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

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

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT07124754
Enrollment
300
Registered
2025-08-15
Start date
2025-01-01
Completion date
2025-12-30
Last updated
2025-08-15

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

Conditions

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

Keywords

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

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.

Interventions

DIAGNOSTIC_TESTMultimodal 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 (resident, attending, senior) to assess its impact on diagnostic accuracy and clinical decision support.

Sponsors

Qun Zhao
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum

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

Design outcomes

Primary

MeasureTime frame
Diagnostic Accuracy of the AI Model for Predicting Lymph Node Metastasis in T1 Gastric CancerImmediately after surgery (within 7 days postoperatively, based on final pathological report)

Countries

China

Outcome results

None listed

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