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

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

Status

Enrolling by invitation

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06957678

Enrollment

1200

Registered

2025-05-04

Start date

2025-01-01

Completion date

2025-12-31

Last updated

2025-05-04

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Artificial Intelligence (AI) in Diagnosis, Gastric Cancer Adenocarcinoma Metastatic, Lymph Node Metastasis

Brief summary

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.

Interventions

DIAGNOSTIC_TESTArtificial Intelligence-Based Predictive Model for Lymph Node Metastasis

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.

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 80 Years

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

Design outcomes

Primary

Measure	Time frame
Diagnostic Accuracy of the AI Model in Predicting Presence of Lymph Node Metastasis in Gastric Cancer	From Preoperative Evaluation to Completion of Postoperative Pathological Analysis (Approximately 4-6 Weeks)

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026