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

NCT ID: NCT03563976

Last Updated: 2025-06-03

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 2000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2015-02-18
• Study Completion Date: 2028-03-15

Brief Summary

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

Detailed 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)

Conditions

Lung Cancer

Study Design

• Observational Model Type: CASE_ONLY
• Study Time Perspective: RETROSPECTIVE

Interventions

Retrospective Study

The medical charts are the subjects. The institutional charts will be identified by the use of definitive radiation therapy correlating with an early stage lung cancer diagnosis during the above time frame. The data from these charts will be entered into a password protected excel spreadsheet. The charts will be identified by name, medical record number, date of birth, and social security number. These are all patients treated by all hospitals and clinics affiliated with UTSW and Parkland. At the time of study, some of the patients will have expired but some will be alive and in the regional North Texas area. Thus, given the minimal risk nature of this retrospective chart review, we could not reasonably conduct this research with a full waiver of consent. The NLST external dataset is proved by the NCI, with no identifying characteristics.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

Patients that have been diagnosed with lung cancer, and are treated at Department of Radiation Oncology, UTSW or Parkland Memorial Hospital.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 99 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

University of Texas Southwestern Medical Center

OTHER

Sponsor Role: lead

Responsible Party

Jing Wang

Associate Professor of Medicine

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Jing Wang, MD

Role: PRINCIPAL_INVESTIGATOR

UTSW Radiation Oncology

Locations

UT Southwestern Medical Center

Dallas, Texas, United States

Site Status: RECRUITING

Countries

United States

Central Contacts

Sarah Neufeld, MS

Role: CONTACT

Sarah.hardee@UTSouthwestern.edu

214-648-1836

Kajal Desai, MS

Role: CONTACT

kajal.desai@utsouthwestern.edu

2146458525

Facility Contacts

Sarah Neufeld

Role: primary

Sarah.Hardee@UTSouthwestern.edu

214-648-1836

Other Identifiers

STU 122014-052

Identifier Type: -

Identifier Source: org_study_id