[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"Qun Zhao\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":151},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,6,0,[8,45,69,91,113,133],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":18,"targetDuration":4,"studyType":21,"phases":4,"briefSummary":22,"conditions":23,"keywords":26,"overallStatus":32,"whyStopped":4,"lastUpdateSubmitDate":33,"lastUpdatePostDateStruct":34,"startDateStruct":37,"completionDateStruct":39,"leadSponsor":41,"locationsCount":44},"100623789","multimodal-ai-for-predicting-response-to-neoadjuvant-immunotherapy-in-gastric-cancer-prism-gc-100623789",false,"NCT07401199","Multimodal AI for Predicting Response to Neoadjuvant Immunotherapy in Gastric Cancer (PRISM-GC)","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)","Inclusion Criteria:\n\nAge ≥ 18 years.\n\nHistologically confirmed gastric or gastroesophageal junction adenocarcinoma.\n\nClinical stage cT3-4a, N+, M0 (locally advanced) assessed by CT\u002FMRI and endoscopic ultrasound.\n\nScheduled to receive neoadjuvant chemotherapy combined with PD-1 inhibitors (regimens including but not limited to SOX\u002FXELOX + Sintilimab\u002FTislelizumab\u002FCamrelizumab, etc.) as standard of care.\n\nAvailability of standard pre-treatment contrast-enhanced abdominal CT images.\n\nWillingness to provide peripheral blood samples and tumor tissue (biopsy\u002Fsurgical) for sequencing and analysis.\n\nECOG performance status 0-1.\n\nAdequate organ function to tolerate systemic chemotherapy.\n\nExclusion Criteria:\n\nEvidence of distant metastasis (Stage IV) or unresectable disease.\n\nPrevious systemic anti-tumor therapy for gastric cancer (chemotherapy, radiotherapy, or immunotherapy).\n\nHistory of other malignancies within the past 5 years.\n\nActive autoimmune diseases requiring systemic immunosuppressive treatment (contraindication for PD-1 inhibitors).\n\nEmergency surgery due to obstruction, perforation, or uncontrolled bleeding.\n\nSevere metallic artifacts on CT images that interfere with radiomic feature extraction.\n\nPregnancy or lactation.","ALL","18 Years",{"count":19,"type":20},2000,"ESTIMATED","OBSERVATIONAL","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.\n\nThe 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.\n\nResearchers 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.",[24,25],"Gastric Cancer (GC)","Locally Advanced Gastric Cancer",[27,28,29,30,31],"Neoadjuvant Immunotherapy","Artificial Intelligence","Deep Learning","Pathological Complete Response","PD-1 Inhibitors","RECRUITING","2026-05-13",{"date":35,"type":36},"2026-05-15","ACTUAL",{"date":38,"type":36},"2026-02-05",{"date":40,"type":20},"2027-12-30",{"name":42,"class":43},"Qun Zhao","OTHER",9,{"id":46,"slug":47,"hasResults":11,"nctId":48,"briefTitle":49,"officialTitle":50,"acronym":4,"eligibilityCriteria":51,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":52,"enrollmentInfo":53,"targetDuration":55,"studyType":21,"phases":4,"briefSummary":56,"conditions":57,"keywords":4,"overallStatus":32,"whyStopped":4,"lastUpdateSubmitDate":60,"lastUpdatePostDateStruct":61,"startDateStruct":63,"completionDateStruct":65,"leadSponsor":67,"locationsCount":68},"100623787","deepcomp-for-prediction-of-gastric-cancer-postoperative-complications-deepcomp-prospective-100623787","NCT07401173","DeepComp for Prediction of Gastric Cancer Postoperative Complications (DeepComp-Prospective)","A Prospective, Multicenter, Observational Study Validating the Multimodal Deep Learning Radiomics Model (DeepComp) for Preoperative Prediction of Major Postoperative Complications in Patients With Gastric Cancer","Inclusion Criteria:\n\nAge ≥ 18 years.\n\nHistologically confirmed gastric adenocarcinoma.\n\nScheduled for elective radical gastrectomy (open, laparoscopic, or robotic) with curative intent.\n\nStandard preoperative contrast-enhanced abdominal CT scans (venous phase) performed within 14 days prior to surgery.\n\nWillingness to sign informed consent.\n\nExclusion Criteria:\n\nEmergency surgery due to perforation, obstruction, or massive bleeding.\n\nIntraoperative findings of distant metastasis (Stage IV) or unresectable disease preventing R0 resection.\n\nConcurrent or previous malignant tumors within the last 5 years (except gastric cancer).\n\nPregnancy or lactation.