Multiparametric Image Analysis and Correlation With Outcomes in Lung Cancer Screening and Early Stage Lung Cancer
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Determine whether CT-based multiparametric analytical models may improve prediction of biopsy and treatment outcome in patients undergoing screening CT scan and/or treatment for early stage lung cancer
Full description
The 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 theNLST imaging and clinical outcomes dataset.
This study involves no treatment or invasive procedures. Investigator will review all charts of patients who were treated for early stage lung cancer with definitive radiation therapy at UTSW or Parkland Memorial hospital, diagnosed with a malignancy from January 1, 2004 to October 31, 2014, to compile demographic, diagnostic, therapeutic, outcome, and toxicity data. Investigator expect that this will include approximately 200 patient charts. This data will be analyzed statistically and used for future directed research. Investigator will also analyze an anonymized dataset of patients from the National Lung Cancer Screening Trial (NLST) provided by the National Cancer Institute (NCI)
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Inclusion criteria
Patients that have been diagnosed with lung cancer, and are treated at Department of Radiation Oncology, UTSW or Parkland Memorial Hospital.
Exclusion criteria
There will be no absolute exclusion criteria as long as the inclusion criteria have been met.
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Central trial contact
Kajal Desai, MS; Sarah Neufeld, MS
Data sourced from clinicaltrials.gov
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