Multiparametric image analysis and correlation with biopsy outcome
Principal Investigator
Name
Michael Folkert
Degrees
MD PhD
Institution
UT Southwestern Medical Center
Position Title
Assistant Professor
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-91
Initial CDAS Request Approval
Sep 29, 2014
Title
Multiparametric image analysis and correlation with biopsy outcome
Summary
The use of image features in computed tomography (CT) to provide prognostic information for a wide range of malignancies is an area of active investigation, with the potential for developing robust prognostic models for diagnosis and treatment outcome.
Our hypothesis is that multiparametric models that incorporate complex image information from screening CT scans will improve prediction of the outcome of subsequent lung biopsy, an invasive diagnostic procedure.
In this project, we will construct an image feature-based multiparametric prognostic model for biopsy outcome from screening lung CT scans performed at our institution, and then validate it using the NLST imaging and clinical outcomes dataset.
The ultimate goal is to provide a refined, robust, and validated model to aid in clinical decision-making for screening and diagnosis in early stage lung cancer. By establishing criteria for low- and high-risk populations, unnecessary procedures could be avoided in patients unlikely to have cancer, while patients most likely to harbor a malignant lesion will move more quickly to a tissue diagnosis.
Our hypothesis is that multiparametric models that incorporate complex image information from screening CT scans will improve prediction of the outcome of subsequent lung biopsy, an invasive diagnostic procedure.
In this project, we will construct an image feature-based multiparametric prognostic model for biopsy outcome from screening lung CT scans performed at our institution, and then validate it using the NLST imaging and clinical outcomes dataset.
The ultimate goal is to provide a refined, robust, and validated model to aid in clinical decision-making for screening and diagnosis in early stage lung cancer. By establishing criteria for low- and high-risk populations, unnecessary procedures could be avoided in patients unlikely to have cancer, while patients most likely to harbor a malignant lesion will move more quickly to a tissue diagnosis.
Aims
Determine whether CT-based multiparametric analytical models may improve prediction of biopsy outcome in patients undergoing screening CT scan for early stage lung cancer.
Collaborators
Dr. Puneeth Iyengar, MD, PhD - UT Southwestern Medical Center
Dr. Robert Timmerman, MD - UT Southwestern Medical Center
Dr. Steve Jiang, PhD - UT Southwestern Medical Center
Dr. Jing Wang, PhD, DABR - UT Southwestern Medical Center