\n\nSevere metallic artifacts on CT images preventing radiomic analysis.","85 Years",{"count":54,"type":20},500,"30 Days","Gastric cancer is a leading cause of cancer-related mortality, and radical surgery remains the primary treatment. However, postoperative complications are common and can significantly impact patient recovery and quality of life. Currently, doctors lack precise tools to accurately predict which patients are at high risk for developing severe complications before surgery.\n\nThis study aims to validate a novel artificial intelligence (AI) model called \"DeepComp.\" The DeepComp model integrates clinical data with advanced radiomic features derived from routine preoperative CT scans. Specifically, it analyzes both the tumor characteristics and the patient's body composition (including skeletal muscle and fat distribution) to assess physiological reserve.\n\nIn this prospective, multicenter observational study, researchers will enroll patients scheduled for gastric cancer surgery across five medical centers. The DeepComp model will be used to predict the risk of moderate-to-severe postoperative complications (Clavien-Dindo grade II or higher). These predictions will then be compared with the actual clinical outcomes observed 30 days after surgery. The goal is to determine the accuracy and reliability of the DeepComp model in a real-world clinical setting, potentially providing a powerful tool for personalized surgical risk assessment.",[58,59],"Gastric Cancer (Diagnosis)","Postoperative Complications","2026-04-06",{"date":62,"type":36},"2026-04-09",{"date":64,"type":36},"2026-03-01",{"date":66,"type":20},"2026-05-01",{"name":42,"class":43},1,{"id":70,"slug":71,"hasResults":11,"nctId":72,"briefTitle":73,"officialTitle":74,"acronym":4,"eligibilityCriteria":75,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":76,"targetDuration":78,"studyType":21,"phases":4,"briefSummary":79,"conditions":80,"keywords":82,"overallStatus":32,"whyStopped":4,"lastUpdateSubmitDate":83,"lastUpdatePostDateStruct":84,"startDateStruct":86,"completionDateStruct":88,"leadSponsor":90,"locationsCount":68},"100602533","multimodal-deep-learning-for-lymph-node-metastasis-prediction-and-physician-performance-assessment-in-t1-gastric-cancer-100602533","NCT07124754","Multimodal Deep Learning for Lymph Node Metastasis Prediction and Physician Performance Assessment in T1 Gastric Cancer","Development and Validation of a Multimodal Artificial Intelligence Model for Predicting Lymph Node Metastasis in T1 Gastric Cancer and Its Impact on Physician Diagnostic Performance","Inclusion Criteria:\n\nAge 18 years or older\n\nHistologically confirmed primary gastric adenocarcinoma\n\nClinical stage T1 (T1a or T1b) confirmed by endoscopy and imaging\n\nUndergoing radical gastrectomy with lymph node dissection\n\nPreoperative data available: clinical variables, CT imaging, and pathology slides\n\nWritten informed consent provided\n\nExclusion Criteria:\n\nHistory of other malignancies within the past 5 years\n\nReceived neoadjuvant chemotherapy or radiotherapy\n\nIncomplete clinical or pathological data\n\nPoor quality or missing CT or histopathology images\n\nPatients with distant metastasis (M1) at diagnosis\n\nInability or refusal to provide informed consent",{"count":77,"type":20},300,"36 Months","This study aims to develop and validate an artificial intelligence (AI) model that integrates clinical, pathological, and imaging data to predict the presence of lymph node metastasis (LNM) in patients with T1-stage gastric cancer.\n\nThe study will also compare the diagnostic performance of physicians with and without AI assistance, including clinicians with varying levels of experience.\n\nThe goal is to improve early decision-making and support more personalized treatment strategies for patients with early gastric cancer.",[81],"T1 Gastric Cancer Lymph Node Metastasis Early Gastric Cancer Artificial Intelligence-Assisted Diagnosis Multimodal Data