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

Официальное название

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)

Обзор

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.

Вмешательства

• Препарат 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.
• Диагностический тест 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.

Первичные конечные точки

• Predictive Accuracy of the Multimodal AI Model for Pathological Complete Response (pCR) [Срок оценки: From baseline assessment to postoperative pathological evaluation (approximately 5 months)]
• Pathological Complete Response (pCR) Rate [Срок оценки: At the time of postoperative pathological evaluation (approximately 1 month after surgery)]

Вторичные конечные точки (1)

• 3-Year Disease-Free Survival (DFS) [Срок оценки: 3 years post-surgery]

Критерии участия

Критерии включения

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.

Критерии исключения

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.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Китай · 9 центров

• The Fifth Affiliated Hospital of Anhui Medical University — Fuyang
• Cangzhou People's Hospital — Cangzhou
• Hengshui People's Hospital — Hengshui
• The Second Affiliated Hospital of Xingtai Medical College — Xingtai
• Renmin Hospital of Wuhan University — Ухань
• Yichang Central Hospital — Yichang
• Baoding Central Hospital — Baoding
• Shijiazhuang People's Hospital — Shijiazhuang
• … и ещё 1 центр

Идентификаторы

NCT: NCT07401199 · PRISM-GC-001

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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