RecruitingNCT06684418

Artificial Intelligence-based Model for the Prediction of Occult Lymph Node Metastasis and Improvement of Clinical Decision-making in Non-small Cell Lung Cancer

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

Sponsor

Fudan University

Enrollment

6,000 participants

Start Date

Dec 1, 2024

Study Type

OBSERVATIONAL

Conditions

Artificial Intelligence (AI)NSCLC (Non-small Cell Lung Cancer)Lymphnode Metastasis

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. 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.

Eligibility

Min Age: 18 Years

Inclusion Criteria8

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 Criteria4

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.

Interventions

DIAGNOSTIC_TESTchest 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.