Integration",[81],"2025-08-14",{"date":85,"type":36},"2025-08-15",{"date":87,"type":36},"2025-01-01",{"date":89,"type":20},"2025-12-30",{"name":42,"class":43},{"id":92,"slug":93,"hasResults":11,"nctId":94,"briefTitle":95,"officialTitle":95,"acronym":96,"eligibilityCriteria":97,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":98,"enrollmentInfo":99,"targetDuration":100,"studyType":21,"phases":4,"briefSummary":101,"conditions":102,"keywords":4,"overallStatus":104,"whyStopped":4,"lastUpdateSubmitDate":105,"lastUpdatePostDateStruct":106,"startDateStruct":108,"completionDateStruct":110,"leadSponsor":112,"locationsCount":4},"100582079","development-of-a-predictive-model-for-gastric-cancer-peritoneal-metastasis-and-cachexia-using-bub1-and-radiopathomics-data-with-deep-learning-100582079","NCT06858644","Development of a Predictive Model for Gastric Cancer Peritoneal Metastasis and Cachexia Using BUB1 and Radiopathomics Data With Deep Learning","BUDDLE","Inclusion Criteria:\n\nAdults aged 18-75 years diagnosed with gastric cancer (GC) at any stage. Histopathologically confirmed GC with available radiological (CT\u002FMRI) and pathological data (biopsy samples).\n\nPatients with or at risk of peritoneal metastasis and\u002For cachexia, as determined by clinical assessment and imaging.\n\nAbility to provide informed consent and comply with study protocols. Willingness to undergo regular follow-up imaging and clinical evaluation for the duration of the study.\n\nExclusion Criteria:\n\nPatients with other primary cancers or serious comorbidities (e.g., severe cardiovascular disease, uncontrolled diabetes).\n\nPregnant or breastfeeding women. Patients with contraindications to MRI or CT imaging. Those with insufficient clinical data (e.g., missing radiopathological information) for model training.\n\nPatients who are unable or unwilling to comply with the study protocol, including follow-up visits and evaluations.","75 Years",{"count":54,"type":20},"5 Years","This clinical trial aims to develop a predictive model for gastric cancer (GC) peritoneal metastasis and cachexia by integrating BUB1 gene data with radiological and pathological data using advanced deep learning techniques. The study will focus on utilizing imaging genomics (radiomics) and histopathological data to identify early biomarkers for peritoneal metastasis and cachexia in GC patients. By leveraging deep learning algorithms, the project seeks to improve the accuracy and reliability of predictions, enabling earlier intervention and personalized treatment strategies. The ultimate goal is to enhance clinical decision-making and prognosis prediction in GC patients with peritoneal metastasis and cachexia.",[103],"Gastric (Stomach) Cancer","NOT_YET_RECRUITING","2025-02-27",{"date":107,"type":36},"2025-03-05",{"date":109,"type":20},"2025-03-01",{"date":111,"type":20},"2027-03-01",{"name":42,"class":43},{"id":114,"slug":115,"hasResults":11,"nctId":116,"briefTitle":117,"officialTitle":118,"acronym":4,"eligibilityCriteria":119,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":120,"targetDuration":4,"studyType":21,"phases":4,"briefSummary":122,"conditions":123,"keywords":4,"overallStatus":32,"whyStopped":4,"lastUpdateSubmitDate":125,"lastUpdatePostDateStruct":126,"startDateStruct":128,"completionDateStruct":130,"leadSponsor":132,"locationsCount":68},"100555033","construction-and-application-of-database-of-hebei-provincial-gastric-cancer-collaborative-network-driven-by-artificial-intelligence-technology-100555033","NCT06506825","Construction and Application of Database of Hebei Provincial Gastric Cancer Collaborative Network Driven by Artificial Intelligence Technology","The Fourth Hospital of Hebei Medical University","Inclusion Criteria:\n\nAll patients with gastric cancer\n\nExclusion Criteria:\n\nNone",{"count":121,"type":20},30000,"This clinical trial aims to construct and apply a collaborative network database for gastric cancer in Hebei Province, driven by artificial intelligence (AI) technology. The project seeks to integrate clinical data, genomic information, and treatment outcomes from multiple hospitals and research centers within the region. By leveraging advanced AI algorithms, the database will facilitate comprehensive data analysis to identify novel biomarkers, optimize therapeutic strategies, and improve patient outcomes. This initiative will also support real-time data sharing and collaboration among healthcare providers, researchers, and policymakers, ultimately enhancing the overall management and treatment of gastric cancer in Hebei Province.",[124],"The Study Focuses on Gastric