Lung NodulesAI-LungNCT06746324
Assessing AI for Detecting Lung Nodules and Cancer: Pre- and Post-Deployment Study
An observational study of Lung Nodules and Lung Cancers, run by University of Florida.
Trial 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. Read more about this study
- Who is it for?
- Ages 35 years and older
- Conditions the study is for
- Lung Nodules, Lung Cancers, Early-Stage Lung Cancer
- How many people
- 0 participants
- Where
- No sites listed yet
- Study dates
- 15 May 2025to 15 Jun 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 35 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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)
Who cannot join
- 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)
About this 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
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.
Read the 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.
- 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
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Through study completion, approximately 12 months.
Secondary outcomes (3)
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).
Measured: 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.
Measured: 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.
Measured: Through study completion, approximately 12 months.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Dec 2024
- Study start15 May 2025 (estimated)
- Primary completion15 Mar 2026 (estimated)
- Study completion15 Jun 2026 (estimated)
Why it stopped: No IRB approval
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung NodulesStudies of the same condition, open and under way.
- More trials run by University of FloridaThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Florida
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06746324
- Sponsor's protocol ID
- qXR-LN-UFL-001
- Acronym
- AI-Lung
- Status last verified
- Dec 2024
- First submitted
- 17 Dec 2024
- Last updated
- 15 Sep 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 15 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT06746324
- Acronym / sponsor ID
- AI-Lung / qXR-LN-UFL-001
- 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
- Study type
- Observational
- Recruitment status
- WithdrawnReason stopped: No IRB approval
- Enrolment
- 0 participants (actual)
- Lead sponsor
- University of FloridaUniversity, hospital or other
- Study start
- 15 May 2025 (estimated)
- Primary completion
- 15 Mar 2026 (estimated)
- Study completion
- 15 Jun 2026 (estimated)
- First posted
- 24 Dec 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 35 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (7 listed)
- 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
Key exclusion criteria (4 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Difference in Nodule Detection Rate Between Pre- and Post-Deployment Cohorts
Time frame: Through study completion, approximately 12 months.
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.
3 secondary outcomes
Percentage of Lung Cancer Diagnosed Through Nodule Pathway
Time frame: Through study completion, approximately 12 months.
Detection of Early-Stage Lung Cancer
Time frame: Through study completion, approximately 12 months.
Reasons for Dropout from Nodule Clinic Pathway
Time frame: Through study completion, approximately 12 months.
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Sep 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Nodules and Lung Cancers, run by University of Florida.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: withdrawn, as of the sponsor's last update on 15 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Difference in Nodule Detection Rate Between Pre- and Post-Deployment Cohorts (time frame: Through study completion, approximately 12 months.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Sep 2025.