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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
trial testing chest enhanced CT in NSCLC (Non-small Cell Lung Cancer) in 6,000 participants. Currently enrolling.
1 December 2025
Quick facts
| Lead sponsor | Fudan University |
|---|---|
| Status | Recruiting now |
| Study type | OBSERVATIONAL |
| Enrollment | 6,000 |
| Start date | 1 December 2024 |
| Primary completion | 1 December 2025 |
| Estimated completion | 30 June 2026 |
| Sites | 1 location across China |
Drugs / interventions tested
- chest enhanced CT
Conditions studied
- NSCLC (Non-small Cell Lung Cancer) — all drugs for NSCLC (Non-small Cell Lung Cancer) →
- Artificial Intelligence (AI) — all drugs for Artificial Intelligence (AI) →
- Lymphnode Metastasis — all drugs for Lymphnode Metastasis →
Sponsor
Fudan University
Who can join
18 and older, any sex, with NSCLC (Non-small Cell Lung Cancer) or Artificial Intelligence (AI). Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
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.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
Verify or expand the search:
- PubMed search for NCT06684418
- Europe PMC full search
- ASCO Meeting Library
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT06684418 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Fudan University
- Last refreshed: 20 January 2025
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT06684418.
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