AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy

Launched by CHINESE ACADEMY OF SCIENCES · Sep 6, 2023

Keywords

ClinConnect Summary

This clinical trial is studying how artificial intelligence (AI) can help predict how well patients with advanced gastric cancer will respond to a type of treatment called neoadjuvant chemotherapy. Neoadjuvant chemotherapy is given before surgery to shrink the cancer. The researchers will use patients' CT scans and biopsy images, along with their medical information, to create a smart model that can estimate the effectiveness of the treatment and help doctors make more personalized decisions for their patients.

To be eligible for this trial, participants must be 18 years or older and have a confirmed diagnosis of advanced gastric cancer. They should not have received any other cancer treatments before starting the chemotherapy in this study. Participants will need to provide recent CT and biopsy images, as well as their complete medical history. If you join the trial, you can expect to undergo standard chemotherapy and have your progress closely monitored. This study is currently recruiting participants of all genders who meet the criteria, and it aims to improve treatment strategies for gastric cancer patients.

Gender

ALL

Eligibility criteria

• Inclusion Criteria:
• • Age 18 years or older;
• • Pathologically diagnosed with advanced gastric cancer in accordance with the American AJCC's TNM staging standards;
• • Have not undergone any systematic anti-cancer treatments before neoadjuvant chemotherapy and have not had surgery for local progression or distant metastasis;
• • Received standard neoadjuvant chemotherapy as recommended by the clinical guidelines, and have documented treatment details;
• • CT imaging and biopsy pathology images strictly taken within one month prior to starting neoadjuvant treatment;
• • Patients possess comprehensive preoperative clinical information and post-operative TRG grading.
• Exclusion Criteria:
• • Patients whose CT or pathology images are unclear, making lesion assessment infeasible;
• • Patients diagnosed with other concurrent tumors.