Artificial Intelligence (AI), Lymphnode Metastasis, NSCLC (Non-small Cell Lung Cancer)
Conditions
Brief 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.
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.
Sponsors
Fudan University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Recurrence-free survival (RFS) | 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
| Measure | Time frame | Description |
|---|---|---|
| Overall Survival (OS) | 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. |
Countries
China
Contacts
Primary ContactZhengfei Zhu, PhD
Outcome results
None listed