AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy

Summary

This study seeks to develop a deep-learning-based intelligent predictive model for the efficacy of neoadjuvant chemotherapy in gastric cancer patients. By utilizing the patients' CT imaging data, biopsy pathology images, and clinical information, the intelligent model will predict the post-neoadjuvant chemotherapy efficacy and prognosis, offering assistance in personalized treatment decisions for gastric cancer patients.

Conditions

• Gastric Cancer
• Image
• Pathology

Interventions

DRUG

Neoadjuvant Chemotherapy

Participants in this group are diagnosed with gastric cancer and are scheduled to undergo neoadjuvant chemotherapy as a part of their treatment regimen. The specific chemotherapy drugs, dosages, and schedules will be determined according to established clinical guidelines and the participant's specific condition.

Sponsors & Collaborators

• Peking University Cancer Hospital & Institute

  collaborator OTHER

• Cancer Institute and Hospital, Chinese Academy of Medical Sciences

  collaborator OTHER

• Yunnan Cancer Hospital

  collaborator OTHER

• Henan Cancer Hospital

  collaborator OTHER_GOV

• Zhenjiang First People's Hospital

  collaborator OTHER

• First Hospital of China Medical University

  collaborator OTHER

• Cancer Hospital of Guangxi Medical University

  collaborator OTHER

• Peking University People's Hospital

  collaborator OTHER

• Tianjin Medical University Cancer Institute and Hospital

  collaborator OTHER

• The First Affiliated Hospital of Zhengzhou University

  collaborator OTHER

• Nanfang Hospital, Southern Medical University

  collaborator OTHER

• The Affiliated Hospital of Qingdao University

  collaborator OTHER

• Ruijin Hospital

  collaborator OTHER

• Sixth Affiliated Hospital, Sun Yat-sen University

  collaborator OTHER

• Peking Union Medical College Hospital

  collaborator OTHER

• Xiangya Hospital of Central South University

  collaborator OTHER

• Affiliated Cancer Hospital & Institute of Guangzhou Medical University

  collaborator OTHER

• The First Affiliated Hospital of Soochow University

  collaborator OTHER

• First Affiliated Hospital, Sun Yat-Sen University

  collaborator OTHER

• Fujian Medical University Union Hospital

  collaborator OTHER

• Fujian Cancer Hospital

  collaborator OTHER_GOV

• San Raffaele University Hospital, Italy

  collaborator OTHER

• Chinese Academy of Sciences

  lead OTHER_GOV

Principal Investigators

• Yali Zang, Ph.D. · Institute of Automation, Chinese Academy of Sciences

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2023-09-10

Primary Completion

2024-08-31

Completion

2029-12-31

Countries

• China
• Italy

Study Locations