Clinical trial NCT06759467: Diagnosis of Peritoneal Exfoliative Cytology-positive Gastric Cancer Based on Artificial Intelligence-driven Virtual Biopsy Technology
Official title: Diagnosis of Peritoneal Exfoliative Cytology-positive Gastric Cancer Based on Artificial Intelligence-driven Virtual Biopsy Technology
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Gastric Cancer With Positive Peritoneal Exfoliative Cytology
Keywords: gastric cancer with positive peritoneal exfoliative cytology
Lead sponsor: Hebei Medical University
Sponsor class: OTHER
First posted: 2025-01-06
Enrollment: 346
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

Brief summary: 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.


Eligibility criteria: 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.

Primary outcomes:
measure: Accuracy of AI-Driven Virtual Biopsy in Diagnosing PEC-Positive Gastric Cancer
timeFrame: 24 months postoperative follow-up
Secondary outcomes:
Other outcomes:
Locations:
city: Shijiazhuang
country: China
facility: the Fourth Hospital of Hebei Medical University
state: None Selected
Study officials:
References: