NCT06858644

Development of a Predictive Model for Gastric Cancer Peritoneal Metastasis and Cachexia Using BUB1 and Radiopathomics Data With Deep Learning

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 500 (estimated)
• Sponsor: Qun Zhao · Academic / Other
• Sex: All
• Age: 18 Years – 75 Years
• Healthy volunteers: —

Summary

This clinical trial aims to develop a predictive model for gastric cancer (GC) peritoneal metastasis and cachexia by integrating BUB1 gene data with radiological and pathological data using advanced deep learning techniques. The study will focus on utilizing imaging genomics (radiomics) and histopathological data to identify early biomarkers for peritoneal metastasis and cachexia in GC patients. By leveraging deep learning algorithms, the project seeks to improve the accuracy and reliability of predictions, enabling earlier intervention and personalized treatment strategies. The ultimate goal is to enhance clinical decision-making and prognosis prediction in GC patients with peritoneal metastasis and cachexia.

Detailed description

Gastric cancer (GC) is one of the most common and aggressive malignancies, with peritoneal metastasis and cachexia significantly contributing to its poor prognosis. The BUB1 gene has been implicated in chromosomal instability and the progression of GC, but its role in peritoneal metastasis and cachexia remains unclear. This clinical trial aims to explore the potential of integrating BUB1 gene expression with imaging and pathological data to develop a predictive model for GC progression. The study will collect comprehensive data from GC patients, including genomic profiles (BUB1 gene expression), radiological images (CT/MRI scans), and histopathological findings. Advanced radiomics analysis will extract quantitative features from imaging data, while pathological data will be analyzed for relevant histological markers. The combined dataset will be fed into a deep learning model to identify patterns associated with peritoneal metastasis and cachexia, focusing on the identification of early biomarkers. The deep learning model will undergo iterative training and validation using both retrospective and prospective patient data. The primary endpoint of the trial is to assess the model's predictive accuracy for peritoneal metastasis and cachexia development, while secondary endpoints include its potential to inform personalized treatment strategies, improve survival rates, and guide clinical decision-making. This study will also investigate the correlation between BUB1 expression and the radiopathomics features in GC, providing insights into the underlying mechanisms driving peritoneal metastasis and cachexia. The findings aim to establish a robust, clinically applicable predictive tool that can be integrated into current clinical practice for better patient outcomes.

Conditions

• Gastric (Stomach) Cancer

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	BUB1-Integrated Deep Learning Model for Gastric Cancer Metastasis and Cachexia Prediction	This intervention utilizes a deep learning model that integrates BUB1 gene expression, radiopathomics (quantitative imaging features), and histopathological data to predict peritoneal metastasis and cachexia in gastric cancer (GC) patients. Unlike traditional approaches, this model combines genomic, imaging, and pathological data to enhance early detection and improve prognostic accuracy. The model aims to identify key patterns in multi-modal data to offer personalized predictions for GC progression. By leveraging artificial intelligence, it seeks to support clinicians in decision-making, improving patient outcomes through earlier interventions and tailored treatments. This approach offers a novel, comprehensive method for predicting GC metastasis and cachexia, providing a unique tool compared to existing interventions.

Timeline

• Start date: 2025-03-01
• Primary completion: 2027-03-01
• Completion: 2027-03-01
• First posted: 2025-03-05
• Last updated: 2025-03-05