Gastric Cancer (GC), Locally Advanced Gastric Cancer
Conditions
Keywords
Neoadjuvant Immunotherapy, Artificial Intelligence, Deep Learning, Pathological Complete Response, PD-1 Inhibitors
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.
Interventions
DRUGStandard 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_TESTMultimodal 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
Qun Zhao
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
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Predictive Accuracy of the Multimodal AI Model for Pathological Complete Response (pCR) | 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. |
| Pathological Complete Response (pCR) Rate | 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. |
Secondary
| Measure | Time frame |
|---|---|
| 3-Year Disease-Free Survival (DFS) | 3 years post-surgery |
Countries
China
Contacts
CONTACTQun Zhao
Outcome results
None listed