Position Title
Assistant Professor of Radiology

Email
mattjstephens@gmail.com

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-314

Initial CDAS Request Approval
Jun 13, 2017

Title
Unsupervised feature extraction for benign and malignant pulmonary nodules

Summary
Study will compare results from internal set of benign and malignant lung cancers to the NLST dataset to evaluate robustness of unsupervised feature extraction in the evaluation and classification of pulmonary nodules. This will require the set of baseline CT scans containing the known malignancies from the NLST dataset and a random sampling of CTs from patients who had no documented history of cancer but documentation of a nodule on a CT report.

Aims

To compare features extracted using unsupervised machine learning algorithms from benign and malignant nodules to identify specific signatures for malignant and benign nodules.

Collaborators

Sangita Kapur, University of Cincinnati
Tony Fattouch, University of Cincinnati