Diagnosis of Peritoneal Exfoliative Cytology-positive Gastric Cancer Based on Artificial Intelligence-driven Virtual Biopsy Technology (GC-CY1)

This clinical study aims to develop and evaluate an artificial intelligence (AI)-driven virtual biopsy technology for the diagnosis of gastric cancer with positive peritoneal exfoliative cytology (PEC). Gastric cancer with peritoneal metastasis often presents a challenge for early detection and diagnosis, with traditional diagnostic methods such as imaging and histopathology being limited in sensitivity and specificity.

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.

Study Overview

• Status: Completed
• Conditions: Gastric Cancer With Positive Peritoneal Exfoliative Cytology
• Intervention / Treatment: Diagnostic test: AI-Driven Virtual Biopsy for Diagnosis of Peritoneal Exfoliative Cytology-Positive Gastric Cance

• Study Type: Observational
• Enrollment (Actual): 346

Contacts and Locations

Study Locations

• China
  • None Selected
    • Shijiazhuang, None Selected, China, 050011
      • The Fourth Hospital of Hebei Medical University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

The study will involve patients diagnosed with primary gastric cancer who have a clinical suspicion or documented evidence of peritoneal metastasis. Specifically, the study will target individuals with peritoneal exfoliative cytology (PEC)-positive gastric cancer, a condition where peritoneal involvement is suspected based on cytological examination of peritoneal lavage fluid.

Description

Inclusion Criteria:

Age: Patients aged 18-75 years. Diagnosis of Gastric Cancer: Histologically confirmed diagnosis of primary gastric cancer, with clinical suspicion of peritoneal metastasis (based on imaging or other clinical findings).

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:

Previous Cancer History: History of other malignancies within the past 5 years, except for non-melanoma skin cancer or in situ cancers.

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.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
Accuracy of AI-Driven Virtual Biopsy in Diagnosing PEC-Positive Gastric Cancer	24 months postoperative follow-up

Collaborators and Investigators

• Sponsor: Qun Zhao

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2024
• Primary Completion (Actual): June 30, 2024
• Study Completion (Actual): June 30, 2024

Study Registration Dates

• First Submitted: December 28, 2024
• First Submitted That Met QC Criteria: December 28, 2024
• First Posted (Actual): March 25, 2025

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: December 28, 2024
• Last Verified: December 1, 2024

More Information

Terms related to this study

• Keywords: gastric cancer with positive peritoneal exfoliative cytology
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Stomach Diseases; Stomach Neoplasms
• Other Study ID Numbers: GC-CY1

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No