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

Qun Zhao

Status: Enrolling

Conditions: T1 Gastric Cancer Lymph Node Metastasis Early Gastric Cancer Artificial Intelligence-Assisted Diagnosis Multimodal Data Integration

Treatments: Diagnostic Test: Multimodal Artificial Intelligence Diagnostic Model for Lymph Node Metastasis in T1 Gastric Cancer

Study type: Observational

Funder types: Other

Identifiers

NCT07124754

GC-RAD-AI-2025-04

Details and patient eligibility

About

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.

Enrollment: 300 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

Age 18 years or older

Histologically confirmed primary gastric adenocarcinoma

Clinical stage T1 (T1a or T1b) confirmed by endoscopy and imaging

Undergoing radical gastrectomy with lymph node dissection

Preoperative data available: clinical variables, CT imaging, and pathology slides

Written informed consent provided

Exclusion criteria

History of other malignancies within the past 5 years

Received neoadjuvant chemotherapy or radiotherapy

Incomplete clinical or pathological data

Poor quality or missing CT or histopathology images

Patients with distant metastasis (M1) at diagnosis

Inability or refusal to provide informed consent