Not Yet RecruitingNot Applicable

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

500 participantsStarted 2025-03-01

Plain-language 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.

Who can participate

Age range18 Years – 75 Years

SexALL

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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.

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.

What they're measuring

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

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

Trial details

NCT IDNCT06858644

SponsorQun Zhao

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2027-03-01