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

NCT07401199 | Recruiting DMC

• 1 other identifier: PRISM-GC-001
• Study Type: observational
• Target: 2,000
• Locations: 1 country
• Sites: 9

Brief 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

Keywords

Neoadjuvant Immunotherapy; Artificial Intelligence; Deep Learning; Pathological Complete Response; PD-1 Inhibitors

Outcome Measures

Primary Outcomes (2)

• Predictive Accuracy of the Multimodal AI Model for Pathological Complete Response (pCR)

  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)

• Pathological Complete Response (pCR) Rate

  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)

Secondary Outcomes (1)

• 3-Year Disease-Free Survival (DFS)

  3 years post-surgery

Study Arms (1)

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; Diagnostic Test: Multimodal AI Assessment

Interventions

Standard of Care PD-1 Inhibitors DRUG

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.

LAGC Pan-Immunotherapy Cohort

Multimodal AI Assessment DIAGNOSTIC_TEST

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.

LAGC Pan-Immunotherapy Cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Qun Zhao: lead
• Shijiazhuang People's Hospital: collaborator
• Baoding Central Hospital: collaborator
• Hengshui People's Hospital: collaborator
• Wuhan University Affiliated People's Hospital: collaborator
• The Fifth Affiliated Hospital of Anhui Medical University: collaborator

Study Sites (9)

The Fifth Affiliated Hospital of Anhui Medical University

Fuyang, Anhui, 050011, China

RECRUITING

Cangzhou People's Hospital

Cangzhou, Hebei, 050011, China

RECRUITING

Hengshui People's Hospital

Hengshui, Hebei, 053099, China

RECRUITING

The Second Affiliated Hospital of Xingtai Medical College

Xingtai, Hebei, 050011, China

RECRUITING

Renmin Hospital of Wuhan University

Wuhan, Hubei, 430065, China

RECRUITING

Yichang Central Hospital

Yichang, Hubei, 448000, China

RECRUITING

Baoding Central Hospital

Baoding, None Selected, 050011, China

RECRUITING

Shijiazhuang People's Hospital

Shijiazhuang, None Selected, 050011, China

RECRUITING

the Fourth Hospital of Hebei Medical University

Shijiazhuang, None Selected, 050011, China

RECRUITING

Biospecimen

Retention: SAMPLES WITH DNA

Tumor tissue samples (including preoperative biopsy and postoperative surgical resection specimens) and matched peripheral blood samples will be retained. These specimens will be processed for DNA/RNA extraction to perform Next-Generation Sequencing (NGS) and multi-omics analysis. The goal is to identify molecular biomarkers and genetic alterations associated with sensitivity or resistance to neoadjuvant immunotherapy.

MeSH Terms

Conditions

Stomach Neoplasms

Condition Hierarchy (Ancestors)

Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Stomach Diseases

Central Study Contacts

Qun Zhao

CONTACT

+8631186095363; zhaoqun@hebmu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: February 3, 2026
• First Posted: February 10, 2026
• Study Start: February 5, 2026
• Primary Completion (Estimated): December 30, 2027
• Study Completion (Estimated): December 30, 2027
• Last Updated: May 15, 2026

Record last verified: 2026-05

Locations

CN (9)