Multimodal Model Predicts Recurrence

Completed

• Conditions: Gastric Adenocarcinoma

• Registration Number: NCT06690268
• Lead Sponsor: Qun Zhao

Brief Summary

This study focuses on developing an advanced model that combines clinical information, imaging, and pathology data to predict the likelihood of cancer returning after surgery in patients with locally advanced gastric cancer. By using artificial intelligence (AI), this model analyzes various data sources to create a more accurate prediction of recurrence risk, which can help doctors, patients, and families better understand the chances of recurrence. This AI-driven approach allows healthcare providers to make more informed decisions about personalized follow-up care and potential additional treatments to improve patient outcomes.

Detailed Description

Not available

Study Timeline

• Study Start: 2022-01-01
• Primary Completion: 2024-10-31
• Study Completion: 2024-10-31
• Status Verified: 2024-11
• Study First Submitted: 2024-11-13
• Study First Submitted QC: 2024-11-13
• Last Update Submitted: 2024-11-13
• Study First Posted: 2024-11-15
• Last Update Posted: 2024-11-15

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 93

Inclusion Criteria

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• Patients diagnosed with locally advanced gastric cancer (Stage II or III).
• Patients who have undergone surgical resection for gastric cancer.
• Patients with complete clinical, imaging, and pathology data available for analysis.
• Age 18 years or older.
• Patients who provide informed consent to participate in the study.

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Exclusion Criteria

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• Patients with distant metastasis (Stage IV) at the time of diagnosis.
• Patients with incomplete or missing clinical, imaging, or pathology data.
• Patients who have received prior treatment for gastric cancer other than surgical resection.
• Patients with other concurrent malignancies.
• Patients who are unable or unwilling to comply with the study follow-up requirements.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Prediction accuracy of postoperative recurrence in locally advanced gastric cancer	24 months postoperative follow-up	The primary outcome measure is the accuracy of the multimodal AI model in predicting the risk of postoperative recurrence in patients with locally advanced gastric cancer. This is assessed by comparing the model's predictions with actual recurrence events over a specified follow-up period, allowing evaluation of its effectiveness in identifying high-risk patients and guiding clinical decisions.

Trial Locations

Locations (1)

the Fourth Hospital of Hebei Medical University; 🇨🇳 Shijiazhuang, Hebei, China