Ignite Creation Date:
2026-03-26 @ 3:16 PM
Ignite Modification Date:
2026-03-30 @ 1:32 AM
Study NCT ID:
NCT07439757
Status:
RECRUITING
Last Update Posted:
2026-02-27
First Post:
2026-02-23
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI-Powered Precision Decision-Making for Pancreatic Diseases
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D010190', 'term': 'Pancreatic Neoplasms'}, {'id': 'D000077779', 'term': 'Pancreatic Intraductal Neoplasms'}, {'id': 'D050500', 'term': 'Pancreatitis, Chronic'}, {'id': 'D007516', 'term': 'Adenoma, Islet Cell'}, {'id': 'D010195', 'term': 'Pancreatitis'}], 'ancestors': [{'id': 'D004067', 'term': 'Digestive System Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D004701', 'term': 'Endocrine Gland Neoplasms'}, {'id': 'D004066', 'term': 'Digestive System Diseases'}, {'id': 'D010182', 'term': 'Pancreatic Diseases'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}, {'id': 'D018299', 'term': 'Neoplasms, Ductal, Lobular, and Medullary'}, {'id': 'D009375', 'term': 'Neoplasms, Glandular and Epithelial'}, {'id': 'D009370', 'term': 'Neoplasms by Histologic Type'}, {'id': 'D002908', 'term': 'Chronic Disease'}, {'id': 'D020969', 'term': 'Disease Attributes'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}, {'id': 'D000236', 'term': 'Adenoma'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 2000}, 'targetDuration': '1 Year', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2026-03-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-02', 'completionDateStruct': {'date': '2029-10-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-02-23', 'studyFirstSubmitDate': '2026-02-23', 'studyFirstSubmitQcDate': '2026-02-23', 'lastUpdatePostDateStruct': {'date': '2026-02-27', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-02-27', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2029-10-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Classification accuracy', 'timeFrame': 'From date of contrast-enhanced CT scan to 1 year', 'description': 'The percentage of cases correctly classified by AI out of the total number of cases.'}], 'secondaryOutcomes': [{'measure': 'Agreement rate with clinical decisions', 'timeFrame': 'From date of contrast-enhanced CT scan to 1 year', 'description': 'The proportion of total cases where AI and clinician classification results are in agreement.'}, {'measure': 'Percentage decrease in unnecessary surgical procedures', 'timeFrame': 'From date of contrast-enhanced CT scan to 1 year', 'description': 'The percentage reduction in the unnecessary surgery rate achieved by AI decision-making compared to traditional decision-making.'}, {'measure': 'Malignancy miss rate', 'timeFrame': 'From date of contrast-enhanced CT scan to 1 year', 'description': 'The proportion of cases classified by AI as non-surgical that actually required surgery.'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Artificial Intelligence (AI), Deep Learning, Contrast-Enhanced CT, Multicenter Clinical Trial, Real-World Study'], 'conditions': ['Pancreatic Cancer', 'Diagnose Disease', 'IPMN, Pancreatic', 'Pancreatic Cystic Lesions', 'Chronic Pancreatitis', 'Pancreatic Neuroendocrine Tumor', 'Acute Pancreatitis (AP)']}, 'descriptionModule': {'briefSummary': "This multicenter clinical trial evaluates an artificial intelligence (AI) system designed to assist in the diagnosis and management of pancreatic diseases. Using contrast-enhanced CT scans, the study compares the AI's recommendations against the decisions of experienced clinicians to verify the system's accuracy and safety in a real-world setting. Patients are categorized into three management groups: Intervention (surgery/treatment), Intensive Surveillance (close monitoring), or Routine Surveillance (standard follow-up). The primary goal is to determine if the AI system can reliably classify patients, reduce the risk of missing malignant lesions, and prevent unnecessary surgeries, thereby improving clinical decision-making for pancreatic conditions.", 'detailedDescription': 'MEHTOD： This multicenter clinical trial evaluates the reliability and effectiveness of an AI system for patients with pancreatic diseases in a real-world clinical environment. The study calculates the AI system\'s classification accuracy using pathological diagnosis (biopsy/surgery results) or long-term follow-up as the "gold standard" for comparison. Additionally, the safety and clinical utility of the management strategies recommended by the AI are assessed by measuring the risk of missing malignant lesions, the rate of unnecessary surgeries for pancreatic diseases, and the level of agreement with traditional clinical decisions.