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NCT06858644

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

Sponsor: Qun Zhao

View on ClinicalTrials.gov

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.

Key Details

Gender

All

Age Range

18 Years - 75 Years

Study Type

OBSERVATIONAL

Enrollment

500

Start Date

2025-03-01

Completion Date

2027-03-01

Last Updated

2025-03-05

Healthy Volunteers

Not specified

Interventions

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.