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

NCT07401199 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 2000

Last updated 2026-05-15

No results posted yet for this study

Summary

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.

Conditions

  • Gastric Cancer (GC)
  • Locally Advanced Gastric Cancer

Interventions

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.

Sponsors & Collaborators

  • Shijiazhuang People's Hospital

    collaborator OTHER

  • Baoding Central Hospital

    collaborator UNKNOWN

  • Hengshui People's Hospital

    collaborator OTHER

  • Wuhan University Affiliated People's Hospital

    collaborator UNKNOWN

  • The Fifth Affiliated Hospital of Anhui Medical University

    collaborator UNKNOWN

  • Qun Zhao

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-02-05
Primary Completion
2027-12-30
Completion
2027-12-30

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07401199 on ClinicalTrials.gov