Validating Artificial Intelligence Effectiveness Defined Lung Nodule Malignancy Score in Patients With Pulmonary Nodule.
CREATE
Prospective Realworld Cohort Study to Validate Effectiveness of an Artificial Intelligence Defined Lung Nodule Malignancy Score in Patients With Pulmonary Nodule Multicentric, Multinational, Prospective, Observational Study.
1 other identifier
observational
712
5 countries
22
Brief Summary
Artificial intelligence (AI) based algorithms have demonstrated increased accuracy in predicting the risk of Lung Cancer among patients with an incidental pulmonary nodule (IPN) on chest radiographs. Qure.ai, an AI company specializing in the reading of chest X- Rays (CXRs) by a proprietary algorithm and has developed a new model, qXR, that can report the lung nodule malignancy score (LNMS) based on lung nodule features. Our study aims to prospectively validate the lung nodule malignancy score against radiologist assessment of CT scans and Lung CT Screening Reporting and Data System score (Lung-RADS).(lung RADS score explained below) Thus, lung nodule malignancy score (interpreted by qXR as a high or low category) will be compared with radiologist-based assessment probability of CT scan and Lung-RADS assessment. The results of this prospective observational study will pave the way for improved nodule management, leading to better clinical outcomes in patients with incidental pulmonary nodule (IPNs), especially concerning malignancy assessment.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Apr 2023
Typical duration for all trials
22 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
March 14, 2023
CompletedFirst Posted
Study publicly available on registry
April 18, 2023
CompletedStudy Start
First participant enrolled
April 20, 2023
CompletedPrimary Completion
Last participant's last visit for primary outcome
December 6, 2026
ExpectedStudy Completion
Last participant's last visit for all outcomes
December 6, 2026
April 16, 2026
April 1, 2026
3.6 years
March 14, 2023
April 15, 2026
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
To estimate the positive and negative predictive values of qXR LNMS (lung nodule malignancy score ) in a multi-centre real-world setting.
PPV( positive predictive value) of qXR-LNMS using a panel of radiologists assigning high-risk based on CT (done within 180 days from Xray) as the reference standard The PPV here is the number of nodules rated as high risk as assessed by a reference standard (a panel of radiologists) on CT divided by the total number of high-risk nodules as reported by qXR-LNMS (lung nodule malignancy score ) (n = 500) NPV( negative predictive value) of qXR-LNMS using panel of radiologists assigning low-risk based on CT (done within 180days from X-ray) as reference standard. The NPV here is number of nodules rated as low risk as assessed by a reference standard (a panel of radiologists) on CT divided by total number of low-risk nodules as reported by qXR-LNMS (lung nodule malignancy score ) (n = 200)
6 months from the Last subject In.
Secondary Outcomes (1)
Demographic and clinical factors associated with high or low predictive values association of qXR LNMS with the Mayo score model.
2 and half years from Last subject In.
Study Arms (1)
Computed tomography Cohort
In case of any nodule detection by qXR, it will be classified either as low-risk LNMS(lung nodule malignancy score ) or high-risk LNMS confirmed by radiologist. The patient will be requested to get a CT scan after enrolment in the study.
Interventions
Patients coming to the facility for chest x-rays for any reason, will undergo x-rays as ordered by their treating clinician. In case of any nodule detection by qXR, it will be classified either as low-risk (Lung nodule malignancy score ) LNMS or high-risk LNMS confirmed by radiologist. Then if patient is eligible will be included in the study and a CT Scan will be requested upon enrolment of the patient.
Eligibility Criteria
Adults patient (35years or more) coming for chest X-ray at the institution.
You may qualify if:
- Male or female patients aged \>35 years
- Patients diagnosed with incidental pulmonary nodule (IPN) on CXR (chest x-ray) by qXR and confirmed by the radiologist at the site with nodule size ≥8 and ≤30 mm.
You may not qualify if:
- Any medical or other contraindications for a CT scan
- Nondigital (chest x-ray)CXR
- CT scan is done more than 6 months after (chest x-ray) CXR
- Patients with already diagnosed lung cancer
- The patients referred for an X-Ray for a suspicious Lung cancer
- A patient who already participated in the study.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- AstraZenecalead
Study Sites (22)
Research Site
Alexandria, Egypt
Research Site
Cairo, Egypt
Research Site
Gurugram, Haryana, 122001, India
Research Site
New Delhi, National Capital Territory of Delhi, 110005, India
Research Site
New Delhi, National Capital Territory of Delhi, 110017, India
Research Site
New Delhi, National Capital Territory of Delhi, 201012, India
Research Site
Hyderabad, Telangana, 500003, India
Research Site
Bangalore, 560011, India
Research Site
Bangalore, 560038, India
Research Site
Chennai, 600026, India
Research Site
Chennai, 600040, India
Research Site
Chennai, 600045, India
Research Site
Chennai, 600053, India
Research Site
Chennai, 600116, India
Research Site
Kolkata, 700025, India
Research Site
Mumbai, 400069, India
Research Site
Pune, 411011, India
Research Site
Surabaya, 60286, Indonesia
Research Site
Monterrey, Nuevo León, 64460, Mexico
Research Site
Mexico City, 6720, Mexico
Research Site
Ankara, Turkey, 06100, Turkey (Türkiye)
Research Site
Mersin, Turkey, 33010, Turkey (Türkiye)
Related Publications (1)
Koksal D, Govindarajan A, Gonuguntla HK, Nayci S, Cordova R, Zidan MH, McCutcheon S, Saha A, Kantharaju P, Sen S, Agrawal R, Wulandari L. Evaluation of an Artificial Intelligence Defined Lung Nodule Malignancy Score in Incidental Pulmonary Nodules: The CREATE Study. Mayo Clin Proc Digit Health. 2026 Jan 19;4(1):100335. doi: 10.1016/j.mcpdig.2026.100335. eCollection 2026 Mar.
PMID: 41716936DERIVED
Biospecimen
We are not collecting any biospecimen sample.
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- INDUSTRY
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
March 14, 2023
First Posted
April 18, 2023
Study Start
April 20, 2023
Primary Completion (Estimated)
December 6, 2026
Study Completion (Estimated)
December 6, 2026
Last Updated
April 16, 2026
Record last verified: 2026-04
Data Sharing
- IPD Sharing
- Will share
- Time Frame
- AstraZeneca will meet or exceed data availability as per the commitments made to the EFPIA Pharma Data Sharing Principles. For details of our timelines, please rerefer to our disclosure commitment at https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure
- Access Criteria
- When a request has been approved AstraZeneca will provide access to the deidentified individual patient-level data in an approved sponsored tool . Signed Data Sharing Agreement (non-negotiable contract for data accessors) must be in place before accessing requested information. Additionally, all users will need to accept the terms and conditions of the SAS MSE to gain access. For additional details, please review the Disclosure Statements at https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure.
Qualified researchers can request access to anonymized individual patient-level data from AstraZeneca group of companies sponsored clinical trials via the request portal. All request will be evaluated as per the AZ disclosure commitment: https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure. Yes, indicates that AZ are accepting requests for IPD, but this does not mean all requests will be shared.