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)
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 2,000
- 试验地点
- 9
- 主要终点
- Predictive Accuracy of the Multimodal AI Model (DeepComp) for Pathological Complete Response (pCR)
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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-
- •Adequate organ function to tolerate systemic chemotherapy.
排除标准
- •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.
研究组 & 干预措施
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.
干预措施: Standard of Care PD-1 Inhibitors (Drug)
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.
干预措施: Multimodal AI Assessment (Diagnostic Test)
结局指标
主要结局
Predictive Accuracy of the Multimodal AI Model (DeepComp) for Pathological Complete Response (pCR)
时间窗: 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
时间窗: 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.
次要结局
- 3-Year Disease-Free Survival (DFS)(3 years post-surgery)
研究者
Qun Zhao
Professor
Hebei Medical University