NSCLC (Non-small Cell Lung Cancer)NCT06684418
Artificial Intelligence-based Model for the Prediction of Occult Lymph Node Metastasis and Improvement of Clinical Decision-making in Non-small Cell Lung Cancer
An observational study of NSCLC (Non-small Cell Lung Cancer) and Artificial Intelligence (AI), run by Fudan University at 1 site in China.
Trial summary
This nationwide, multicenter observational study aims to develop and validate a multimodal artificial intelligence (AI) model for detecting occult lymph node metastasis in early-stage non-small cell lung cancer (NSCLC) patients. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- NSCLC (Non-small Cell Lung Cancer), Artificial Intelligence (AI), Lymphnode Metastasis
- What is being tested?
- chest enhanced CT
- How many people
- 6,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Dec 2024to 30 Jun 2026
- 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: Early-stage NSCLC receiving curative treatment (surgery or SBRT).
Who may be able to join
- Pathologically confirmed non-small cell lung cancer;
- Clinical stage I (AJCC, 8th edition, 2017);
- Age≥18 years old;
- KPS score≥70;
- Patients who have undergone primary NSCLC radical surgery or SBRT treatment;
- Complete systemic lesion imaging assessment before primary NSCLC radical surgery or SBRT treatment (Note: Tumor size ≥ 3 cm or centrally located tumor requires PET/CT and/or invasive mediastinal staging);
- Patients willing to cooperate with the follow-up after primary NSCLC radical surgery;
- informed consent of the patient.
Who cannot join
- Poor quality of computed tomography imaging;
- Baseline imaging shows pure ground-glass nodules (GGO);
- Uncontrolled epilepsy, central nervous system disease, or history of mental disorders, judged by the researcher to potentially interfere with the signing of the informed consent form or affect patient compliance.;
- Loss to follow-up.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Fudan university Shanghai Cancer Center
Shanghai
Zhengfei Zhu, PhD, 18017312901, fuscczzf@163.com
About this study
Official title: Artificial Intelligence-based Model for the Prediction of Occult Lymph Node Metastasis and Improvement of Clinical Decision-making in Non-small Cell Lung Cancer: A Multicenter, Prospective, Observational Study
This nationwide, multicenter observational study aims to develop and validate a multimodal artificial intelligence (AI) model for detecting occult lymph node metastasis in early-stage non-small cell lung cancer (NSCLC) patients. Despite advances in lymph node staging, 12.9%-39.3% of occult nodal metastasis cases remain undetected preoperatively, affecting treatment decisions. This study will use deep learning to extract imaging features of occult metastasis and combine them with clinical data to build an AI model for risk prediction. This study will provide insights into the feasibility of AI-driven detection of occult metastasis, supporting clinical decision-making and potentially revealing underlying biological mechanisms of lymph node metastasis in NSCLC.
- Being tested
- chest enhanced CT
- 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.
chest enhanced CTDiagnostic test
This is an observational study and patients will receive routine clinical treatment according to the corresponding guidelines. We will collect the enrolled patient's chest enhanced CT and clinicopathological parameters.
| Study group | Role | What they receive |
|---|---|---|
| Retrospective Cohort | Enrolling about 5,000 early-stage NSCLC patients from January 2018 to June 2024 across 25 centers in China, data including chest CT scans and clinicopathological parameters will be used to train and validate the AI model. Patients will be divided into "high-risk" and "low-risk" groups based on the model's risk score, and clinical benefits of treatments like lymph node dissection, adjuvant therapy, and SBRT will be analyzed. Receives: Diagnostic Test: chest enhanced CT | |
| Prospective Cohort | Enrolling 1,000 patients from November 2024 to October 2025, this cohort will prospectively validate the AI model's performance and explore the biological basis of metastasis by analyzing pathological tissues, RNA sequencing, and tumor immune microenvironment characteristics. Receives: Diagnostic Test: chest enhanced CT |
What is being measured
The primary outcome is the main question the study is designed to answer.
Recurrence-free survival (RFS)
The time from surgical treatment or SBRT to disease recurrence or death. Patients who were still not progressing at the time of analysis will have the date of their last contact as the cutoff date.
