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

Recruiting
Conditions
T1 Gastric Cancer Lymph Node Metastasis Early Gastric Cancer Artificial Intelligence-Assisted Diagnosis Multimodal Data Integration
Registration Number
NCT07124754
Lead Sponsor
Qun Zhao
Brief 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.

Detailed Description

Not available

Recruitment & Eligibility

Status
RECRUITING
Sex
All
Target Recruitment
300
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

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Primary Outcome Measures
NameTimeMethod
Diagnostic Accuracy of the AI Model for Predicting Lymph Node Metastasis in T1 Gastric CancerAt the time of final pathological diagnosis (typically within 3-7 days after surgery)
Secondary Outcome Measures
NameTimeMethod

Trial Locations

Locations (1)

the Fourth Hospital of Hebei Medical University

🇨🇳

Shijiazhuang, None Selected, China

the Fourth Hospital of Hebei Medical University
🇨🇳Shijiazhuang, None Selected, China
Ping'an Ding
Contact
031186095363
ding_ping_an@hebmu.edu.cn

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