Ignite Creation Date:
2025-12-25 @ 2:19 AM
Ignite Modification Date:
2025-12-28 @ 8:53 PM
Study NCT ID:
NCT06760234
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2025-01-09
First Post:
2024-12-29
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Multimodal Deep Learning Model Predicts Pancreatic Cancer Prognosis
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 247}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2024-07-05', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-07', 'completionDateStruct': {'date': '2029-07-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-01-07', 'studyFirstSubmitDate': '2024-12-29', 'studyFirstSubmitQcDate': '2024-12-29', 'lastUpdatePostDateStruct': {'date': '2025-01-09', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-01-06', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-12-15', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Performance of deep learning model', 'timeFrame': 'Baseline treatment', 'description': "The model's performance was evaluated using metrics including area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity."}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Deep Learning Model', 'Pancreatic adenocarcinoma', 'prognosis'], 'conditions': ['Pancreatic Adenocarcinoma']}, 'descriptionModule': {'briefSummary': 'This study describes the development and validation of a deep learning prediction model, which extracts deep learning features from preoperative enhanced CT scans and analyzes postoperative pathological specimens of pancreatic cancer patients. The aim is to predict patient prognosis and response to chemotherapy treatment.', 'detailedDescription': "This study retrospectively collected enhanced CT scan data, pathological paraffin blocks, and clinical data from pancreatic cancer patients who underwent surgery at multiple centers between March 2013 and May 2024. The pathological paraffin blocks were stained using immunohistochemistry for prognostic immune microenvironment markers, and patients were classified based on these results. Subsequently, deep learning features were extracted from enhanced CT scans, and a multimodal prediction model was constructed using imaging features and clinical information. The model's performance was evaluated using metrics including area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '90 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Pancreatic cancer patients who were undergo surgery and received adjuvant chemotherapy after surgery.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Patients with pancreatic cancer, diagnosed through pathology;\n2. Patients underwent surgery and received adjuvant chemotherapy after surgery.\n\nExclusion Criteria:\n\n1. Missing or inadequate quality of CT,\n2. Incomplete clinical or pathological data.\n3. Multiple primary malignancies;\n4. History of malignancy.'}, 'identificationModule': {'nctId': 'NCT06760234', 'briefTitle': 'Multimodal Deep Learning Model Predicts Pancreatic Cancer Prognosis', 'organization': {'class': 'OTHER', 'fullName': 'Second Affiliated Hospital, School of Medicine, Zhejiang University'}, 'officialTitle': 'Prediction of Pancreatic Cancer Prognosis Using a Multimodal Deep Learning Model Based on Intratumoral Immune Microenvironment', 'orgStudyIdInfo': {'id': 'PCPAI'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Training Cohort', 'description': 'Patients diagnosed with pancreatic cancer who underwent surgery and other treatments at the Second Affiliated Hospital, Zhejiang University School of Medicine', 'interventionNames': ['Diagnostic Test: No Interventions']}, {'label': 'Test Cohort', 'description': 'Patients diagnosed with pancreatic cancer who underwent surgery and other treatments at the Fourth Affiliated Hospital, Zhejiang University School of Medicine and Hangzhou Hosptial of Traditional Chinese Medicine', 'interventionNames': ['Diagnostic Test: No Interventions']}], 'interventions': [{'name': 'No Interventions', 'type': 'DIAGNOSTIC_TEST', 'description': 'The high-throughput extraction of quantitative image features from medical images', 'armGroupLabels': ['Test Cohort', 'Training Cohort']}, {'name': 'No Interventions', 'type': 'DIAGNOSTIC_TEST', 'description': 'Immunohistochemical analysis', 'armGroupLabels': ['Test Cohort', 'Training Cohort']}]}, 'contactsLocationsModule': {'locations': [{'zip': '310009', 'city': 'Hangzhou', 'state': 'Zhejiang', 'country': 'China', 'facility': 'the Second Affiliated Hospital Zhejiang University School of Medicine', 'geoPoint': {'lat': 30.29365, 'lon': 120.16142}}], 'overallOfficials': [{'name': 'Yulian Wu, PhD.', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Second Affiliated Hospital of Zhejiang University School of Medicine'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Second Affiliated Hospital, School of Medicine, Zhejiang University', 'class': 'OTHER'}, 'collaborators': [{'name': 'the fourth affiliated hospital, Zhejiang university school of medcine', 'class': 'UNKNOWN'}, {'name': 'Hangzhou Hospital of Traditional Chinese Medicine', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}