Measured: 1 year
Secondary outcomes (1)
Overall Survival (OS)
The time from the surgery or SBRT until death from any cause. Patients who are still alive at the time of analysis will have their last contact date used as the cutoff date.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Nov 2024
- Study start1 Dec 2024
- Primary completion1 Dec 2025 (estimated)
- Study completion30 Jun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for NSCLC (Non-small Cell Lung Cancer)Studies of the same condition, open and under way.
- More trials run by Fudan UniversityThe sponsor's other registered studies.
- 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
- 6,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Fudan University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Zhengfei Zhu, Fudan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06684418
- Sponsor's protocol ID
- OLNM-AI
- Status last verified
- Jan 2025
- First submitted
- 11 Nov 2024
- Last updated
- 20 Jan 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 20 Jan 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-based Model for the Prediction of Occult Lymph Node Metastasis and Improvement of Clinical Decision-making in Non-small Cell Lung Cancer: A Multicenter, Prospective, Observational Study
- ClinicalTrials.gov ID
- NCT06684418
- Acronym / sponsor ID
- OLNM-AI
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 6,000 participants (estimated)
- Lead sponsor
- Fudan UniversityUniversity, hospital or other
- Study start
- 1 Dec 2024 (actual)
- Primary completion
- 1 Dec 2025 (estimated)
- Study completion
- 30 Jun 2026 (estimated)
- First posted
- 12 Nov 2024 (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 nationwide, multicenter observational study aims to develop and validate a multimodal artificial intelligence (AI) model for detecting occult lymph node metastasis in early-stage non-small cell lung cancer (NSCLC) patients. Despite advances in lymph node staging, 12.9%-39.3% of occult nodal metastasis cases remain undetected preoperatively, affecting treatment decisions. This study will use deep learning to extract imaging features of occult metastasis and combine them with clinical data to build an AI model for risk prediction. This study will provide insights into the feasibility of AI-driven detection of occult metastasis, supporting clinical decision-making and potentially revealing underlying biological mechanisms of lymph node metastasis in NSCLC.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Retrospective Cohort | – | Diagnostic Test: chest enhanced CTEnrolling about 5,000 early-stage NSCLC patients from January 2018 to June 2024 across 25 centers in China, data including chest CT scans and clinicopathological parameters will be used to train and validate the AI model. Patients will be divided into "high-risk" and "low-risk" groups based on the model's risk score, and clinical benefits of treatments like lymph node dissection, adjuvant therapy, and SBRT will be analyzed. |
| Prospective Cohort | – | Diagnostic Test: chest enhanced CTEnrolling 1,000 patients from November 2024 to October 2025, this cohort will prospectively validate the AI model's performance and explore the biological basis of metastasis by analyzing pathological tissues, RNA sequencing, and tumor immune microenvironment characteristics. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Early-stage NSCLC receiving curative treatment (surgery or SBRT).
Key inclusion criteria (8 listed)
- Pathologically confirmed non-small cell lung cancer;
- Clinical stage I (AJCC, 8th edition, 2017);
- Age≥18 years old;
- KPS score≥70;
- Patients who have undergone primary NSCLC radical surgery or SBRT treatment;
Key exclusion criteria (4 listed)
- Poor quality of computed tomography imaging;
- Baseline imaging shows pure ground-glass nodules (GGO);
- Uncontrolled epilepsy, central nervous system disease, or history of mental disorders, judged by the researcher to potentially interfere with the signing of the informed consent form or affect patient compliance.;
- 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 |
|---|---|---|
| chest enhanced CTThis is an observational study and patients will receive routine clinical treatment according to the corresponding guidelines. We will collect the enrolled patient's chest enhanced CT and clinicopathological parameters. | Diagnostic test | Prospective Cohort, Retrospective Cohort |
Primary outcome
Recurrence-free survival (RFS)
Time frame: 1 year
The time from surgical treatment or SBRT to disease recurrence or death. Patients who were still not progressing at the time of analysis will have the date of their last contact as the cutoff date.
1 secondary outcome
Overall Survival (OS)
Time frame: 1 year
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 20 Jan 2025.
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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.
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 NSCLC (Non-small Cell Lung Cancer) and Artificial Intelligence (AI), run by Fudan 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 8 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: looking for participants now, as of the sponsor's last update on 20 Jan 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?
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?
Contact Zhengfei Zhu, PhD (+86-18017312901, fuscczzf@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Recurrence-free survival (RFS) (time frame: 1 year). 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 20 Jan 2025; AzTrials last refreshed its copy on 26 Oct 2026.