Actively Recruiting
Artificial Intelligence Model to Predict Hidden Lymph Node Metastasis and Aid Clinical Decisions in Early Non-small Cell Lung Cancer: A Multicenter Observational Study
Led by Fudan University · Updated on 2025-01-20
6000
Participants Needed
1
Research Sites
82 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
Researchers are developing and validating a new artificial intelligence (AI) model to detect hidden lymph node metastasis in patients with early-stage non-small cell lung cancer (NSCLC). Despite advances in imaging, a significant number of cases with occult nodal metastasis remain undetected before surgery, which affects treatment choices. This nationwide, multicenter observational study aims to combine detailed imaging features with clinical data using deep learning to better predict the risk of hidden lymph node involvement and improve clinical decision-making. Participants will undergo routine clinical treatment based on current guidelines, and their chest enhanced CT scans along with clinical and pathological data will be collected for analysis. The study uses these multimodal data to train and validate the AI model without altering the standard care procedures. This approach allows the researchers to assess the feasibility of AI-driven detection methods in a real-world clinical setting. During the study, participants will have complete systemic imaging assessments before surgery or stereotactic body radiotherapy (SBRT), depending on tumor characteristics. Follow-up will be conducted after the primary treatment to monitor outcomes. The main outcome measured is recurrence-free survival at one year, which will help evaluate the AI model's potential to support treatment decisions and understand the biological processes behind lymph node metastasis in NSCLC.
CONDITIONS
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
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Pathologically confirmed non-small cell lung cancer
- Clinical stage I (AJCC, 8th edition, 2017)
- Age 18 years or older
- Karnofsky Performance Status (KPS) score of 70 or higher
- Patients who have undergone primary NSCLC radical surgery or stereotactic body radiotherapy (SBRT)
- Complete systemic lesion imaging assessment before primary NSCLC radical surgery or SBRT (PET/CT and/or invasive mediastinal staging required for tumors 2 3 cm or centrally located tumors)
- Patients willing to cooperate with follow-up after primary NSCLC treatment
- Informed consent provided by the patient
You will not qualify if you...
- 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 that may interfere with consent or compliance
- Loss to follow-up
AI-Screening
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Trial Site Locations
Total: 1 location
1
Fudan university Shanghai Cancer Center
Shanghai, China
Actively Recruiting
Research Team
Z
Zhengfei Zhu, PhD
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
2
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