\n\nSTUDY DESIGN\n\nAll contrast-enhanced CT images from patients with pancreatic diseases are analyzed by the AI system to generate a classification result (Intervention, Intensive Surveillance, or Routine Surveillance). Simultaneously, clinical doctors review the same data and categorize patients into these three groups to determine their actual care plan:\n\n1. INTERVENTION: Patients assessed by doctors as needing "Intervention" are recommended for further surgical evaluation or treatment.\n2. INTENSIVE SURVEILLANCE: Patients assessed by doctors as needing "Intensive Surveillance" receive a personalized, high-frequency follow-up plan until the study endpoint.\n3. ROUTINE SURVEILLANCE: Patients assessed by doctors as needing "Routine Surveillance" undergo follow-up for at least one year. If abnormalities arise during this period, the patient is transferred to the appropriate "Intervention" or "Intensive Surveillance" protocol.\n\nOUTCOMES： The study compares the performance of the AI system against clinical doctors regarding classification accuracy, the risk of missed diagnoses, unnecessary surgery rates, and decision consistency. These metrics are used to validate the AI system\'s value, safety, and utility in the clinical management of pancreatic diseases.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '80 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The study enrolls patients with clinically suspected pancreatic disease who have available contrast-enhanced CT scans and provide informed consent. Patients are excluded if they have a history of pancreatic surgery, contraindications to contrast media, suboptimal image quality, or other conditions deemed unsuitable by the investigator (e.g., pregnancy, cognitive impairment, or concurrent trial participation).', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Clinically suspected pancreatic disease.\n* Scheduled to undergo contrast-enhanced CT.\n* Signed informed consent form indicating agreement to participate.\n\nExclusion Criteria:\n\n* History of pancreatic surgery.\n* Contraindications to contrast-enhanced CT, including known hypersensitivity to iodinated contrast media or severe renal/hepatic dysfunction.\n* Suboptimal image quality affecting diagnosis.\n* Concurrent participation in another interventional clinical trial.\n* Unsuitability for participation as determined by the investigator, including but not limited to: pregnancy or lactation, severe psychiatric disorders or cognitive impairment, significant comorbidities that may interfere with study results or patient safety.'}, 'identificationModule': {'nctId': 'NCT07439757', 'briefTitle': 'AI-Powered Precision Decision-Making for Pancreatic Diseases', 'organization': {'class': 'OTHER', 'fullName': 'Changhai Hospital'}, 'officialTitle': 'A Multicenter Clinical Study on AI-Powered Precision Decision-Making Management for Pancreatic Diseases Using Contrast-Enhanced CT', 'orgStudyIdInfo': {'id': 'Prospective PRISM'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'AI group', 'description': 'Diagnosis by Artificial Intelligence model', 'interventionNames': ['Diagnostic Test: Diagnosis by Artificial Intelligence model']}, {'label': 'Clinicians group', 'description': 'Diagnosis by clinicians'}], 'interventions': [{'name': 'Diagnosis by Artificial Intelligence model', 'type': 'DIAGNOSTIC_TEST', 'description': 'To develop an artificial intelligence-based classification management system for pancreatic diseases, achieving automated and precise classification. Contrast-enhanced CT images from all study subjects will be analyzed by the AI system to generate classification results, categorizing patients into three groups: INTERVENTIOM, INTENSIVE SURVEILLANCE or ROUTINE SURVEILLANCE.', 'armGroupLabels': ['AI group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '200433', 'city': 'Shanghai', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Beilei Wang, Doctor', 'role': 'CONTACT', 'email': 'lilly_wang@126.com', 'phone': '+86 13774238083'}], 'facility': 'Changhai Hospital', 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}], 'centralContacts': [{'name': 'Beilei Wang, Doctor', 'role': 'CONTACT', 'email': 'lilly_wang@126.com', 'phone': '+86 13774238083'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Changhai Hospital', 'class': 'OTHER'}, 'collaborators': [{'name': 'The First Affiliated Hospital with Nanjing Medical University', 'class': 'OTHER'}, {'name': "The Affiliated People's Hospital of Ningbo University", 'class': 'OTHER_GOV'}, {'name': 'The Second Affiliated Hospital of Jiaxing University', 'class': 'OTHER'}, {'name': 'Shanghai Changzheng Hospital', 'class': 'OTHER'}, {'name': 'Xinhua Hospital, Shanghai Jiao Tong University School of Medicine', 'class': 'OTHER'}, {'name': 'Shengjing Hospital', 'class': 'OTHER'}, {'name': "Shanghai Fourth People's Hospital Tongji University", 'class': 'OTHER'}, {'name': 'The First Affiliated Hospital of Medical School of Zhejiang University', 'class': 'UNKNOWN'}, {'name': 'Shanghai Fudan University Cancer Center', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'SPONSOR'}}}}