Ignite Creation Date:
2026-03-26 @ 3:15 PM
Ignite Modification Date:
2026-03-31 @ 10:20 AM
Study NCT ID:
NCT07304492
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-12-26
First Post:
2025-12-13
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI for Renal Tumors Using Non-Contrast CT
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D007680', 'term': 'Kidney Neoplasms'}], 'ancestors': [{'id': 'D014571', 'term': 'Urologic Neoplasms'}, {'id': 'D014565', 'term': 'Urogenital Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D052776', 'term': 'Female Urogenital Diseases'}, {'id': 'D005261', 'term': 'Female Urogenital Diseases and Pregnancy Complications'}, {'id': 'D000091642', 'term': 'Urogenital Diseases'}, {'id': 'D007674', 'term': 'Kidney Diseases'}, {'id': 'D014570', 'term': 'Urologic Diseases'}, {'id': 'D052801', 'term': 'Male Urogenital Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'OTHER', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 10000}, 'targetDuration': '6 Months', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-10', 'completionDateStruct': {'date': '2028-12', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-12-13', 'studyFirstSubmitDate': '2025-12-13', 'studyFirstSubmitQcDate': '2025-12-13', 'lastUpdatePostDateStruct': {'date': '2025-12-26', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-12-26', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2028-12', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Building an intelligent diagnostic system for renal diseases based on CT scans.', 'timeFrame': '1 year', 'description': 'To construct an intelligent system for the detection of renal mass lesions and their differentiation into cysts, benign, and malignant neoplasms.'}], 'secondaryOutcomes': [{'measure': 'Further develop artificial intelligence model to effectively diagnose pathological types of common renal tumors.', 'timeFrame': '1 year'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Artificial intelligence', 'Renal Neoplasms', 'Renal Cyst', 'Computer tomography'], 'conditions': ['Renal Neoplasms', 'Renal Cyst']}, 'descriptionModule': {'briefSummary': 'The goal of this observational study is to learn whether the artificial intelligence method can automatically identify and diagnose renal lesions using non-contrast CT or opportunistic screening.', 'detailedDescription': 'This study first establishes an AI model capable of effectively detecting and diagnosing kidney lesions based on a multicenter retrospective cohort study. Then, the AI model is applied to a large-scale real-world retrospective and prospective population to validate and improve its effectiveness.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '80 Years', 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Patients who underwent an abdominal CT examination.', 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Patients who underwent an abdominal CT examination.\n2. Patients with renal lesions were managed according to standard clinical pathways, which included follow-up, biopsy, or surgery.\n3. Malignant lesions were pathologically confirmed; benign lesions were confirmed by either pathological diagnosis or imaging follow-up.\n4. No prior treatment had been received for the renal disease.\n\nExclusion Criteria:\n\n1. Patients refuse to undergo recommended follow-up, biopsy, or surgery, which precluded definitive diagnosis of the renal lesion.\n2. Absence of complete pathological confirmation for lesions suspected to be malignant.\n3. Patients have received any form of prior treatment for the renal lesion.\n4. Poor image quality that hampered diagnostic evaluation.'}, 'identificationModule': {'nctId': 'NCT07304492', 'briefTitle': 'AI for Renal Tumors Using Non-Contrast CT', 'organization': {'class': 'OTHER', 'fullName': 'Fudan University'}, 'officialTitle': 'An Artificial Intelligence Model for Screening and Diagnosis of Renal Tumors Based on Non-Contrast CT', 'orgStudyIdInfo': {'id': '2509-Exp275'}}, 'contactsLocationsModule': {'locations': [{'zip': '200032', 'city': 'Shanghai', 'state': 'Shanghai Municipality', 'country': 'China', 'contacts': [{'name': 'Yajia Gu, MD', 'role': 'CONTACT', 'email': 'guyajia@fudan.edu.cn', 'phone': '+8621-64175590'}, {'name': 'Bingni Zhou, MD', 'role': 'CONTACT', 'email': 'jobay2621405@126.com', 'phone': '+8621-64175590'}], 'facility': 'Fudan university Shanghai Cancer Center', 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}], 'centralContacts': [{'name': 'Yajia Gu, MD', 'role': 'CONTACT', 'email': 'guyajia@fudan.edu.cn', 'phone': '+8621-64175590'}, {'name': 'Bingni Zhou, MD', 'role': 'CONTACT', 'email': 'jobay2621405@126.com', 'phone': '+8621-64175590'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Fudan University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Director, Head of Radiology, Principal Investigator, Clinical Professor', 'investigatorFullName': 'Yajia Gu, MD', 'investigatorAffiliation': 'Fudan University'}}}}