Pulmonary NodulesNCT07895147
Clinical Application of Artificial Intelligence Methods in Diagnostic Imaging of Pulmonary Nodules
An observational study of Pulmonary Nodules and Lung Neoplasms, run by Shanghai East Hospital at 1 site in China.
Trial summary
This is a retrospective, multicenter observational study evaluating the clinical value of combining high-resolution (1024×1024 matrix) targeted CT scans with an artificial intelligence (AI) deep learning system to distinguish between benign and malignant pulmonary nodules. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Pulmonary Nodules, Lung Neoplasms, Lung Diseases
- How many people
- 10,000 participants
- Where
- 1 siteChina
- Study dates
- 8 Jan 2026to 18 Dec 2026
- How long will I take part?
- 1 Year
- 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: This study population consists of adult patients (aged ≥ 18 years) who underwent chest high-resolution targeted CT scans at Shanghai East Hospital (lead site), Shanghai Renji Hospital, or Shanghai Chest Hospital between January 1, 2015, and January 1, 2025. Participants were retrospectively identified via the hospital PACS system using keywords such as "pulmonary nodule" and "targeted CT scan." Eligible patients had pulmonary nodules (diameter ≥ 3 mm and ≤ 30 mm) with complete imaging data, including scan slice thickness 1 mm, and images with a 1024×1024 scan matrix. The diagnosis of benign or malignant status was confirmed by surgical resection pathology (gold standard) or at least 12 months of clinical follow-up (stability/shrinkage for benign; progression/growth for malignant). The planned enrollment is approximately 10,000 cases, comprising roughly 7,000 malignant and 3,000 benign nodules, with a 1:1 ratio estimation targeted for statistical power.
Who may be able to join
- Age ≥ 18 years.
- Pulmonary nodules (diameter ≥ 3 mm and ≤ 30 mm) detected by high-resolution targeted CT scan.
- Confirmed pathological diagnosis benign or malignant (gold standard).
- Complete imaging data, including scan slice thickness 1 mm, and images with a 1024×1024 scan matrix.
Who cannot join
- Severe motion artifacts or noise on CT images affecting image quality and diagnostic assessment.
- History of ipsilateral lung malignancy or prior local treatments (e.g., radiotherapy, ablation) to the lung.
- Pregnant or lactating women.
- Incomplete clinical data or loss to follow-up.
Trial locations and contacts
1 site in 1 country.
China1 site
Shanghai East Hospital (East Hospital Affiliated to Tongji University)
Shanghai
About this study
This is a retrospective, multicenter observational study evaluating the clinical value of combining high-resolution (1024×1024 matrix) targeted CT scans with an artificial intelligence (AI) deep learning system to distinguish between benign and malignant pulmonary nodules. By analyzing existing medical records and CT images of adult patients (aged 18 and older) with pulmonary nodules (3-30 mm) who have confirmed diagnoses (via postoperative pathology）, this study aims to assess the diagnostic accuracy and efficiency of the AI system. The study plans to enroll approximately 10,000 cases from three hospitals in Shanghai. The primary goal is to evaluate the AI system's diagnostic performance (such as sensitivity, specificity, and Area Under the ROC Curve) and compare it with the performance of radiologists, ultimately exploring whether this combined approach can provide more accurate clinical diagnoses and reduce unnecessary procedures.
- Conditions
- Pulmonary Nodules, Lung Neoplasms, Lung Diseases
- Being tested
- No intervention
- Keywords
- Artificial Intelligence, Deep Learning, Pulmonary Nodules, Lung Cancer, Ultra-High-Resolution CT, 1024x1024 Matrix, Diagnostic Imaging, Retrospective Study
- Medical subject headings
- Multiple Pulmonary Nodules, Lung Neoplasms, Lung Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventionOther
This is a retrospective observational study. No intervention was administered. The AI-assisted diagnostic system was used as an observational assessment tool.
What is being measured
The primary outcome is the main question the study is designed to answer.
The Area Under the Receiver Operating Characteristic Curve (AUC) for AI Model Diagnosis of Pulmonary Nodules
Using pathological results as the gold standard, calculate the area under the operating characteristic curve (AUC) of the AI model for diagnosing pulmonary nodules at the nodule level, and report the 95% confidence interval. If there are multiple nodules in the same patient, adjust according to patient clustering.
Measured: Baseline to 1 month after obtaining pathological results
Secondary outcomes (4)
Sensitivity of AI model in diagnosing pulmonary nodules
Using pathological results as the gold standard, calculate the sensitivity of the AI model in diagnosing pulmonary nodules, defined as the true positive number/(true positive number+false negative number), and summarize it in proportion and 95% confidence interval.
Measured: Baseline to 1 month after obtaining pathological results
Specificity of artificial intelligence models in diagnosing pulmonary nodules
Using pathological results as the gold standard, calculate the specificity of the AI model in diagnosing pulmonary nodules, defined as the true negative number/(true negative number+false positive number), and summarize it in proportion and 95% confidence interval.
Measured: Baseline to 1 month after obtaining pathological results
Consistency between AI and Radiologists (Kappa)
Agreement in diagnosing benign and malignant pulmonary nodules between the AI and radiologists, measured by Kappa statistics.
Measured: Assessed during the data analysis phase (September 2026 to December 2026)
Diagnostic Time
The time taken by radiologists to review the CT images and provide a diagnosis.
