NCT06690268

Multimodal Model Predicts Recurrence

Multimodal Clinical-imaging-pathology-driven Artificial Intelligence Model for Predicting Postoperative Recurrence of Locally Advanced Gastric Cancer

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 93 (actual)
• Sponsor: Qun Zhao · Academic / Other
• Sex: All
• Age: 18 Years – 75 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Gastric Adenocarcinoma

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Multimodal AI-driven predictive model	This intervention involves a multimodal artificial intelligence (AI) model that integrates clinical data, imaging results, and pathology findings to predict the risk of postoperative recurrence in patients with locally advanced gastric cancer. Unlike traditional methods that may rely on single data sources, this AI-driven model synthesizes multiple types of patient information, offering a comprehensive and personalized prediction of recurrence risk. This approach aims to improve accuracy in identifying high-risk patients, allowing for more tailored follow-up and treatment planning to enhance patient outcomes.

Timeline

• Start date: 2022-01-01
• Primary completion: 2024-10-31
• Completion: 2024-10-31
• First posted: 2024-11-15
• Last updated: 2024-11-15

Locations

1 site across 1 country: China