Cancer","2024-07-12",{"date":127,"type":36},"2024-07-18",{"date":129,"type":20},"2024-08-01",{"date":131,"type":20},"2030-12-31",{"name":42,"class":43},{"id":134,"slug":135,"hasResults":11,"nctId":136,"briefTitle":137,"officialTitle":137,"acronym":4,"eligibilityCriteria":138,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":139,"targetDuration":4,"studyType":21,"phases":4,"briefSummary":140,"conditions":141,"keywords":4,"overallStatus":32,"whyStopped":4,"lastUpdateSubmitDate":143,"lastUpdatePostDateStruct":144,"startDateStruct":146,"completionDateStruct":148,"leadSponsor":150,"locationsCount":68},"100552846","machine-learning-driven-noninvasive-screening-of-transcriptomics-liquid-biopsies-for-early-diagnosis-of-occult-peritoneal-metastases-in-locally-advanced-gastric-cancer-100552846","NCT06478394","Machine Learning-driven Noninvasive Screening of Transcriptomics Liquid Biopsies for Early Diagnosis of Occult Peritoneal Metastases in Locally Advanced Gastric Cancer","Inclusion Criteria:\n\nDiagnosis of Locally Advanced Gastric Cancer (LAGC): Patients must have a confirmed diagnosis of locally advanced gastric cancer, as determined by standard diagnostic procedures such as imaging and histopathological examination.\n\nAge: Participants must be adults aged 18 years or older. Consent: Patients must be able to provide informed consent to participate in the study.\n\nAdequate Organ Function: Participants should have adequate bone marrow, liver, and kidney function as defined by specific laboratory criteria (e.g., specific levels of hemoglobin, platelet count, liver enzymes, and creatinine clearance).\n\nPerformance Status: Patients should have an Eastern Cooperative Oncology Group (ECOG) performance status of 0 to 2, indicating they are fully active, restricted in physically strenuous activity but ambulatory, or capable of all self-care but unable to carry out any work activities.\n\nWillingness to Provide Blood Samples: Participants must be willing to provide blood samples at specified time points throughout the study.\n\nPrevious Treatment: Patients who have received prior treatments for gastric cancer (e.g., chemotherapy, radiation therapy, or surgery) may be included, provided there is a sufficient washout period as determined by the study protocol.\n\nExclusion Criteria:\n\n* Distant Metastases: Patients with confirmed distant metastases (beyond the peritoneum) are excluded.\n\nOther Malignancies: Individuals with a history of other malignancies within the past five years, except for adequately treated basal cell or squamous cell skin cancer, or carcinoma in situ of the cervix.\n\nSevere Comorbid Conditions: Patients with severe or uncontrolled comorbid conditions, such as significant cardiovascular disease, uncontrolled diabetes, severe infections, or other conditions that could interfere with the study participation or outcomes.\n\nPregnancy and Lactation: Pregnant or lactating women are excluded due to potential risks to the fetus or infant.\n\nImmunocompromised Status: Patients who are immunocompromised, such as those with HIV\u002FAIDS, or who are receiving immunosuppressive therapy.\n\nConcurrent Participation in Other Clinical Trials: Individuals currently participating in another clinical trial that could interfere with this study's procedures or outcomes.\n\nAllergies to Study Materials: Patients with known allergies to any components of the study materials used for liquid biopsy processing and analysis.\n\nNon-compliance: Individuals deemed unable or unwilling to comply with the study procedures and follow-up requirements.",{"count":77,"type":20},"Brief Summary: Machine Learning-Driven Noninvasive Screening of Transcriptomics Liquid Biopsies for Early Diagnosis of Occult Peritoneal Metastases in Locally Advanced Gastric Cancer\n\nGastric cancer, commonly known as stomach cancer, is a significant health issue worldwide, especially when it progresses to an advanced stage. One of the major challenges in treating locally advanced gastric cancer (LAGC) is the detection of occult (hidden) peritoneal metastases. These metastases are cancer cells that spread to the peritoneum (the lining of the abdominal cavity) but are not easily detectable with standard imaging techniques or during surgery. Early and accurate detection of these hidden metastases can significantly improve treatment strategies and outcomes for patients.