Non-Small Cell Lung CancerNCT07065422
AI Models for Predicting Occult Pleural Dissemination in NSCLC
An observational study of Non-Small Cell Lung Cancer, run by Daping Hospital and the Research Institute of Surgery of the Third Military Medical University at 1 site in China.
Trial summary
Occult pleural dissemination (PD) in non-small cell lung cancer (NSCLC) patients is likely to be missed on computed tomography (CT) scans, associated with poor survival, and generally contraindicated for radical surgery. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Non-Small Cell Lung Cancer
- How many people
- 326 participants
- Where
- 1 siteChina
- Study dates
- 13 Dec 2023to 1 Jan 2025
- How long will I take part?
- 1 Month
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: From January 2016 to December 2023, NSCLC patients with or without surgically confirmed occult PD from three high-volume centers in China were enrolled.
Who may be able to join
- pathologically confirmed primary NSCLC with malignant pleural dissemination;
- no preoperative treatment;
- clinicopathological data were complete.
Who cannot join
- pleural effusion detected preoperatively;
- preoperatively diagnosed with PD;
- poor CT quality or no CT scans within 1 month before surgery.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Comparing Radiomics, Deep Learning, and Fusion Models for Predicting Occult Pleural Dissemination in Patients With Non-small Cell Lung Cancer
Occult pleural dissemination (PD) in non-small cell lung cancer (NSCLC) patients is likely to be missed on computed tomography (CT) scans, associated with poor survival, and generally contraindicated for radical surgery. This study aimed to develop and compare the performance of radiomics-based machine learning (ML), deep learning (DL), and fusion models to preoperatively identify occult PDs in NSCLC patients. Patients from three Chinese high-volume medical centers (2016-2023) were retrospectively collected and divided into training, internal test, and external test cohorts. Ten radiomics-based ML models and eight DL models were trained using CT plain scan images at the maximum cross-sectional areas of the primary tumor. Moreover, another two fusion models (prefusion and postfusion) were developed using feature-based and decision-based methods. The receiver operating characteristic curve (ROC) and area under the curve (AUC) were mainly used to compare the predictive performance of the models.
- Conditions
- Non-Small Cell Lung Cancer
- Medical subject headings
- Carcinoma, Non-Small-Cell Lung
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 |
|---|---|---|
| non-small cell lung cancer (NSCLC) patients with or without occult pleural dissemination. | Not described |
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)
Measured: through study completion, an average of 6 months.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Jul 2025
- Study start13 Dec 2023
- Primary completion1 Jan 2025
- Study completion1 Jan 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Non-Small Cell Lung CancerStudies 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
- Cross-sectional
- Enrolment
- 326 participants (actual)
- Target duration
- 1 Month
Sponsor and investigators
- Lead sponsor
- Daping Hospital and the Research Institute of Surgery of the Third Military Medical University
- Sponsor type
- University, hospital or other
- Collaborators
- First Affiliated Hospital of Chongqing Medical University, Xinqiao hospital of the third military medical university
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07065422
- Sponsor's protocol ID
- GUOW
- Status last verified
- May 2025
- First submitted
- 3 Jul 2025
- Last updated
- 6 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Study at a glance
From the registry record, last updated by the sponsor on 6 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparing Radiomics, Deep Learning, and Fusion Models for Predicting Occult Pleural Dissemination in Patients With Non-small Cell Lung Cancer
- ClinicalTrials.gov ID
- NCT07065422
- Acronym / sponsor ID
- GUOW
- Conditions
- Non-Small Cell Lung Cancer
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 326 participants (actual)
- Lead sponsor
- Daping Hospital and the Research Institute of Surgery of the Third Military Medical UniversityUniversity, hospital or other
- Collaborators
- First Affiliated Hospital of Chongqing Medical University, Xinqiao hospital of the third military medical university
- Study start
- 13 Dec 2023 (actual)
- Primary completion
- 1 Jan 2025 (actual)
- Study completion
- 1 Jan 2025 (actual)
- First posted
- 15 Jul 2025 (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.
Occult pleural dissemination (PD) in non-small cell lung cancer (NSCLC) patients is likely to be missed on computed tomography (CT) scans, associated with poor survival, and generally contraindicated for radical surgery. This study aimed to develop and compare the performance of radiomics-based machine learning (ML), deep learning (DL), and fusion models to preoperatively identify occult PDs in NSCLC patients. Patients from three Chinese high-volume medical centers (2016-2023) were retrospectively collected and divided into training, internal test, and external test cohorts. Ten radiomics-based ML models and eight DL models were trained using CT plain scan images at the maximum cross-sectional areas of the primary tumor. Moreover, another two fusion models (prefusion and postfusion) were developed using feature-based and decision-based methods. The receiver operating characteristic curve (ROC) and area under the curve (AUC) were mainly used to compare the predictive performance of the models.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Target follow-up
- 1 Month
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| non-small cell lung cancer (NSCLC) patients with or without occult pleural dissemination. | – | – |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- From January 2016 to December 2023, NSCLC patients with or without surgically confirmed occult PD from three high-volume centers in China were enrolled.
Key inclusion criteria (3 listed)
- pathologically confirmed primary NSCLC with malignant pleural dissemination;
- no preoperative treatment;
- clinicopathological data were complete.
Key exclusion criteria (3 listed)
- pleural effusion detected preoperatively;
- preoperatively diagnosed with PD;
- poor CT quality or no CT scans within 1 month before surgery.
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
The area under the receiver operating characteristic curve (AUC)
Time frame: through study completion, an average of 6 months.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Aug 2025.
- Individual participant dataSharing planned: Yes. The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Related trials for Non-Small Cell Lung Cancer
Active, not recruitingPhase 1/2
A Beta-only IL-2 ImmunoTherapY Study
Advanced Solid Tumor, Unresectable Solid Tumor
RecruitingPhase 2/3
Phase 2 Trial of Adagrasib Monotherapy and in Combination With Pembrolizumab and a Phase 3 Trial of Adagrasib in Combination in Patients With a KRAS G12C Mutation KRYSTAL-7
Advanced Non-Small Cell Lung Cancer, Metastatic Non-Small Cell Lung Cancer
RecruitingPhase 3
A Clinical Trial of Adjuvant Intismeran (V940) With or Without Pembrolizumab Coformulated With Berahyaluronidase Alfa (MK-3475A) in High-Risk Stage I Non-Small Cell Lung Cancer (V940-014)
Non-small Cell Lung Cancer
Recruiting
Understanding the Feasibility of a Co-designed Pre-radiotherapy Exercise Programme - A Single-arm Study
Lung Cancer
RecruitingPhase 2
Adjuvant ctDNA-Adapted Personalized Treatment in Early Stage NSCLC (ADAPT-E)
Non-small Cell Lung Cancer, Non-small Cell Lung Cancer Stage I
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.
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 Non-Small Cell Lung Cancer, run by Daping Hospital and the Research Institute of Surgery of the Third Military Medical University 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 3 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: finished, as of the sponsor's last update on 6 Aug 2025. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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) (time frame: through study completion, an average of 6 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 6 Aug 2025.