Multiparametric Image Analysis and Correlation With Outcomes in Lung Cancer Screening and Early Stage Lung Cancer

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)

Inclusion Criteria:

1) Patients that have been diagnosed with lung cancer, and are treated at Department of
Radiation Oncology, UTSW.

2Patients are greater than 18 years of age.

3)Patients understand a written informed consent document and are willing to sign the
consent form.

4)The tumor must be ineligible for definitive surgical resection.

Exclusion Criteria:

1. Women who are pregnant or trying to get pregnant

2. Children (under age of 18)

3. The tumor must be ineligible for stereotactic body radiation therapy.

4. Chemotherapy given within one week of study registration.

5. Evidence of small cell histology.