\n\nThis clinical study explores an innovative approach to tackle this problem using machine learning (ML) technology and liquid biopsies. Liquid biopsies are a noninvasive method that involves analyzing blood samples to detect cancer-related biomarkers, such as circulating tumor DNA or RNA. This study specifically focuses on the transcriptomics of liquid biopsies, which refers to the analysis of RNA molecules to understand the gene expression profiles associated with cancer.\n\nHypothesis\n\nThe hypothesis of this study is that machine learning algorithms can effectively analyze transcriptomics data from liquid biopsies to detect occult peritoneal metastases in patients with locally advanced gastric cancer. By doing so, this method could provide a noninvasive, accurate, and early diagnosis of metastases, which are otherwise difficult to identify through traditional methods.\n\nStudy Design\n\n1. Participants: The study will enroll patients diagnosed with locally advanced gastric cancer. These patients will undergo standard diagnostic and staging procedures to confirm their cancer stage and overall health status.\n2. Sample Collection: Blood samples will be collected from the participants at various stages of their treatment journey. These samples will be processed to extract RNA, which will then be analyzed to obtain transcriptomic data.\n3. Machine Learning Analysis: Advanced machine learning algorithms will be employed to analyze the transcriptomic data from the liquid biopsies. The algorithms will be trained to identify patterns and markers associated with occult peritoneal metastases. The models will be continuously refined and validated using a subset of the collected data to ensure accuracy and reliability.\n4. Comparison with Traditional Methods: The results of the machine learning analysis will be compared with the outcomes of traditional diagnostic methods, such as imaging and surgical examinations, to evaluate the effectiveness of the ML-driven approach.\n5. Outcome Measures: The primary outcome measure will be the accuracy of the machine learning models in detecting occult peritoneal metastases compared to traditional methods. Secondary measures will include the impact of early detection on treatment decisions, patient outcomes, and overall survival rates.\n\nSignificance\n\nEarly and accurate detection of occult peritoneal metastases in locally advanced gastric cancer is crucial for effective treatment planning. Traditional diagnostic methods often fail to identify these hidden metastases until they have progressed, limiting the treatment options and adversely affecting patient prognosis. By leveraging machine learning technology to analyze transcriptomics data from liquid biopsies, this study aims to develop a noninvasive and reliable screening tool that can detect these metastases at an earlier stage.\n\nSuch an advancement could lead to several benefits, including:\n\n* Improved Treatment Planning: Early detection allows for more tailored and effective treatment strategies, potentially including more aggressive therapies or surgical interventions when necessary.\n* Better Patient Outcomes: With earlier and more accurate diagnosis, patients have a higher chance of receiving timely and appropriate treatments, which can improve survival rates and quality of life.\n* Noninvasive Screening: Liquid biopsies are less invasive than traditional biopsy methods, reducing the physical and psychological burden on patients.\n* Cost-Effectiveness: Early detection and treatment can potentially reduce the overall cost of care by preventing the need for more extensive and expensive treatments at later stages of the disease.\n\nConclusion\n\nThis clinical study represents a promising step forward in the fight against gastric cancer. By integrating machine learning with noninvasive liquid biopsy techniques, it aims to provide a new tool for the early detection of occult peritoneal metastases, ultimately improving outcomes for patients with locally advanced gastric cancer. The success of this study could pave the way for broader applications of machine learning in cancer diagnostics and personalized medicine.",[142],"1. Locally Advanced Gastric Cancer (LAGC): The Primary Condition Under Investigation is Locally Advanced Gastric Cancer, Which Refers to Stomach","2024-06-22",{"date":145,"type":36},"2024-06-27",{"date":147,"type":36},"2024-01-30",{"date":149,"type":20},"2025-12-31",{"name":42,"class":43},""]