Assessing AI for Detecting Lung Nodules and Cancer: Pre- and Post-Deployment Study

NCT06746324 | Withdrawn

• 1 other identifier: qXR-LN-UFL-001
• Study Type: observational
• Target: N/A
• Locations: 0 countries
• Sites: N/A

Brief Summary

The study evaluates the impact of qXR-LN compared to standard radiologist-only interpretations before and after AI deployment. The goal is to compare how well lung nodules and cancers are detected in two time periods: before and after the implementation of the AI tool in routine clinical practice. The study aims to determine whether the AI system can help radiologists identify more actionable lung nodules and diagnose lung cancer earlier, ultimately improving patient outcomes. No changes will be made to patients' standard care, and all treatment decisions will be based on the clinical judgment of physicians. The study includes patients over 35 years old who undergo chest X-rays for various medical reasons, excluding those with known lung cancer.

Conditions

Lung Nodules; Lung Cancers; Early-Stage Lung Cancer; Artificial Intelligence in Radiology; Computer-Aided Detection

Keywords

Lung Nodules; Lung Cancer; Early-Stage Lung Cancer; Chest X-Ray (CXR); Artificial Intelligence (AI); Computer-Aided Detection (CADe); Cancer Screening; FDA-Cleared Device

Outcome Measures

Primary Outcomes (1)

• Difference in Nodule Detection Rate Between Pre- and Post-Deployment Cohorts

  Compare the proportion of patients with lung nodules detected on chest X-rays before and after implementing the AI tool (qXR-LN). Lung nodule detection will be determined by radiological interpretation of chest X-rays. For the pre-deployment cohort, the presence or absence of nodules will be derived from radiology reports and confirmed by a clinical research associate. For the post-deployment cohort, nodules identified by qXR-LN and subsequently reviewed by radiologists (using the qTrack tool) will serve as the primary measure.

  Through study completion, approximately 12 months.

Secondary Outcomes (3)

• Percentage of Lung Cancer Diagnosed Through Nodule Pathway

  Through study completion, approximately 12 months.

• Detection of Early-Stage Lung Cancer

  Through study completion, approximately 12 months.

• Reasons for Dropout from Nodule Clinic Pathway

  Through study completion, approximately 12 months.

Study Arms (2)

Pre-Deployment Cohort

Patients undergoing standard chest X-rays prior to the introduction of the AI-based Computer Aided Detection (CAD) system. This cohort represents the baseline population used for comparison, with no AI intervention applied during their imaging or reporting process.

Post-Deployment Cohort

Patients undergoing chest X-rays after the AI-based Computer Aided Detection (CAD) tool has been integrated into the clinical workflow. Although not assigned as an "intervention group" per a traditional trial protocol, these patients receive imaging evaluated by the AI tool, and the impact on diagnostic outcomes will be compared to the pre-deployment cohort.

Eligibility Criteria

• Age: 35 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population consists of patients aged 35 years and older who have undergone chest X-rays as part of routine clinical care. These patients are evaluated for the presence of lung nodules and potential lung cancer. The study includes two cohorts: a pre-deployment cohort, where chest X-rays are interpreted using standard clinical methods, and a post-deployment cohort, where chest X-rays are interpreted with the assistance of the FDA-cleared AI tool qXR-LN. Patients with a known history of lung cancer or those undergoing lateral chest X-ray views are excluded. The population includes individuals from diverse clinical settings, such as outpatient clinics, emergency departments, and inpatient hospital units, to ensure a representative sample of real-world patients with respiratory conditions. The primary goal is to assess the impact of AI assistance on lung nodule detection and early-stage lung cancer diagnosis.

You may qualify if:

• Age ≥35 years at the time of chest X-ray acquisition
• Chest X-ray must be obtained as part of routine care (e.g., ordered for respiratory complaints, screening, or other clinical indications)
• Chest X-ray performed using CR/DR/DX imaging modality
• Examination described as "Chest"
• View: PA or AP
• Patient positioned as Erect or Supine
• Image available in valid DICOM format with proper DICOM prefix values (including "DICM" in the header)

You may not qualify if:

• Patients aged \<35 years at the time of chest X-ray
• Patients with known lung cancer at the time of chest X-ray acquisition
• Lateral views or any imaging modality other than CR/DR/DX
• Imaging or anatomy not specified as Chest (e.g., different body parts or modalities)

Sponsors & Collaborators

• University of Florida: lead

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 17, 2024
• First Posted: December 24, 2024
• Study Start: May 15, 2025
• Primary Completion: March 15, 2026
• Study Completion (Estimated): June 15, 2026
• Last Updated: September 15, 2025

Record last verified: 2024-12

Data Sharing

• IPD Sharing: Will not share