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

NCT06858644 | Not Yet Recruiting

• 1 other identifier: BUB1
• Study Type: observational
• Target: 500
• Locations: 0 countries
• Sites: N/A

Brief 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.

Conditions

Gastric (Stomach) Cancer

Outcome Measures

Primary Outcomes (1)

• Predictive Accuracy of the BUB1-Integrated Deep Learning Model for Gastric Cancer Peritoneal Metastasis and Cachexia

  12 months for model training, validation, and initial clinical application

Interventions

BUB1-Integrated Deep Learning Model for Gastric Cancer Metastasis and Cachexia Prediction DIAGNOSTIC_TEST

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.

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Qun Zhao: lead

MeSH Terms

Conditions

Stomach Neoplasms

Condition Hierarchy (Ancestors)

Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Stomach Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 5 Years
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: February 27, 2025
• First Posted: March 5, 2025
• Study Start: March 1, 2025
• Primary Completion (Estimated): March 1, 2027
• Study Completion (Estimated): March 1, 2027
• Last Updated: March 5, 2025

Record last verified: 2025-02