Ignite Creation Date:
2025-12-25 @ 2:50 AM
Ignite Modification Date:
2026-01-07 @ 3:41 PM
Study NCT ID:
NCT06204133
Status:
COMPLETED
Last Update Posted:
2024-07-22
First Post:
2023-12-15
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Model Study on Cervical Cancer Screening Strategies and Risk Prediction
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 1112846}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2023-11-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-07', 'completionDateStruct': {'date': '2024-06-30', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2024-07-19', 'studyFirstSubmitDate': '2023-12-15', 'studyFirstSubmitQcDate': '2024-01-02', 'lastUpdatePostDateStruct': {'date': '2024-07-22', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-01-12', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-04-30', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Cervical histopathology', 'timeFrame': 'within 8 weeks，', 'description': 'Cervical histopathological diagnosis within 8 weeks'}, {'measure': 'colposcopy', 'timeFrame': 'Percentage of patients diagnosed with cervical intraepithelial neoplasia of grade 3 (CIN3) or worse by cervical histopathological measurements within 8 weeks', 'description': 'Colposcopists use colposcopic equipment to investigate the occurrence of cervical and vaginal lesions within 8 weeks'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Cervical Cancer Screening', 'Risk Assessment', 'Artificial Intelligence', 'Machine Learning']}, 'descriptionModule': {'briefSummary': 'By collecting non-image medical data of women undergoing cervical screening in multiple centers in China, including age, HPV infection status, HPV infection type, TCT results, and colposcopy biopsy pathology results, a multi-source heterogeneous cervical lesion collaborative research big data platform was established. Based on artificial intelligence (AI) machine learning, cervical lesion screening features are refined, a multi-modal cervical cancer intelligent screening prediction and risk triage model is constructed, and its clinical application value is preliminarily explored.', 'detailedDescription': 'By collecting non-image medical data of women undergoing cervical screening in multiple centers in China, including age, HPV infection status, HPV infection type, TCT results, and colposcopy biopsy pathology results, a multi-source heterogeneous cervical lesion collaborative research big data platform was established. Based on artificial intelligence (AI) machine learning, cervical lesion screening features are refined, a multi-modal cervical cancer intelligent screening prediction and risk triage model is constructed, and its clinical application value is preliminarily explored. The effect of clinical application of the model was evaluated by internal data from Fujian Province and external data from several other regions in China.'}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['ADULT'], 'maximumAge': '64 Years', 'minimumAge': '25 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'For women aged 25-64 years who undergo cervical cancer screening, all women use HR-HPV testing as a primary screening strategy.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Age 25-64 years old;\n* There was no history of precancerous lesions or cervical cancer;\n* No previous cervical surgery or cervical removal;\n\nExclusion Criteria:\n\n* HPV test results are not available;\n* Pregnant or lactating women;\n* There is a serious immune system disease, and the disease is active;'}, 'identificationModule': {'nctId': 'NCT06204133', 'briefTitle': 'Model Study on Cervical Cancer Screening Strategies and Risk Prediction', 'organization': {'class': 'OTHER', 'fullName': 'Fujian Maternity and Child Health Hospital'}, 'officialTitle': 'Model Study on Cervical Cancer Screening Strategies and Risk Prediction', 'orgStudyIdInfo': {'id': 'CSRM2304'}}, 'armsInterventionsModule': {'interventions': [{'name': 'Artificial intelligence model building', 'type': 'OTHER', 'description': 'Using non-image medical data of cervical lesions and clinical pathology results in different medical institutions, machine learning is adopted to establish multiple multi-modal cervical cancer intelligent screening prediction models. This method was used to analyze the prediction performance of the multi-modal cervical cancer intelligent screening prediction and risk triage model, and to evaluate and optimize the self-learning ability of the established multi-modal cervical cancer intelligent screening prediction model.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '350001', 'city': 'Fuzhou', 'state': 'Fujian', 'country': 'China', 'facility': 'Fujian Maternity and Child Health Hospital', 'geoPoint': {'lat': 26.06139, 'lon': 119.30611}}, {'city': 'Ningde', 'state': 'Fujian', 'country': 'China', 'facility': 'Ningde maternal and child health hospital', 'geoPoint': {'lat': 26.66167, 'lon': 119.52278}}, {'city': 'Lanzhou', 'state': 'Ganshu', 'country': 'China', 'facility': 'Gansu Provincial Maternity and Child-care Hospital', 'geoPoint': {'lat': 36.05701, 'lon': 103.83987}}, {'city': 'Foshan', 'state': 'Guangdong', 'country': 'China', 'facility': "Shunde Women's and Children's Hospital of Guangdong Medical University", 'geoPoint': {'lat': 23.02677, 'lon': 113.13148}}, {'city': 'Shenzhen', 'state': 'Guangdong', 'country': 'China', 'facility': 'Shenzhen Maternal and Child Health Hospital', 'geoPoint': {'lat': 22.54554, 'lon': 114.0683}}, {'city': 'Guiyang', 'state': 'Guizhou', 'country': 'China', 'facility': 'Guiyang maternal and child health care hospital', 'geoPoint': {'lat': 26.58333, 'lon': 106.71667}}, {'city': 'Wuhan', 'state': 'Hubei', 'country': 'China', 'facility': 'Hubei Maternal and Child Health Hospital', 'geoPoint': {'lat': 30.58333, 'lon': 114.26667}}], 'overallOfficials': [{'name': 'Pengming Sun', 'role': 'STUDY_CHAIR', 'affiliation': 'Fujian Maternal and Child Health Hospital'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Fujian Maternity and Child Health Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}