Clinical trial · Observational
Validating Artificial Intelligence Effectiveness Defined Lung Nodule Malignancy Score in Patients With Pulmonary Nodule.
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.
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Lung Malignancy | Lung Neoplasm | ONTOLOGY_EXACT | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Participant Cohort | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Computed tomography Cohort
- description
- 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.
- interventionNames
- Other: Participant Cohort
Primary outcomes (1)
- measure
- To estimate the positive and negative predictive values of qXR LNMS (lung nodule malignancy score ) in a multi-centre real-world setting.
- timeFrame
- 6 months from the Last subject In.
- description
- 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)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 35 Years
Show eligibility criteria text
Inclusion Criteria: * 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. Exclusion Criteria: * 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.
References
Publications (1)
- DERIVEDKoksal 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 41716936