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NCT Number: 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

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.

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Key information

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Fudan university Shanghai Cancer Center

Shanghai, China

Location status: Recruiting

Location contact

Zhengfei Zhu, PhD

CONTACT

[email protected]

18017312901

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

chest enhanced CT

Diagnostic Test

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 outcomes

  1. Recurrence-free survival (RFS)

    Time frame: 1 year

    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.

Secondary outcomes

  1. Overall Survival (OS)

    Time frame: 1 year

    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.

Study contacts

Contact information is provided by the study sponsor or research team.

Zhengfei Zhu, PhD

CONTACT

[email protected]

+86-18017312901

Sponsors and collaborators

Lead sponsor

Fudan University

Other

Registry information

Official study 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

Important dates

Study start
2024
Primary completion
2025
Study completion
2026
First posted
Nov 12, 2024
Registry last updated
Jan 20, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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