Last reviewed · How we verify
NCT07401199
Multimodal AI for Predicting Response to Neoadjuvant Immunotherapy in Gastric Cancer (PRISM-GC)
trial testing Standard of Care PD-1 Inhibitors in Gastric Cancer (GC) in 2,000 participants. Not yet recruiting.
30 December 2026
Quick facts
| Lead sponsor | Qun Zhao |
|---|---|
| Status | Not yet recruiting |
| Study type | OBSERVATIONAL |
| Enrollment | 2,000 |
| Start date | 5 February 2026 |
| Primary completion | 30 December 2026 |
| Estimated completion | 30 December 2026 |
Drugs / interventions tested
- Standard of Care PD-1 Inhibitors — full drug profile →
- Multimodal AI Assessment
Conditions studied
- Gastric Cancer (GC) — all drugs for Gastric Cancer (GC) →
- Locally Advanced Gastric Cancer — all drugs for Locally Advanced Gastric Cancer →
Sponsor
Qun Zhao — full company profile →
Who can join
18 and older, any sex, with Gastric Cancer (GC) or Locally Advanced Gastric Cancer. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
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.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
Verify or expand the search:
- PubMed search for NCT07401199
- Europe PMC full search
- ASCO Meeting Library
- ESMO Meeting Library
- bioRxiv preprints
- medRxiv preprints
- Google Scholar
Related trials
Other recruiting trials for Gastric Cancer (GC)
Currently open trials in the same condition.
- NCT07522320 — Costs of Opportunistic Upper Gastrointestinal Endoscopy and the Economic Burden of Gastric Cancer Management · active not recruiting
- NCT07311408 — SHR-1701 + Rivoceranib (± SHR-2554) in Advanced GC After First-Line Immunotherapy Failure · Phase 2 · recruiting
- NCT07366528 — Adjuvant Oxaliplatin Plus S-1 Versus Docetaxel Plus S-1 for Stage III Gastric Cancer · Phase 3 · recruiting
- NCT07313579 — Hyperthermic Intraperitoneal Chemoperfusion (HIPEC) in Gastric Cancer · recruiting
- NCT07217704 — Using 18F-FAPI PET to Detect Metastatic Disease in Patients That Have Gastric or Esophageal Cancer. · Phase 3 · recruiting
Other Qun Zhao trials
Trials by the same sponsor.
- NCT07401173 — DeepComp for Prediction of Gastric Cancer Postoperative Complications (DeepComp-Prospective) · recruiting
- NCT06858644 — Development of a Predictive Model for Gastric Cancer Peritoneal Metastasis and Cachexia Using BUB1 and Radiopathomics Da · not yet recruiting
- NCT07124754 — Multimodal Deep Learning for Lymph Node Metastasis Prediction and Physician Performance Assessment in T1 Gastric Cancer · recruiting
- NCT06957678 — AI-Based Prediction of Lymph Node Metastasis in Gastric Cancer Using Preoperative Multimodal Data · enrolling by invitation
- NCT06947096 — Radiomics-Based AI Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer Patients · enrolling by invitation
Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT07401199 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Qun Zhao
- Last refreshed: 10 February 2026
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT07401199.
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing