Next Generation Chest X-Ray Tomosynthesis for Screening of Lung Cancer

Chest X-ray Tomosynthesis for Detection of Lung Cancer and Lung Disease

The goal of this observational clinical trial is to learn if chest tomosynthesis is a potential alternative to computed tomography for the detection of lung cancer. It will also develop artificial intelligence tools to aid in the diagnosis of lung cancer on chest tomosynthesis images. The main questions it aims to answer are:

• What is the accuracy of chest X-ray tomosynthesis in diagnosing lung cancer in a population of individuals undergoing lung cancer screening or evaluation of a suspicious lung nodule?
• Can artificial intelligence help us detect lung cancer on chest tomosynthesis images?

Researchers will compare chest tomosynthesis images to computed tomography scans for each participant to see how they compare in diagnosing lung cancer.

Participants will a chest tomosynthesis scan in addition to their routine clinical computed tomography scan.

Study Overview

• Status: Recruiting
• Conditions: Lung Cancer
• Intervention / Treatment: Diagnostic test: Chest X-ray Tomosynthesis

Detailed Description

Lung cancer remains the most common cause of cancer death in the United States for which low-dose CT has proven benefit for early detection and survival from lung cancer. However, adoption remains low. Furthermore, >95% of nodules detected on low-dose CT, especially those smaller than 6 mm, do not represent cancer. We have partnered to develop a novel chest x-ray tomosynthesis (CXRT) device with the hypothesis that this device might be an alternative to CT for detection of lung cancer. We seek to recruit a cohort of patients to undergo CXRT, composed of patients concurrently undergoing lung cancer screening CT and diagnostic CT for new lung cancer. We will determine the effectiveness of CXRT for detecting lung cancer in this population, evaluating its sensitivity and specificity for detecting cancer and lung nodules at multiple size thresholds in a multireader study. We will additionally develop artificial intelligence algorithms and evaluate their efficacy to further enhance cancer detection.

• Study Type: Observational
• Enrollment (Estimated): 1000

Contacts and Locations

• Study Contact: Name: Alexander Cypro, MD; Phone Number: 858-2462196; Email: aidaresearch2023@gmail.com

Study Locations

• United States
  • California
    • San Diego, California, United States, 92093
      • Recruiting
      • University Of California San Diego
      • Contact: Alexander Cypro, MD; Phone Number: 858-246-2196; Email: aidaresearch2023@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Patients undergoing lung cancer screening CT or undergoing diagnostic chest CT for incidentally detected pulmonary nodules or lung cancer.

Description

Inclusion Criteria:

• undergoing lung cancer screening
• undergoing evaluation of suspicious pulmonary nodule
• newly diagnosed lung cancer

Exclusion Criteria:

• prior history of lung cancer treatment

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Chest X-ray Tomosynthesis Participants; Participants who had a recent, routine clinical chest computed tomography scan for lung cancer screening or evaluation of nodule suspicious for lung cancer will undergo an additional chest X-ray tomosynthesis scan.	Diagnostic test: Chest X-ray Tomosynthesis; An imaging device that uses X-rays projected from multiple angles to reconstruct a three-dimensional images of the chest; Other Names: CXRT

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Chest X-ray tomosynthesis detection of lung cancer	Diagnostic accuracy of chest tomosynthesis in identifying biopsy-proven lung cancer	through 12/31/2026

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Chest X-ray tomosynthesis detection of suspicious nodules	Diagnostic accuracy of chest tomosynthesis in identifying pulmonary nodules seen on chest CT at multiple minimum size thresholds: 6-mm, 8-mm, 10-mm	through 12/31/2026

Collaborators and Investigators

• Sponsor: University of California, San Diego
• Collaborators: AIxSCAN, Inc.
• Investigators: Principal Investigator: Albert Hsiao, MD PhD, UC San Diego, Department of Radiology

Publications and helpful links

Helpful Links

• AIxScan, Inc

Study record dates

Study Major Dates

• Study Start (Actual): November 2, 2023
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): June 30, 2027

Study Registration Dates

• First Submitted: August 27, 2024
• First Submitted That Met QC Criteria: August 27, 2024
• First Posted (Actual): August 29, 2024

Study Record Updates

• Last Update Posted (Actual): August 29, 2024
• Last Update Submitted That Met QC Criteria: August 27, 2024
• Last Verified: August 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Neoplasms; Lung Diseases; Neoplasms by Site; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• Other Study ID Numbers: 805210

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No