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

May 13, 2026 updated by: Qun Zhao

A Prospective, Multicenter, Real-World Cohort Study for the Development and Validation of a Multimodal Artificial Intelligence System to Predict Response to Neoadjuvant Chemo-Immunotherapy in Locally Advanced Gastric Cancer (The PRISM-GC Study)

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

2000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Anhui
      • Fuyang, Anhui, China, 050011
        • Recruiting
        • The Fifth Affiliated Hospital of Anhui Medical University
        • Contact:
    • Hebei
      • Cangzhou, Hebei, China, 050011
        • Recruiting
        • Cangzhou People's Hospital
        • Contact:
      • Hengshui, Hebei, China, 053099
      • Xingtai, Hebei, China, 050011
        • Recruiting
        • The Second Affiliated Hospital of Xingtai Medical College
        • Contact:
    • Hubei
      • Wuhan, Hubei, China, 430065
        • Recruiting
        • Renmin Hospital of Wuhan University
        • Contact:
      • Yichang, Hubei, China, 448000
        • Recruiting
        • Yichang Central Hospital
        • Contact:
    • None Selected
      • Baoding, None Selected, China, 050011
        • Recruiting
        • Baoding Central Hospital
        • Contact:
      • Shijiazhuang, None Selected, China, 050011
        • Recruiting
        • The Fourth Hospital of Hebei Medical University
        • Contact:
      • Shijiazhuang, None Selected, China, 050011

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Adult patients with locally advanced gastric cancer who are admitted to the participating centers and are scheduled to undergo neoadjuvant chemo-immunotherapy according to real-world clinical practice.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
LAGC Pan-Immunotherapy Cohort
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.
Drug: Standard of Care PD-1 Inhibitors
Patients receive standard neoadjuvant chemotherapy (e.g., SOX or XELOX regimen) combined with any NMPA-approved PD-1 inhibitor (including but not limited to Sintilimab, Tislelizumab, Camrelizumab, etc.) as determined by the treating physician in real-world practice.
Diagnostic test: Multimodal AI Assessment
Non-invasive assessment using a multimodal deep learning system (DeepComp) to analyze preoperative contrast-enhanced CT images and pathological slides. The AI model predicts the probability of pathological complete response (pCR) but does not alter the clinical treatment plan.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Pathological Complete Response (pCR) Rate
Time Frame: At the time of postoperative pathological evaluation (approximately 1 month after surgery)
Defined as the complete absence of viable tumor cells in the resected specimen (primary tumor and lymph nodes, ypT0N0), assessed according to standard pathological guidelines (TRG 0). This outcome measures the real-world efficacy of neoadjuvant chemo-immunotherapy across the cohort.
At the time of postoperative pathological evaluation (approximately 1 month after surgery)
Predictive Accuracy of the Multimodal AI Model for Pathological Complete Response (pCR)
Time Frame: From baseline assessment to postoperative pathological evaluation (approximately 5 months)
The performance of the DeepComp AI model in predicting pCR will be evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC). The model's predictions (based on preoperative baseline CT and pathology slides) will be compared with the ground truth postoperative pathological results. Secondary metrics including sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) will also be calculated.
From baseline assessment to postoperative pathological evaluation (approximately 5 months)

Secondary Outcome Measures

Outcome Measure
Time Frame
3-Year Disease-Free Survival (DFS)
Time Frame: 3 years post-surgery
3 years post-surgery

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Qun Zhao

Collaborators

Shijiazhuang People's Hospital
Hengshui People's Hospital
Baoding Central Hospital
Wuhan University Affiliated People's Hospital
The Fifth Affiliated Hospital of Anhui Medical University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

February 5, 2026

Primary Completion (Estimated)

December 30, 2027

Study Completion (Estimated)

December 30, 2027

Study Registration Dates

First Submitted

February 3, 2026

First Submitted That Met QC Criteria

February 3, 2026

First Posted (Actual)

February 10, 2026

Study Record Updates

Last Update Posted (Actual)

May 15, 2026

Last Update Submitted That Met QC Criteria

May 13, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • PRISM-GC-001

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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