Diagnosis of Peritoneal Exfoliative Cytology-positive Gastric Cancer Based on Artificial Intelligence-driven Virtual Biopsy Technology
NCT ID: NCT06759467
Last Updated: 2025-01-06
Study Results
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Basic Information
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COMPLETED
346 participants
OBSERVATIONAL
2024-01-01
2024-06-30
Brief Summary
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In this study, we propose the use of AI algorithms to analyze non-invasive biomarkers, including transcriptomic profiles and imaging data, to predict the presence of peritoneal exfoliative cytology-positive gastric cancer. Virtual biopsy leverages AI to integrate multiple datasets, providing a comprehensive diagnostic tool that could potentially replace or supplement current invasive diagnostic procedures. By developing this technology, we aim to improve the early diagnosis and monitoring of gastric cancer, particularly in cases with occult peritoneal metastasis, and ultimately enhance patient outcomes through more timely and accurate treatment strategies.
The study will involve the collection of clinical samples from gastric cancer patients with suspected peritoneal metastasis. The AI model will be trained on these samples to identify relevant biomarkers for PEC-positive gastric cancer. Clinical validation will be conducted to assess the performance of this AI-driven virtual biopsy system compared to conventional diagnostic methods.
This study has the potential to provide a novel, non-invasive diagnostic approach for gastric cancer with peritoneal involvement, offering a significant advancement in the field of early cancer detection and personalized medicine.
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Interventions
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AI-Driven Virtual Biopsy for Diagnosis of Peritoneal Exfoliative Cytology-Positive Gastric Cance
The intervention involves the use of an artificial intelligence (AI)-driven virtual biopsy technology for the non-invasive diagnosis of gastric cancer with positive peritoneal exfoliative cytology (PEC). Unlike traditional biopsy methods, which require invasive procedures to obtain tissue samples, this intervention utilizes AI algorithms to analyze non-invasive biomarkers derived from patient samples such as blood, urine, or peritoneal lavage fluid.
The AI model is designed to integrate various data types, including transcriptomic profiling, imaging data, and other biomarkers, to predict the presence of PEC-positive gastric cancer. This technology employs advanced machine learning techniques to identify molecular and cellular features indicative of peritoneal metastasis, providing a diagnostic tool that is potentially more sensitive and less invasive than conventional methods.
The intervention is unique in its ability to combine multi-omics data (such as gene expression and imaging.
Eligibility Criteria
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Inclusion Criteria
Positive Peritoneal Lavage Cytology (PEC): Patients with suspected PEC-positive gastric cancer, based on previous or current peritoneal lavage cytology results or high clinical suspicion.
ECOG Performance Status: Eastern Cooperative Oncology Group (ECOG) performance status of 0-2, indicating that the patient is well enough to participate in the study and undergo necessary diagnostic procedures.
Informed Consent: Ability and willingness to provide informed consent and comply with the study protocol.
Exclusion Criteria
Severe Comorbidities: Severe cardiovascular, respiratory, renal, or hepatic disease that would impair the patient's ability to participate in the study or undergo the required diagnostic procedures.
Pregnancy or Lactation: Pregnant or breastfeeding women, or women planning to become pregnant during the study period.
Non-Eligible Clinical Conditions: Any condition that, in the opinion of the investigator, could interfere with the patient's participation or compliance with the study protocol, or affect the quality of the data.
Inability to Provide Samples: Patients who are unable to provide the necessary clinical samples (e.g., blood, urine, or peritoneal lavage fluid) for the AI-driven virtual biopsy analysis.
18 Years
75 Years
ALL
No
Sponsors
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Qun Zhao
OTHER
Responsible Party
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Qun Zhao
Professor
Locations
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the Fourth Hospital of Hebei Medical University
Shijiazhuang, None Selected, China
Countries
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Other Identifiers
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GC-CY1
Identifier Type: -
Identifier Source: org_study_id
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