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.

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.

Inclusion Criteria:

Adults aged 18-75 years diagnosed with gastric cancer (GC) at any stage. Histopathologically confirmed GC with available radiological (CT/MRI) and pathological data (biopsy samples).

Patients with or at risk of peritoneal metastasis and/or cachexia, as determined by clinical assessment and imaging.

Ability to provide informed consent and comply with study protocols. Willingness to undergo regular follow-up imaging and clinical evaluation for the duration of the study.

Exclusion Criteria:

Patients with other primary cancers or serious comorbidities (e.g., severe cardiovascular disease, uncontrolled diabetes).

Pregnant or breastfeeding women. Patients with contraindications to MRI or CT imaging. Those with insufficient clinical data (e.g., missing radiopathological information) for model training.

Patients who are unable or unwilling to comply with the study protocol, including follow-up visits and evaluations.

The study population will consist of adult patients diagnosed with gastric cancer (GC) at various stages of the disease. Patients will be selected based on the presence of or risk factors for peritoneal metastasis and/or cachexia, which will be assessed through clinical evaluations, imaging (CT/MRI), and histopathological examination. Participants will be recruited from a cohort of GC patients who have available genomic, radiological, and pathological data, which are essential for training the predictive model.

The study will focus on patients with a broad spectrum of GC manifestations, including both early and advanced stages, to ensure the model is applicable across different disease profiles. This diverse population will help evaluate the robustness and generalizability of the model in predicting peritoneal metastasis and cachexia, aiming for a comprehensive representation of GC progression.