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

An Ambispective Pre and Post Deployment Observational Cohort Study to Evaluate the Yield of Actionable Lung Nodules and Lung Cancer Through Chest X-Rays Using Artificial Intelligence

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.

Study Overview

• Status: Withdrawn
• Conditions: Lung Nodules; Lung Cancers; Early-Stage Lung Cancer; Artificial Intelligence in Radiology; Computer-Aided Detection

Detailed Description

This study evaluates the clinical impact of the FDA-cleared artificial intelligence (AI) tool, qXR-LN, for detecting lung nodules and diagnosing lung cancer using chest X-rays (CXR). The study employs an ambispective observational cohort design with two cohorts: pre-deployment (before AI implementation) and post-deployment (after AI implementation).

The primary objective is to assess differences in lung nodule detection rates and the percentage of lung cancers diagnosed through the nodule pathway between the two cohorts. Secondary objectives include evaluating whether the AI tool aids in detecting more early-stage lung cancers and identifying reasons for patients dropping out of the nodule clinic pathway.

In the post-deployment cohort, qXR-LN integrates seamlessly with the hospital's existing systems to provide real-time AI findings on radiologists' workstations. Radiologists can accept or reject AI suggestions, ensuring that the final decisions remain under human supervision. Data from both cohorts, including patient demographics, nodule detection rates, cancer diagnoses, and treatment outcomes, will be collected and analyzed.

The study excludes patients under 35 years old and those with known lung cancer at the time of imaging. Ethical considerations include obtaining waivers of consent where applicable and ensuring minimal risk to participants. The findings of this study aim to inform clinical practices and enhance the use of AI tools in lung cancer screening and diagnosis.

• Study Type: Observational

Contacts and Locations

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

Inclusion Criteria:

• 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)

Exclusion Criteria:

• 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)

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Percentage of Lung Cancer Diagnosed Through Nodule Pathway	Assess the difference in the percentage of lung cancer cases diagnosed via the nodule pathway in pre- and post-deployment cohorts. Lung cancer diagnosis and staging will be confirmed through pathology reports (biopsy results) and/or imaging follow-up (CT/PET), as documented in the electronic health record (EHR) and the Radiology Information System (RIS).	Through study completion, approximately 12 months.
Detection of Early-Stage Lung Cancer	Compare the proportion of early-stage (Stage I and Stage II) lung cancer diagnoses between pre- and post-deployment cohorts. Staging will be obtained from the pathology report, imaging studies (CT scans), and clinical documentation in the EHR. Established TNM (Tumor, Node, Metastasis) classification guidelines will be used for determining cancer stage.	Through study completion, approximately 12 months.
Reasons for Dropout from Nodule Clinic Pathway	Summarize and analyze reasons for patients not completing the nodule clinic pathway in the post-deployment cohort. Reasons for dropout will be extracted from patient records, clinical notes, follow-up logs, and administrative records. These data may include documentation of patient contact attempts, scheduling records, and physician or nurse notes indicating patient-reported reasons for not completing the pathway.	Through study completion, approximately 12 months.

Collaborators and Investigators

• Sponsor: University of Florida

Study record dates

Study Major Dates

• Study Start (Estimated): May 15, 2025
• Primary Completion (Estimated): March 15, 2026
• Study Completion (Estimated): June 15, 2026

Study Registration Dates

• First Submitted: December 17, 2024
• First Submitted That Met QC Criteria: December 19, 2024
• First Posted (Actual): December 24, 2024

Study Record Updates

• Last Update Posted (Estimated): September 15, 2025
• Last Update Submitted That Met QC Criteria: September 8, 2025
• Last Verified: December 1, 2024

More Information

Terms related to this study

• Keywords: Lung Cancer; Cancer Screening; Artificial Intelligence (AI); Early-Stage Lung Cancer; Lung Nodules; Chest X-Ray (CXR); Computer-Aided Detection (CADe); FDA-Cleared Device
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Lung Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• Other Study ID Numbers: qXR-LN-UFL-001

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
• product manufactured in and exported from the U.S.: No