Multimodal Deep Learning for Lymph Node Metastasis Prediction and Physician Performance Assessment in T1 Gastric Cancer

Condition(s) studied

Investigational drug(s) / intervention(s)

Multimodal Artificial Intelligence Diagnostic Model for Lymph Node Metastasis in T1 Gastric Cancer: This intervention involves the use of a custom-built artificial intelligence (AI) diagnostic model that integrates multimodal data-including clinical variables, histopathological features, and imaging data-to predict lymph node metastasis in patients with T1-stage gastric cancer. The model provides risk probability scores and classification outputs that assist physicians in diagnostic decision-making. The AI system will be compared with physician performance at different levels of experience (resident, attending, senior) to assess its impact on diagnostic accuracy and clinical decision support.

Study summary

This study aims to develop and validate an artificial intelligence (AI) model that integrates clinical, pathological, and imaging data to predict the presence of lymph node metastasis (LNM) in patients with T1-stage gastric cancer.

The study will also compare the diagnostic performance of physicians with and without AI assistance, including clinicians with varying levels of experience.

The goal is to improve early decision-making and support more personalized treatment strategies for patients with early gastric cancer.

Eligibility

Primary outcome measure(s)

• Diagnostic Accuracy of the AI Model for Predicting Lymph Node Metastasis in T1 Gastric Cancer — Immediately after surgery (within 7 days postoperatively, based on final pathological report)
• Diagnostic Accuracy of the AI Model for Predicting Lymph Node Metastasis in T1 Gastric Cancer — At the time of final pathological diagnosis (typically within 3-7 days after surgery)

Trial sites (1)

More Qun Zhao trials in China