Ignite Creation Date:
2025-12-25 @ 1:38 AM
Ignite Modification Date:
2025-12-25 @ 11:53 PM
Study NCT ID:
NCT06336694
Status:
COMPLETED
Last Update Posted:
2024-03-29
First Post:
2023-11-26
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
A Deep Learning Radiomics Model for Predicting Occult Peritoneal Metastases of Pancreatic Adenocarcinoma
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D010190', 'term': 'Pancreatic Neoplasms'}], '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'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D013514', 'term': 'Surgical Procedures, Operative'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITHOUT_DNA', 'description': 'Peritoneum metastases confirmed by pathology'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 302}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2021-01-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-03', 'completionDateStruct': {'date': '2023-07-30', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2024-03-26', 'studyFirstSubmitDate': '2023-11-26', 'studyFirstSubmitQcDate': '2024-03-26', 'lastUpdatePostDateStruct': {'date': '2024-03-29', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-03-29', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2022-10-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'diagnosed with peritoneal metastases', 'timeFrame': 'immediately after the surgery', 'description': 'percentage'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Pancreatic Neoplasms']}, 'descriptionModule': {'briefSummary': 'Occult peritoneal metastases (OPM) in patients with pancreatic ductal adenocarcinoma (PDAC) are frequently overlooked during imaging. We aimed to develop and validate a CT-based deep learning-based radiomics (DLR) model with clinical-radiological characteristics to identify OPM in patients with PDAC before treatment.', 'detailedDescription': 'This retrospective, bicentric study included 302 patients with PDAC (training: n = 167, OPM-positive, n=22; internal test: n = 72, OPM-positive, n=9: external test, n=63, OPM-positive, n=9) who had undergone baseline CT examinations between January 2012 and October 2022. Handcrafted radiomics (HCR) and DLR features of the tumor and HCR features of peritoneum were extracted from CT images. Mutual information and least absolute shrinkage and selection operator algorithms were used for feature selection. A combined model, which incorporated the selected clinical-radiological, HCR, and DLR features, was developed using a logistic regression classifier using data from the training cohort and validated in the test cohorts.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients with suspected pancreatic tumors who had undergone contrast-enhanced CT and pathological examinations were screened at Center 1 between January 2012 and October 2022 and Center 2 between January 2014 and October 2021.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\nPatients with suspected pancreatic tumors who underwent contrast enhanced CT and pathological examinations at Center 1 and Center 2 were eligible for inclusion in this study.\n\nExclusion Criteria:\n\n* (a) pathologically diagnosed PDAC by pathology, (b) time intervals between contrast-enhanced CT and pathology less than 2 weeks; (c) history of pancreatic surgery, (d) history of pancreatic malignancy, and (e) poor CT image quality that undermined peritoneal lesion assessment'}, 'identificationModule': {'nctId': 'NCT06336694', 'briefTitle': 'A Deep Learning Radiomics Model for Predicting Occult Peritoneal Metastases of Pancreatic Adenocarcinoma', 'organization': {'class': 'OTHER', 'fullName': 'First Affiliated Hospital, Sun Yat-Sen University'}, 'officialTitle': 'Development and Validation of a Deep Learning Radiomics Model With Clinical-radiological Characteristics for the Identification of Occult Peritoneal Metastases in Patients With Pancreatic Ductal Adenocarcinoma', 'orgStudyIdInfo': {'id': 'SYSUFAH2021-025'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'peritoneal metastases', 'description': 'No intervention had been adminstered during treatment of patients.', 'interventionNames': ['Procedure: surgery or diagnostic staging laparoscopy']}, {'label': 'without peritoneal metastases', 'description': 'No intervention had been adminstered during treatment of patients.', 'interventionNames': ['Procedure: surgery or diagnostic staging laparoscopy']}], 'interventions': [{'name': 'surgery or diagnostic staging laparoscopy', 'type': 'PROCEDURE', 'description': 'diagnosis of PDAC with peritoneal examination based on the surgical (for tumors treated with surgery) or diagnostic staging laparoscopy findings (for tumors treated with radiotherapy/chemotherapy)', 'armGroupLabels': ['peritoneal metastases', 'without peritoneal metastases']}]}, 'contactsLocationsModule': {'locations': [{'zip': '510000', 'city': 'Guangzhou', 'state': 'Guangdong', 'country': 'China', 'facility': 'Shi Siya', 'geoPoint': {'lat': 23.11667, 'lon': 113.25}}], 'overallOfficials': [{'name': 'Shi-Ting Feng, MD', 'role': 'STUDY_DIRECTOR', 'affiliation': 'First Affiliated Hospital, Sun Yat-Sen University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'All data generated for this study were from the authors upon reasonable request.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'First Affiliated Hospital, Sun Yat-Sen University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'clinical doctor', 'investigatorFullName': 'Siya Shi', 'investigatorAffiliation': 'First Affiliated Hospital, Sun Yat-Sen University'}}}}