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

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.