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Quantitative image analysis of lung nodules

Principal Investigator
Name
Ruijiang Li
Institution
Stanford University
Position Title
Assistant Professor
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-116
Initial CDAS Request Approval
Feb 5, 2015
Title
Quantitative image analysis of lung nodules
Summary
Early detection of lung cancer via low-dose CT screening has been shown to lead to improved survival in high-risk patients. However, ~95% of positive screening results from low-dose CT lung cancer screenings are false positives. False positive screening induces anxiety in patients and their families, require additional expensive tests, and may result in harm if a follow-up study leads to a complication. So improvements are clearly needed. We propose to develop and validate high-throughput, quantitative image analysis and advanced pattern classification methods to better distinguish between benign and malignant lung nodules. We will apply our methods to the NLST data with the goal of predicting the nodule type while reducing false positives.
Aims

1. Develop tools to automatically extract quantitative, high-throughput image features.
2. Develop methods to classify nodules into benign and malignant subtypes based on high-dimensional image features.
3. Evaluate the prediction performance of the algorithms using cross validation.