Ignite Creation Date:
2025-12-24 @ 7:39 PM
Ignite Modification Date:
2026-02-25 @ 3:00 AM
Study NCT ID:
NCT04036903
Status:
COMPLETED
Last Update Posted:
2023-02-08
First Post:
2019-07-26
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
D-Lung: An Analytics Platform for Lung Cancer Based on Deep Learning Technology
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D008175', 'term': 'Lung Neoplasms'}], 'ancestors': [{'id': 'D012142', 'term': 'Respiratory Tract Neoplasms'}, {'id': 'D013899', 'term': 'Thoracic Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D008171', 'term': 'Lung Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'CASE_ONLY'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 130}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2018-07-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-02', 'completionDateStruct': {'date': '2020-06-30', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2023-02-07', 'studyFirstSubmitDate': '2019-07-26', 'studyFirstSubmitQcDate': '2019-07-26', 'lastUpdatePostDateStruct': {'date': '2023-02-08', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2019-07-30', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2020-06-30', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'accuracy', 'timeFrame': '2 years', 'description': 'proportion of true results(both true positives and true negatives) among whole instances'}, {'measure': 'sensitivity', 'timeFrame': '2 years', 'description': 'true positive rate in percentage(%) derived by ROC analysis'}, {'measure': 'specificity', 'timeFrame': '2 years', 'description': 'true negative rate in percentage (%) derived by ROC analysis'}, {'measure': 'area under curve (AUC)', 'timeFrame': '2 years', 'description': 'area under ROC curve in percentage (%)'}], 'secondaryOutcomes': [{'measure': 'average number of false positives per scan (FPs/scan)', 'timeFrame': '2 years', 'description': 'FPs/scan in number (N) based on free-response receiver operating characteristic (FROC) analysis'}, {'measure': 'competition performance metric (CPM)', 'timeFrame': '2 years', 'description': 'Competitive performance metric (CPM) is a criterion used for CAD system evaluation. Based on FROC paradigm, CPM score is computed as an average sensitivity at seven predefined average false positive rates. CPM score ranges from 0 to 1, with higher CPM score indicating better CAD performance.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Lung nodules', 'Deep learning'], 'conditions': ['Lung Cancer']}, 'descriptionModule': {'briefSummary': 'Lung cancer is one of main cause of cancer death in worldwide, characterized of low 5-year survival rate of less than 20%. Pulmonary nodule is considered as the typical imaging manifestation in early stage of lung cancer. The National Lung Screen Trial has demonstrated that the mortality rates could decline greatly, by the utility of low-dose helical computed tomography for screen of pulmonary nodules. Thus, automatic detection, diagnosis and management of pulmonary nodules, play the vital roles in computer-aided lung cancer screening and early intervention.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'This is a single institutional retrospective cohort study of patients within hospitals in Hong Kong, who had undergone thoracic CT for suspicious lung nodules.', 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Subjects with suspicious lung nodules.\n* Thin-layer thoracic CT and pathology examination have been performed for suspicious lung nodules.\n\nExclusion Criteria:\n\n* Subjects with accompanied lesions on CT images that may interfere to lung nodules analysis'}, 'identificationModule': {'nctId': 'NCT04036903', 'briefTitle': 'D-Lung: An Analytics Platform for Lung Cancer Based on Deep Learning Technology', 'organization': {'class': 'OTHER', 'fullName': 'Chinese University of Hong Kong'}, 'officialTitle': 'D-Lung: An Analytics Platform for Primary Lung Cancer Screening, Diagnosis and Management Based on Deep Learning Technology', 'orgStudyIdInfo': {'id': '2019.306'}}, 'armsInterventionsModule': {'interventions': [{'name': 'computed tomography', 'type': 'RADIATION', 'description': 'thoracic CT examinations for diagnosis, and/or follow-up.'}]}, 'contactsLocationsModule': {'locations': [{'city': 'Hong Kong', 'state': 'Shatin', 'country': 'Hong Kong', 'facility': 'The Chinese University of Hong Kong, Prince of Wale Hospital', 'geoPoint': {'lat': 22.27832, 'lon': 114.17469}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Chinese University of Hong Kong', 'class': 'OTHER'}, 'collaborators': [{'name': 'Department of Computer Science & Engineering, CUHK', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Professor Winnie W.C. Chu', 'investigatorAffiliation': 'Chinese University of Hong Kong'}}}}