Measured: Assessed during the data analysis phase (September 2026 to December 2026)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Oct 2026
- Study start8 Jan 2026
- Primary completion6 Oct 2026
- Study completion18 Dec 2026 (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
- Other
- Enrolment
- 10,000 participants (estimated)
- Target duration
- 1 Year
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
- Investigators
- Qing Lu, M.D., Ph.D., Principal investigator, Shanghai East Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07895147
- Sponsor's protocol ID
- 2026YS-001
- Other IDs
- No. 202340212, No. 24SF1903903, No. PW2024D-01, No. DRFC2023015
- Status last verified
- Oct 2026
- First submitted
- 13 Oct 2026
- Last updated
- 26 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Given the retrospective observational nature of this study, the ethics committee approved a waiver of informed consent. To strictly protect patient privacy and comply with institutional data security policies, individual participant data will not be shared with external researchers.
Study at a glance
From the registry record, last updated by the sponsor on 26 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Application of Artificial Intelligence Methods in Diagnostic Imaging of Pulmonary Nodules
- ClinicalTrials.gov ID
- NCT07895147
- Acronym / sponsor ID
- 2026YS-001
- Conditions
- Pulmonary Nodules, Lung Neoplasms, Lung Diseases
- Keywords
- Artificial Intelligence, Deep Learning, Pulmonary Nodules, Lung Cancer, Ultra-High-Resolution CT, 1024x1024 Matrix, Diagnostic Imaging, Retrospective Study
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- Shanghai East HospitalUniversity, hospital or other
- Collaborators
- Shanghai Chest Hospital of Shanghai Jiao Tong University, RenJi Hospital
- Study start
- 8 Jan 2026 (actual)
- Primary completion
- 6 Oct 2026 (actual)
- Study completion
- 18 Dec 2026 (estimated)
- First posted
- 26 Oct 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 a retrospective, multicenter observational study evaluating the clinical value of combining high-resolution (1024×1024 matrix) targeted CT scans with an artificial intelligence (AI) deep learning system to distinguish between benign and malignant pulmonary nodules. By analyzing existing medical records and CT images of adult patients (aged 18 and older) with pulmonary nodules (3-30 mm) who have confirmed diagnoses (via postoperative pathology）, this study aims to assess the diagnostic accuracy and efficiency of the AI system. The study plans to enroll approximately 10,000 cases from three hospitals in Shanghai. The primary goal is to evaluate the AI system's diagnostic performance (such as sensitivity, specificity, and Area Under the ROC Curve) and compare it with the performance of radiologists, ultimately exploring whether this combined approach can provide more accurate clinical diagnoses and reduce unnecessary procedures.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 1 Year
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This study population consists of adult patients (aged ≥ 18 years) who underwent chest high-resolution targeted CT scans at Shanghai East Hospital (lead site), Shanghai Renji Hospital, or Shanghai Chest Hospital between January 1, 2015, and January 1, 2025. Participants were retrospectively identified via the hospital PACS system using keywords such as "pulmonary nodule" and "targeted CT scan." Eligible patients had pulmonary nodules (diameter ≥ 3 mm and ≤ 30 mm) with complete imaging data, including scan slice thickness 1 mm, and images with a 1024×1024 scan matrix. The diagnosis of benign or malignant status was confirmed by surgical resection pathology (gold standard) or at least 12 months of clinical follow-up (stability/shrinkage for benign; progression/growth for malignant). The planned enrollment is approximately 10,000 cases, comprising roughly 7,000 malignant and 3,000 benign nodules, with a 1:1 ratio estimation targeted for statistical power.
Key inclusion criteria (4 listed)
- Age ≥ 18 years.
- Pulmonary nodules (diameter ≥ 3 mm and ≤ 30 mm) detected by high-resolution targeted CT scan.
- Confirmed pathological diagnosis benign or malignant (gold standard).
- Complete imaging data, including scan slice thickness 1 mm, and images with a 1024×1024 scan matrix.
Key exclusion criteria (4 listed)
- Severe motion artifacts or noise on CT images affecting image quality and diagnostic assessment.
- History of ipsilateral lung malignancy or prior local treatments (e.g., radiotherapy, ablation) to the lung.
- Pregnant or lactating women.
- Incomplete clinical data or loss to follow-up.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No interventionThis is a retrospective observational study. No intervention was administered. The AI-assisted diagnostic system was used as an observational assessment tool. | Other | – |
Primary outcome
The Area Under the Receiver Operating Characteristic Curve (AUC) for AI Model Diagnosis of Pulmonary Nodules
Time frame: Baseline to 1 month after obtaining pathological results
Using pathological results as the gold standard, calculate the area under the operating characteristic curve (AUC) of the AI model for diagnosing pulmonary nodules at the nodule level, and report the 95% confidence interval. If there are multiple nodules in the same patient, adjust according to patient clustering.
4 secondary outcomes
Sensitivity of AI model in diagnosing pulmonary nodules
Time frame: Baseline to 1 month after obtaining pathological results
Specificity of artificial intelligence models in diagnosing pulmonary nodules
Time frame: Baseline to 1 month after obtaining pathological results
Consistency between AI and Radiologists (Kappa)
Time frame: Assessed during the data analysis phase (September 2026 to December 2026)
Diagnostic Time
Time frame: Assessed during the data analysis phase (September 2026 to December 2026)
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 26 Oct 2026.
- Individual participant dataSharing planned: No. Given the retrospective observational nature of this study, the ethics committee approved a waiver of informed consent. To strictly protect patient privacy and comply with institutional data security policies, individual participant data will not be shared with external researchers.
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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.
Qing Lu, M.D., Ph.D. principal investigator
Shanghai East Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pulmonary Nodules and Lung Neoplasms, run by Shanghai East Hospital at 1 site in China.
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 4 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: under way, not looking for participants, as of the sponsor's last update on 26 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
The Area Under the Receiver Operating Characteristic Curve (AUC) for AI Model Diagnosis of Pulmonary Nodules (time frame: Baseline to 1 month after obtaining pathological results). 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 26 Oct 2026.