Artificial Intelligence-based Model for the Prediction of Occult Lymph Node Metastasis and Improvement of Clinical Decision-making in Non-small Cell Lung Cancer
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: chest enhanced CT.
- Who it may be relevant to
- Registry conditions: NSCLC (Non-small Cell Lung Cancer), Artificial Intelligence (AI), Lymphnode Metastasis. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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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
Overview
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.
Interventions
- Diagnostic test chest enhanced CT
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.
Primary outcome measures
- Recurrence-free survival (RFS) [Time frame: 1 year]
Secondary outcome measures (1)
- Overall Survival (OS) [Time frame: 1 year]
Eligibility 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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Fudan university Shanghai Cancer Center — Shanghai
Identifiers
NCT: NCT06684418 · OLNM-AI