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

Qun Zhao

Status: Enrolling

Conditions: Gastric Cancer (GC); Locally Advanced Gastric Cancer

Treatments: Diagnostic Test: Multimodal AI Assessment; Drug: Standard of Care PD-1 Inhibitors

Study type: Observational

Funder types: Other

Identifiers

NCT07401199

PRISM-GC-001

Details and patient eligibility

About

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.

Enrollment: 2,000 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

Age ≥ 18 years.

Histologically confirmed gastric or gastroesophageal junction adenocarcinoma.

Clinical stage cT3-4a, N+, M0 (locally advanced) assessed by CT/MRI and endoscopic ultrasound.

Scheduled to receive neoadjuvant chemotherapy combined with PD-1 inhibitors (regimens including but not limited to SOX/XELOX + Sintilimab/Tislelizumab/Camrelizumab, etc.) as standard of care.

Availability of standard pre-treatment contrast-enhanced abdominal CT images.

Willingness to provide peripheral blood samples and tumor tissue (biopsy/surgical) for sequencing and analysis.

ECOG performance status 0-1.

Adequate organ function to tolerate systemic chemotherapy.

Exclusion criteria

Evidence of distant metastasis (Stage IV) or unresectable disease.

Previous systemic anti-tumor therapy for gastric cancer (chemotherapy, radiotherapy, or immunotherapy).

History of other malignancies within the past 5 years.

Active autoimmune diseases requiring systemic immunosuppressive treatment (contraindication for PD-1 inhibitors).

Emergency surgery due to obstruction, perforation, or uncontrolled bleeding.

Severe metallic artifacts on CT images that interfere with radiomic feature extraction.

Pregnancy or lactation.

Trial design

2,000 participants in 1 patient group

LAGC Pan-Immunotherapy Cohort

Description:

Patients diagnosed with locally advanced gastric cancer (cT3-4a, N+) who are scheduled to receive neoadjuvant chemotherapy combined with PD-1 inhibitors (including but not limited to Sintilimab, Tislelizumab, Camrelizumab, etc.) in a real-world clinical setting. The specific choice of immunotherapy regimen is determined by the treating physician. Multimodal data, including preoperative contrast-enhanced CT images, pathological whole-slide images, and biospecimens (blood/tissue), will be collected for AI model development and validation.

Treatment:

Drug: Standard of Care PD-1 Inhibitors

Diagnostic Test: Multimodal AI Assessment