Ignite Creation Date:
2025-12-24 @ 9:10 PM
Ignite Modification Date:
2026-01-26 @ 10:35 PM
Study NCT ID:
NCT05400304
Status:
UNKNOWN
Last Update Posted:
2022-06-29
First Post:
2022-05-26
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Radiomics Combined With Frozen Section Prediction Model for Spread Through Air Space in Lung Adenocarcinoma
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D000077192', 'term': 'Adenocarcinoma of Lung'}], 'ancestors': [{'id': 'D000230', 'term': 'Adenocarcinoma'}, {'id': 'D002277', 'term': 'Carcinoma'}, {'id': 'D009375', 'term': 'Neoplasms, Glandular and Epithelial'}, {'id': 'D009370', 'term': 'Neoplasms by Histologic Type'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D008175', 'term': 'Lung Neoplasms'}, {'id': 'D012142', 'term': 'Respiratory Tract Neoplasms'}, {'id': 'D013899', 'term': 'Thoracic Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D000097188', 'term': 'Radiomics'}], 'ancestors': [{'id': 'D003952', 'term': 'Diagnostic Imaging'}, {'id': 'D019937', 'term': 'Diagnostic Techniques and Procedures'}, {'id': 'D003933', 'term': 'Diagnosis'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 900}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2022-07-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2022-06', 'completionDateStruct': {'date': '2023-05-12', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2022-06-22', 'studyFirstSubmitDate': '2022-05-26', 'studyFirstSubmitQcDate': '2022-05-26', 'lastUpdatePostDateStruct': {'date': '2022-06-29', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-06-01', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2023-05-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Sensitivity', 'timeFrame': '24 hour before operation', 'description': 'Testing the sensitivity of Radiomics to predict STAS using the area under receiver operating characteristic curve'}, {'measure': 'Specificity', 'timeFrame': '24 hour before operation', 'description': 'Testing the specificity of Radiomics to predict STAS using the area under receiver operating characteristic curve'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Spread Through Air Space', 'radiomics', 'frozen section', 'Prediction Model'], 'conditions': ['Lung Adenocarcinoma']}, 'descriptionModule': {'briefSummary': 'a multifactorial model combining radiomics with frozen section analysis is a potential biomarker for assessing Spread Through Air Space during surgery, which can provide decision-making support to therapeutic planning for early-stage lung adenocarcinomas.', 'detailedDescription': 'Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. Its preoperative assessment could thus be useful to customize surgical treatment. Radiomics and frozen section haave been recently proposed to predict STAS in patients with lung adenocarcinoma. Radiomics-based Prediction Model is highly sensitive but not specific for STAS detection. While, frozen section is highly specific but not sensitive for STAS detection in early lung adenocarcinomas.\n\nTherefore, the proposed project aims to develop and validate a multifactorial model combining radiomics with frozen section analysis to assesse Spread Through Air Space during surgery, which can provide decision-making support to therapeutic planning for early-stage lung adenocarcinomas.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '75 Years', 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Patients with pulmonary nodules in the collaborating institutes.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. intraoperative frozen section diagnosis and final pathology diagnosis are available\n2. preoperative standard non-enhanced CT is available\n3. Pathologically confirmed\n\nExclusion Criteria:\n\n1. with a previous history of radiation therapy, chemotherapy or biopsy\n2. the time interval between the CT examination and surgery was more than two weeks'}, 'identificationModule': {'nctId': 'NCT05400304', 'briefTitle': 'Radiomics Combined With Frozen Section Prediction Model for Spread Through Air Space in Lung Adenocarcinoma', 'organization': {'class': 'OTHER', 'fullName': 'Union Hospital, Tongji Medical College, Huazhong University of Science and Technology'}, 'officialTitle': 'Preoperative CT-based Radiomics Combined With Intraoperative Frozen Section is Predictive of Spread Through Air Space in Early Lung Adenocarcinoma', 'orgStudyIdInfo': {'id': 'RFSTAS'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Training dataset', 'description': 'No interventions', 'interventionNames': ['Diagnostic Test: radiomics']}, {'label': 'External validation1', 'description': 'No interventions', 'interventionNames': ['Diagnostic Test: radiomics']}, {'label': 'External validation2', 'description': 'No interventions', 'interventionNames': ['Diagnostic Test: radiomics']}], 'interventions': [{'name': 'radiomics', 'type': 'DIAGNOSTIC_TEST', 'description': 'The high-throughput extraction of large amounts of quantitative image features from medical images', 'armGroupLabels': ['External validation1', 'External validation2', 'Training dataset']}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Union Hospital, Tongji Medical College, Huazhong University of Science and Technology', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}