Gastric Cancer (GC)NCT07401199
Multimodal AI for Predicting Response to Neoadjuvant Immunotherapy in Gastric Cancer (PRISM-GC)
An observational study of Gastric Cancer (GC) and Locally Advanced Gastric Cancer, run by Qun Zhao at 9 sites in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Gastric Cancer (GC), Locally Advanced Gastric Cancer
- What is being tested?
- Multimodal AI Assessment
- How many people
- 2,000 participants
- Where
- 9 sitesChina
- Study dates
- 5 Feb 2026to 30 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
9 sites in 1 country. Contact the study team or a site near you to ask about taking part.
9 sites in 1 country
China9 sites, 9 recruiting
- Recruiting
Baoding Central Hospital
Baoding, None Selected, 050011
Xiaolong Li, 033486223422, hh185496959@126.com
- Recruiting
Cangzhou People's Hospital
Cangzhou, Hebei, 050011
Kaixuan Gao, 031186095363, 790806885@qq.com
- Recruiting
The Fifth Affiliated Hospital of Anhui Medical University
Fuyang, Anhui, 050011
Yanlong Shi, 031186095363, yan_long_shi@163.com
- Recruiting
Hengshui People's Hospital
Hengshui, Hebei, 053099
Zhenjiang Guo, 031186095363, guo_zhen_jiang123@163.com
- Recruiting
Shijiazhuang People's Hospital
Shijiazhuang, None Selected, 050011
Ning Meng, buezasessiany@outlook.com
- Recruiting
the Fourth Hospital of Hebei Medical University
Shijiazhuang, None Selected, 050011
Qun Zhao, 031186095363, zhaoqun@hebmu.edu.cn
- Recruiting
Renmin Hospital of Wuhan University
Wuhan, Hubei, 430065
Lilong Zhang, 031186095363, hb19843362@163.com
- Recruiting
The Second Affiliated Hospital of Xingtai Medical College
Xingtai, Hebei, 050011
Yongli Chen, 031186095363, chen_yong_li888@163.com
- Recruiting
Yichang Central Hospital
Yichang, Hubei, 448000
Wen Xu, 031186095363, xu_wen_man@163.com
About this study
Official title: 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)
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.
- Being tested
- Standard of Care PD-1 Inhibitors, Multimodal AI Assessment
- Keywords
- Neoadjuvant Immunotherapy, Artificial Intelligence, Deep Learning, Pathological Complete Response, PD-1 Inhibitors
- Medical subject headings
- Stomach Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Standard of Care PD-1 InhibitorsDrug
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.
Multimodal AI AssessmentDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Drug: Standard of Care PD-1 Inhibitors; Diagnostic Test: Multimodal AI Assessment |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: At the time of postoperative pathological evaluation (approximately 1 month after surgery)
Secondary outcomes (1)
3-Year Disease-Free Survival (DFS)
Measured: 3 years post-surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Feb 2026
- Study start5 Feb 2026
- Primary completion30 Dec 2027 (estimated)
- Study completion30 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastric Cancer (GC)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Qun Zhao
- Sponsor type
- University, hospital or other
- Collaborators
- 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
- Responsible party
- Sponsor-investigator, Qun Zhao, Hebei Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07401199
- Sponsor's protocol ID
- PRISM-GC-001
- Status last verified
- May 2026
- First submitted
- 3 Feb 2026
- Last updated
- 15 May 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 15 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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)
- ClinicalTrials.gov ID
- NCT07401199
- Acronym / sponsor ID
- PRISM-GC-001
- Keywords
- Neoadjuvant Immunotherapy, Artificial Intelligence, Deep Learning, Pathological Complete Response, PD-1 Inhibitors
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Qun ZhaoUniversity, hospital or other
- Collaborators
- 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 start
- 5 Feb 2026 (actual)
- Primary completion
- 30 Dec 2027 (estimated)
- Study completion
- 30 Dec 2027 (estimated)
- First posted
- 10 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| LAGC Pan-Immunotherapy Cohort | – | Drug: Standard of Care PD-1 Inhibitors, Diagnostic Test: Multimodal AI AssessmentPatients 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (8 listed)
- 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.
Key exclusion criteria (7 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Standard of Care PD-1 InhibitorsPatients 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. | Drug | LAGC Pan-Immunotherapy Cohort |
| Multimodal AI AssessmentNon-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. | Diagnostic test | LAGC Pan-Immunotherapy Cohort |
Primary outcome
Predictive Accuracy of the Multimodal AI Model for Pathological Complete Response (pCR)
Time frame: 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
Time frame: 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.
1 secondary outcome
3-Year Disease-Free Survival (DFS)
Time frame: 3 years post-surgery
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 May 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
9 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gastric Cancer (GC) and Locally Advanced Gastric Cancer, run by Qun Zhao at 9 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 15 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 9 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Qun Zhao (+8631186095363, zhaoqun@hebmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Predictive Accuracy of the Multimodal AI Model for Pathological Complete Response (pCR) (time frame: From baseline assessment to postoperative pathological evaluation (approximately 5 months)). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 May 2026.