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

Condition(s) studied

Investigational drug(s) / intervention(s)

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.

Study 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

Primary outcome measure(s)

• 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.

Trial sites (1)

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