Multimodal AI for Predicting Response to Neoadjuvant Immunotherapy in Gastric Cancer (PRISM-GC)

Gastric cancer is a major global health challenge. Currently, a combination of chemotherapy and immunotherapy (PD-1 inhibitors) is frequently used before surgery to shrink tumors, a strategy known as neoadjuvant therapy. While this approach is effective for many patients, responses vary significantly, and there are currently no reliable tools to predict which patients will benefit the most before treatment begins. The PRISM-GC study aims to develop and validate a novel Artificial Intelligence (AI) system to address this need. This is a prospective, observational study that will collect data from patients diagnosed with locally advanced gastric cancer who are scheduled to receive standard neoadjuvant chemotherapy combined with immunotherapy in a real-world clinical setting. The specific choice of immunotherapy drug is determined by the treating physician and is not dictated by the study. Researchers will analyze standard preoperative CT scans and pathological tissue slides using advanced deep learning algorithms. The goal is to create a "multimodal" AI model that can accurately predict how well a tumor will respond to treatment (specifically, whether the tumor will disappear or shrink significantly). If successful, this AI tool could help doctors personalize treatment plans in the future, ensuring that each patient receives the most effective therapy while avoiding unnecessary side effects.

NCT Number	NCT07401199
Lead Sponsor	Qun Zhao Collaborators: Shijiazhuang People's Hospital, Baoding Central Hospital, Hengshui People's Hospital, Wuhan University Affiliated People's Hospital, The Fifth Affiliated Hospital of Anhui Medical University
Conditions	Gastric Cancer (GC), Locally Advanced Gastric Cancer
Enrollment	2,000 participants
Start Date	2026-02-05
Primary Completion	2027-12-30 (estimated)
Study Completion	2027-12-30 (estimated)
Updated on ClinicalTrials.gov	2026-05-15