AI model to predict hidden lymph node cancer in lung patients
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 study is testing a new AI tool to see if it can find hidden cancer in lymph nodes for people with early-stage lung cancer to help doctors make better treatment choices.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 6000 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | Fudan University Academic / other |
| Locations | 1 site (Shanghai) |
| Trial ID | NCT06684418 on ClinicalTrials.gov |
What this trial studies
This nationwide, multicenter observational study aims to develop and validate an artificial intelligence (AI) model designed to detect occult lymph node metastasis in patients with early-stage non-small cell lung cancer (NSCLC). The study addresses the challenge of undetected lymph node metastasis, which affects treatment decisions in a significant percentage of cases. By utilizing deep learning techniques to analyze imaging features alongside clinical data, the study seeks to enhance clinical decision-making and improve patient outcomes. Insights gained may also reveal biological mechanisms underlying lymph node metastasis in NSCLC.
Who should consider this trial
Good fit: Ideal candidates include adults aged 18 and older with pathologically confirmed early-stage non-small cell lung cancer who have undergone primary radical surgery or stereotactic body radiation therapy.
Not a fit: Patients with poor quality imaging, pure ground-glass nodules, or those with uncontrolled epilepsy or mental disorders may not benefit from this study.
Why it matters
Potential benefit: If successful, this study could lead to improved detection of hidden lymph node metastasis, allowing for more informed treatment decisions for NSCLC patients.
How similar studies have performed: Other studies utilizing AI for cancer detection have shown promising results, indicating potential success for this approach.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * 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. Exclusion Criteria: * 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.
Where this trial is running
Shanghai
- Fudan university Shanghai Cancer Center — Shanghai, China (Recruiting)
Study contacts
- Study coordinator: Zhengfei Zhu, PhD
- Email: fuscczzf@163.com
- Phone: +86-18017312901
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.