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

NCT ID: NCT06577883

Last Updated: 2024-08-29

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

1000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-11-02

Study Completion Date

2027-06-30

Brief Summary

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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.

Detailed Description

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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.

Conditions

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Lung Cancer

Study Design

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Observational Model Type

COHORT

Study Time Perspective

CROSS_SECTIONAL

Study Groups

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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.

Chest X-ray Tomosynthesis

Intervention Type DIAGNOSTIC_TEST

An imaging device that uses X-rays projected from multiple angles to reconstruct a three-dimensional images of the chest

Interventions

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Chest X-ray Tomosynthesis

An imaging device that uses X-rays projected from multiple angles to reconstruct a three-dimensional images of the chest

Intervention Type DIAGNOSTIC_TEST

Other Intervention Names

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CXRT

Eligibility Criteria

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Inclusion Criteria

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

Exclusion Criteria

* prior history of lung cancer treatment
Minimum Eligible Age

30 Years

Maximum Eligible Age

85 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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AIxSCAN, Inc.

UNKNOWN

Sponsor Role collaborator

University of California, San Diego

OTHER

Sponsor Role lead

Responsible Party

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Albert Hsiao

Professor in Residence, Department of Radiology

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Albert Hsiao, MD PhD

Role: PRINCIPAL_INVESTIGATOR

UC San Diego, Department of Radiology

Locations

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University of California San Diego

San Diego, California, United States

Site Status RECRUITING

Countries

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United States

Central Contacts

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Alexander Cypro, MD

Role: CONTACT

858-2462196

Facility Contacts

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Alexander Cypro, MD

Role: primary

858-246-2196

Related Links

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Other Identifiers

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805210

Identifier Type: -

Identifier Source: org_study_id

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