Clinical trial NCT06746324: Assessing AI for Detecting Lung Nodules and Cancer: Pre- and Post-Deployment Study
Official title: 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
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
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
Lead sponsor: University of Florida
Sponsor class: OTHER
First posted: 2024-12-24
Enrollment: 200000
Sex: ALL
Minimum age (years): 35
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: OTHER
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.

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.

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.

Eligibility criteria: 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)

Primary outcomes:
measure: Difference in Nodule Detection Rate Between Pre- and Post-Deployment Cohorts
timeFrame: Through study completion, approximately 12 months.
Secondary outcomes:
measure: Percentage of Lung Cancer Diagnosed Through Nodule Pathway
timeFrame: Through study completion, approximately 12 months.
measure: Detection of Early-Stage Lung Cancer
timeFrame: Through study completion, approximately 12 months.
measure: Reasons for Dropout from Nodule Clinic Pathway
timeFrame: Through study completion, approximately 12 months.
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