Multimodal Deep Learning for Lymph Node Metastasis Prediction and Physician Performance Assessment in T1 Gastric Cancer
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Multimodal Artificial Intelligence Diagnostic Model for Lymph Node Metastasis in T1 Gastric Cancer.
- Who it may be relevant to
- Registry conditions: T1 Gastric Cancer Lymph Node Metastasis Early Gastric Cancer Artificial Intelligence-Assisted Diagnosis Multimodal Data Integration. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Development and Validation of a Multimodal Artificial Intelligence Model for Predicting Lymph Node Metastasis in T1 Gastric Cancer and Its Impact on Physician Diagnostic Performance
Overview
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.
Interventions
- Diagnostic test 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 (
Primary outcome measures
- Diagnostic Accuracy of the AI Model for Predicting Lymph Node Metastasis in T1 Gastric Cancer [Time frame: 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 [Time frame: At the time of final pathological diagnosis (typically within 3-7 days after surgery)]
Eligibility criteria
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- the Fourth Hospital of Hebei Medical University — Shijiazhuang
Identifiers
NCT: NCT07124754 · GC-RAD-AI-2025-04