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 Type
OBSERVATIONAL
Enrollment
346
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.
the Fourth Hospital of Hebei Medical University
Shijiazhuang, None Selected, China
Accuracy of AI-Driven Virtual Biopsy in Diagnosing PEC-Positive Gastric Cancer
Time frame: 24 months postoperative follow-up
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