Pulmonary NodulesNCT07815483
A Multicenter Cohort Study of AI-Based Methods for Pulmonary Nodule Diagnosis and Follow-up
An observational study of Pulmonary Nodules and Lung Neoplasms, run by Shanghai East Hospital.
Trial summary
This is an observational, multicentre study. The primary objective of this study was to evaluate the diagnostic performance and clinical applicability of artificial intelligence models for pulmonary nodule segmentation, benign-malignant risk stratification, and follow-up management. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Pulmonary Nodules, Lung Neoplasms, Artificial Intelligence (AI)
- How many people
- 16,880 participants
- Where
- No sites listed yet
- Study dates
- Oct 2026to Oct 2029
- How long will I take part?
- 12 Months
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Consecutively enrolled, representative patients (aged ≥18 years, with a maximum nodule diameter of ≥3 mm and ≤30 mm) who underwent chest CT for routine clinical care and were found to have pulmonary nodules, including solid, part-solid, and pure ground-glass nodules.
Who may be able to join
- Age ≥18 years; (2) At least one non-calcified pulmonary nodule (diameter ≥3 mm and ≤30 mm) detected on chest CT; (3) Agreement to participate in the study and provision of written informed consent.
- -
Who cannot join
- Poor CT image quality with severe artifacts that preclude AI-based analysis;
- Expected survival <12 months or inability to complete follow-up;
- History of prior ipsilateral lung malignancy or local treatment. -
About this study
Official title: A Prospective, Multicentre Cohort Study of Artificial Intelligence-based Methods for Pulmonary Nodule Segmentation, Benign-Malignant Risk Stratification, and Follow-up
This is an observational, multicentre study. The primary objective of this study was to evaluate the diagnostic performance and clinical applicability of artificial intelligence models for pulmonary nodule segmentation, benign-malignant risk stratification, and follow-up management. We will collect CT images and medical data from participants at several hospitals. Participants will not receive any drugs or medical interventions.
- Keywords
- Pulmonary Nodules, Lung Neoplasms, Diagnostic Imaging, Artificial Intelligence, Deep Learning
- Medical subject headings
- Multiple Pulmonary Nodules, 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 |
|---|---|---|
| Benign Pulmonary Nodule Group | Participants with pulmonary nodules diagnosed as benign, confirmed by either pathological examination or stability on follow-up imaging. No intervention is applied. | |
| Malignant Pulmonary Nodule Group | Participants with pulmonary nodules diagnosed as malignant, confirmed by pathological examination. No intervention is applied. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic performance of the AI model for pulmonary nodule malignancy
Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the AI model for classifying benign and malignant nodules, using pathology results or longitudinal stability as the reference standard.
Measured: Up to 24 months after enrollment
Secondary outcomes (1)
Risk stratification accuracy across different nodule sizes
The ability of the AI model to correctly stratify nodules into low, intermediate, and high-risk categories compared to clinical judgement.
Measured: Up to 24 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Sep 2026
- Study startOct 2026 (estimated)
- Primary completionOct 2028 (estimated)
- Study completionOct 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pulmonary NodulesStudies of the same condition, open and under way.
- 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
- Prospective
- Enrolment
- 16,880 participants (estimated)
- Target duration
- 12 Months
Sponsor and investigators
- Lead sponsor
- Shanghai East Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Shanghai Chest Hospital of Shanghai Jiao Tong University, RenJi Hospital
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07815483
- Sponsor's protocol ID
- 2026YS-265
- Other IDs
- No. DRFC2023015, No. PW2024D-01, No. 202340212, No. 24SF1903903
- Status last verified
- Sep 2026
- First submitted
- 8 Sep 2026
- Last updated
- 11 Sep 2026
- 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 11 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Prospective, Multicentre Cohort Study of Artificial Intelligence-based Methods for Pulmonary Nodule Segmentation, Benign-Malignant Risk Stratification, and Follow-up
- ClinicalTrials.gov ID
- NCT07815483
- Acronym / sponsor ID
- 2026YS-265
- Keywords
- Pulmonary Nodules, Lung Neoplasms, Diagnostic Imaging, Artificial Intelligence, Deep Learning
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 16,880 participants (estimated)
- Lead sponsor
- Shanghai East HospitalUniversity, hospital or other
- Collaborators
- Shanghai Chest Hospital of Shanghai Jiao Tong University, RenJi Hospital
- Study start
- Oct 2026 (estimated)
- Primary completion
- Oct 2028 (estimated)
- Study completion
- Oct 2029 (estimated)
- First posted
- 11 Sep 2026 (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.
This is an observational, multicentre study. The primary objective of this study was to evaluate the diagnostic performance and clinical applicability of artificial intelligence models for pulmonary nodule segmentation, benign-malignant risk stratification, and follow-up management. We will collect CT images and medical data from participants at several hospitals. Participants will not receive any drugs or medical interventions.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 12 Months
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Benign Pulmonary Nodule Group | – | –Participants with pulmonary nodules diagnosed as benign, confirmed by either pathological examination or stability on follow-up imaging. No intervention is applied. |
| Malignant Pulmonary Nodule Group | – | –Participants with pulmonary nodules diagnosed as malignant, confirmed by pathological examination. No intervention is applied. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Consecutively enrolled, representative patients (aged ≥18 years, with a maximum nodule diameter of ≥3 mm and ≤30 mm) who underwent chest CT for routine clinical care and were found to have pulmonary nodules, including solid, part-solid, and pure ground-glass nodules.
Key inclusion criteria (2 listed)
- Age ≥18 years; (2) At least one non-calcified pulmonary nodule (diameter ≥3 mm and ≤30 mm) detected on chest CT; (3) Agreement to participate in the study and provision of written informed consent.
- -
Key exclusion criteria (3 listed)
- Poor CT image quality with severe artifacts that preclude AI-based analysis;
- Expected survival <12 months or inability to complete follow-up;
- History of prior ipsilateral lung malignancy or local treatment. -
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
Diagnostic performance of the AI model for pulmonary nodule malignancy
Time frame: Up to 24 months after enrollment
Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the AI model for classifying benign and malignant nodules, using pathology results or longitudinal stability as the reference standard.
1 secondary outcome
Risk stratification accuracy across different nodule sizes
Time frame: Up to 24 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Sep 2026.
- 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.
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 Pulmonary Nodules and Lung Neoplasms, run by Shanghai East Hospital.
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 2 inclusion and 3 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: opening soon, as of the sponsor's last update on 11 Sep 2026. 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?
This study is still under way. Results are not available in the source record yet.
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?
Diagnostic performance of the AI model for pulmonary nodule malignancy (time frame: Up to 24 months after enrollment). 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 11 Sep 2026.