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.
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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.
Cohort
Prospective
BUB1-Integrated Deep Learning Model for Gastric Cancer Metastasis and Cachexia Prediction
Diagnostic testThis 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.
Predictive Accuracy of the BUB1-Integrated Deep Learning Model for Gastric Cancer Peritoneal Metastasis and Cachexia
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Qun Zhao
Lead sponsor
Hebei Medical University
Sponsor institution
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