AI-Based Prediction of Lymph Node Metastasis in Gastric Cancer Using Preoperative Multimodal Data

This study aims to develop and validate an artificial intelligence (AI) system that can predict whether lymph node metastasis has occurred in patients with gastric cancer before surgery. Using preoperative imaging and pathology data, the AI models will not only predict if metastasis is present but also identify which specific lymph node stations or individual lymph nodes are involved. All lymph nodes will be carefully removed during surgery and examined one by one with detailed pathological methods to ensure accurate diagnosis. The goal is to improve the accuracy of lymph node assessment and assist doctors in making better treatment decisions.

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Key information

Conditions

Gastric Cancer Adenocarcinoma Metastatic Artificial Intelligence (AI) in Diagnosis Lymph Node Metastasis Lymphatic Metastasis Neoplasm Metastasis Neoplasms Neoplastic Processes Pathologic Processes Pathological Conditions, Signs and Symptoms

Age range

18 year–80 year

Sex eligibility

All sexes

Study type

Observational

Primary location

the Fourth Hospital of Hebei Medical University

Shijiazhuang, None Selected, 050011, China

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Age 18 years or older

Histologically confirmed gastric adenocarcinoma

Scheduled for curative-intent gastrectomy with lymphadenectomy

Completed preoperative imaging with contrast-enhanced CT or MRI

Available preoperative biopsy pathology report

Able and willing to provide written informed consent

Exclusion criteria

Evidence of distant metastasis on preoperative imaging

Prior chemotherapy, radiotherapy, or major abdominal surgery

Severe comorbidities contraindicating surgery

Incomplete or poor-quality preoperative imaging or pathology data

Pregnancy or lactation

Treatment and study plan

Artificial Intelligence-Based Predictive Model for Lymph Node Metastasis

Diagnostic Test

The intervention is an artificial intelligence-based predictive model developed using preoperative multimodal data, including contrast-enhanced CT images, preoperative histopathological findings, and clinical features. The model is designed to predict (1) the presence or absence of lymph node metastasis, (2) the specific lymph node stations involved, and (3) the individual lymph nodes involved. Each lymph node's metastatic status is confirmed by serial pathological sectioning of surgically retrieved nodes, ensuring a highly accurate reference standard for model training and validation. This distinguishes the intervention from traditional imaging-based assessments and from other AI models that do not use individually validated lymph node pathology.

Primary outcomes

Diagnostic Accuracy of the AI Model in Predicting Presence of Lymph Node Metastasis in Gastric Cancer

Time frame: From Preoperative Evaluation to Completion of Postoperative Pathological Analysis (Approximately 4-6 Weeks)

Sponsors and collaborators

Lead sponsor

Qun Zhao

Other

Collaborators

Baoding First Central Hospital
Hengshui People's Hospital
Nanjing University School of Medicine
No.1 Hospital of Shijiazhuang City
Renmin Hospital of Wuhan University
The Second Affiliated Hospital of Xingtai Medical College

Registry information

Official study title

Artificial Intelligence-Based Prediction of Lymph Node Metastasis and Nodal Station Involvement in Gastric Cancer Using Preoperative Multimodal Imaging and Pathology Data

Important dates

Study start: 2025
Primary completion: 2025
Study completion: 2025
First posted: May 4, 2025
Registry last updated: May 4, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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