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

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.

Study Overview

• Status: Recruiting
• Conditions: NSCLC (Non-small Cell Lung Cancer); Lymphnode Metastasis; Artificial Intelligence (AI)
• Intervention / Treatment: Diagnostic test: chest enhanced CT

• Study Type: Observational
• Enrollment (Estimated): 6000

Contacts and Locations

• Study Contact: Name: Zhengfei Zhu, PhD; Phone Number: +86-18017312901; Email: fuscczzf@163.com

Study Locations

• China
  • Shanghai, China
    • Recruiting
    • Fudan University Shanghai Cancer Center
    • Contact: Zhengfei Zhu, PhD; Phone Number: 18017312901; Email: fuscczzf@163.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Early-stage NSCLC receiving curative treatment (surgery or SBRT).

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Retrospective Cohort; Enrolling about 5,000 early-stage NSCLC patients from January 2018 to June 2024 across 25 centers in China, data including chest CT scans and clinicopathological parameters will be used to train and validate the AI model. Patients will be divided into "high-risk" and "low-risk" groups based on the model's risk score, and clinical benefits of treatments like lymph node dissection, adjuvant therapy, and SBRT will be analyzed.	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.
Prospective Cohort; Enrolling 1,000 patients from November 2024 to October 2025, this cohort will prospectively validate the AI model's performance and explore the biological basis of metastasis by analyzing pathological tissues, RNA sequencing, and tumor immune microenvironment characteristics.	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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Recurrence-free survival (RFS)	The time from surgical treatment or SBRT to disease recurrence or death. Patients who were still not progressing at the time of analysis will have the date of their last contact as the cutoff date.	1 year

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Overall Survival (OS)	The time from the surgery or SBRT until death from any cause. Patients who are still alive at the time of analysis will have their last contact date used as the cutoff date.	1 year

Collaborators and Investigators

• Sponsor: Fudan University

Study record dates

Study Major Dates

• Study Start (Actual): December 1, 2024
• Primary Completion (Estimated): December 1, 2025
• Study Completion (Estimated): June 30, 2026

Study Registration Dates

• First Submitted: November 11, 2024
• First Submitted That Met QC Criteria: November 11, 2024
• First Posted (Actual): November 12, 2024

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: January 17, 2025
• Last Verified: January 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Lung Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplastic Processes; Carcinoma, Bronchogenic; Bronchial Neoplasms; Lung Neoplasms; Neoplasm Metastasis; Carcinoma, Non-Small-Cell Lung; Lymphatic Metastasis
• Other Study ID Numbers: OLNM-AI

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No