Ignite Creation Date:
2025-12-24 @ 3:57 PM
Ignite Modification Date:
2026-01-01 @ 7:57 AM
Study NCT ID:
NCT05542992
Status:
UNKNOWN
Last Update Posted:
2022-09-16
First Post:
2022-09-13
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Deep Learning Model for Pure Solid Nodules Classification
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': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 260}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'RECRUITING', 'startDateStruct': {'date': '2022-01-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2022-09', 'completionDateStruct': {'date': '2023-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2022-09-14', 'studyFirstSubmitDate': '2022-09-13', 'studyFirstSubmitQcDate': '2022-09-14', 'lastUpdatePostDateStruct': {'date': '2022-09-16', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-09-16', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2023-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'AUC', 'timeFrame': '2022.01-2023.12', 'description': 'Area under the curve of the receiver operating characteristic'}], 'secondaryOutcomes': [{'measure': 'Accuracy', 'timeFrame': '2022.01-2023.12', 'description': 'Ratio of the number of correctly classified samples to the total number of samples'}, {'measure': 'sensitivity', 'timeFrame': '2022.01-2023.12', 'description': 'The probability of detecting a positive test in the population with the gold standard for disease (positive)'}, {'measure': 'Specificity', 'timeFrame': '2022.01-2023.12', 'description': 'Odds of detecting a negative test in a population judged disease-free (negative) by the gold standard'}, {'measure': 'PPV', 'timeFrame': '2022.01-2023.12', 'description': 'Positive predictive value'}, {'measure': 'NPV', 'timeFrame': '2022.01-2023.12', 'description': 'Negative predictive value'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['deep learning model', 'pure-solid nodules', 'PET-CT'], 'conditions': ['Lung Cancer']}, 'descriptionModule': {'briefSummary': 'The purpose of this study is to compare the predictive performance of a CT-based deep learning model for pure-solid nodules classification and compared with the tumor maximum standardized uptake value on PET in a multicenter prospective cohort.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '75 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients with pulmonary radiological pure-solid nodules with size less than 3cm', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Participants scheduled for surgery for radiological finding of pulmonary pure-solid lesions from the preoperative thin-section CT scans;\n* The maximum short-axis diameter of lymph nodes less than 3 cm on CT scan;\n* Age ranging from 18-75 years;\n* definied pathological examination report available;\n* Obtained written informed consent.\n\nExclusion Criteria:\n\n* Multiple lung lesions;\n* Poor quality of CT images;\n* Participants with incomplete clinical information;\n* Participants who have received neoadjuvant therapy before initial CT evaluation.'}, 'identificationModule': {'nctId': 'NCT05542992', 'briefTitle': 'Deep Learning Model for Pure Solid Nodules Classification', 'organization': {'class': 'OTHER', 'fullName': 'Shanghai Pulmonary Hospital, Shanghai, China'}, 'officialTitle': 'Deep Learning Model Supplementary PET-CT as a More Effectively Diagnostic Method for Pure Solid Nodules Classification: a Multicenter Observational Study', 'orgStudyIdInfo': {'id': 'L21-022'}}, 'armsInterventionsModule': {'interventions': [{'name': 'CT-based deep learning model', 'type': 'DIAGNOSTIC_TEST', 'description': 'CT-based deep learning model for pure-solid nodules classifications'}]}, 'contactsLocationsModule': {'locations': [{'city': 'Yangpu', 'state': 'Shanghai Municipality', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Chang Chen, MD, PhD', 'role': 'CONTACT', 'email': 'chenthoracic@163.com', 'phone': '+86-021-65115006', 'phoneExt': '2074'}], 'facility': 'Shanghai Pulmonary Hospital', 'geoPoint': {'lat': 31.26193, 'lon': 121.51904}}, {'city': 'China, Gansu', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Minjie Ma, Dr', 'role': 'CONTACT', 'email': '721405952@qq.com', 'phone': '021-65115006'}], 'facility': 'Lanzhou', 'geoPoint': {'lat': 23.72839, 'lon': 108.65543}}, {'city': 'China, Guizhou', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Yongxiang Song, Dr', 'role': 'CONTACT', 'email': 'zhaosurgery@163.com', 'phone': '021-65115006'}], 'facility': 'Zunyi', 'geoPoint': {'lat': 31.01667, 'lon': 110.58333}}, {'city': 'China, Jiangxi', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Bentong Yu, Dr', 'role': 'CONTACT', 'email': '1151697503@qq.com', 'phone': '021-65115006'}], 'facility': 'Nanchang', 'geoPoint': {'lat': 33.99934, 'lon': 105.19994}}, {'city': 'China, Zhejiang', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Minglei Yang, Dr', 'role': 'CONTACT', 'email': 'almondjj@163.com', 'phone': '021-65115006'}], 'facility': 'Ningbo', 'geoPoint': {'lat': 25.3238, 'lon': 105.583}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Chang Chen', 'class': 'OTHER'}, 'collaborators': [{'name': 'Ningbo No.2 Hospital', 'class': 'OTHER'}, {'name': 'Zunyi Medical College', 'class': 'OTHER'}, {'name': 'The First Affiliated Hospital of Nanchang University', 'class': 'OTHER'}, {'name': 'The First Hospital of Lanzhou University, Gansu, China', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Chang Chen', 'investigatorAffiliation': 'Shanghai Pulmonary Hospital, Shanghai, China'}}}}