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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
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