Ignite Creation Date:
2025-12-24 @ 11:14 PM
Ignite Modification Date:
2026-01-02 @ 6:59 AM
Study NCT ID:
NCT07020169
Status:
RECRUITING
Last Update Posted:
2025-06-13
First Post:
2025-05-24
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Using 3D Kidney Model Based on Artificial Intelligence to Assist Partial Nephrectomy: A Prospective Validation Study
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D002292', 'term': 'Carcinoma, Renal Cell'}], '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': 'D007680', 'term': 'Kidney Neoplasms'}, {'id': 'D014571', 'term': 'Urologic Neoplasms'}, {'id': 'D014565', 'term': 'Urogenital Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'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': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'TREATMENT', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 232}}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2025-05-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-06', 'completionDateStruct': {'date': '2027-03-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-06-12', 'studyFirstSubmitDate': '2025-05-24', 'studyFirstSubmitQcDate': '2025-06-12', 'lastUpdatePostDateStruct': {'date': '2025-06-13', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-06-13', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-09-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Operative Time', 'timeFrame': 'Intraoperative'}], 'secondaryOutcomes': [{'measure': "Operating Surgeon's Assessment", 'timeFrame': 'immediately after the surgery', 'description': 'Scoring for Each Surgery(0-5) by navigation accuracy, image rendering smoothness'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['partial nephrectomy', 'Articicial Intelligence', 'Intraoperative Real-Time Navigation'], 'conditions': ['Renal Cell Cancer']}, 'descriptionModule': {'briefSummary': 'The goal of this study is to develop a real-time artificial intelligence-driven 3D kidney model to assist robotic or laparoscopic partial nephrectomy:\n\n• Can this AI-powered model optimize the workflow of partial nephrectomy and enhance surgical benefits?', 'detailedDescription': 'This study aims to evaluate the feasibility of the AI-based real-time image-guided kidney model system in optimizing partial nephrectomy workflows. Patients scheduled for laparoscopic or robotic-assisted partial nephrectomy will be randomized to receive either AI-assisted surgical navigation (utilizing intraoperative 3D model overlay with automated registration) or conventional approaches. Comparative metrics will include ischemia time, margin positivity rate, and operative efficiency indices. Findings will inform iterative refinement of the system architecture based on clinical performance feedback.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '80 Years', 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Ages 18-80 years, regardless of gender\n* Written informed consent obtained from the patient or legally authorized representative after full protocol disclosure\n* Preoperative imaging (CT/MRI) confirming clinical stage T1a or select T1b renal tumors suitable for partial nephrectomy (R.E.N.A.L. nephrometry score ≤10)\n* Localized renal tumors without lymph node/distant metastasis per NCCN Guidelines® (v2023)\n* Elective minimally invasive partial nephrectomy (laparoscopic/robotic) after comprehensive surgical counseling\n\nExclusion Criteria:\n\n* Multifocal renal tumors (bilateral or unilateral)\n* Prior systemic anticancer therapy (targeted agents/immunotherapy/chemotherapy) within 6 months\n* Absolute surgical contraindications (e.g., ASA class ≥IV, uncontrolled coagulopathy)\n* Intraoperative conversion to radical nephrectomy or open approach\n* Postoperative adjuvant therapy during protocol-defined follow-up (12 months)\n* Major comorbidities (e.g., NYHA class III/IV heart failure, eGFR \\<30 mL/min/1.73m²) affecting outcome assessment\n* Concurrent enrollment in interventional clinical trials\n* Investigator-determined ineligibility based on risk-benefit analysis'}, 'identificationModule': {'nctId': 'NCT07020169', 'briefTitle': 'Using 3D Kidney Model Based on Artificial Intelligence to Assist Partial Nephrectomy: A Prospective Validation Study', 'organization': {'class': 'OTHER', 'fullName': 'The First Affiliated Hospital with Nanjing Medical University'}, 'officialTitle': 'Artificial Intelligence-Driven 3D Kidney Model for Real-Time Augmented Reality and Surgical Navigation in Minimally Invasive (Robotic/Laparoscopic) Partial Nephrectomy: A Prospective Validation Study', 'orgStudyIdInfo': {'id': '2025-SR-309'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'NO_INTERVENTION', 'label': 'Conventional Surgical Approaches', 'description': 'perform conventional laparoscopic or robotic-assisted surgical approaches'}, {'type': 'EXPERIMENTAL', 'label': 'AI model group', 'description': 'Use the AI-model to locate kidney and tumour, assisting surgeon with the operation', 'interventionNames': ['Procedure: an AI-based real-time image-guided kidney model system']}], 'interventions': [{'name': 'an AI-based real-time image-guided kidney model system', 'type': 'PROCEDURE', 'description': 'Use the AI-model to locate kidney and tumour, assisting surgeon with the operation', 'armGroupLabels': ['AI model group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '210036', 'city': 'Nanjing', 'state': 'Jiangsu', 'status': 'NOT_YET_RECRUITING', 'country': 'China', 'contacts': [{'name': 'Pengfei Shao, Professor', 'role': 'CONTACT', 'email': 'spf032@hotmail.com', 'phone': '+8613851925825'}, {'name': 'Haoqi Miao, Postgraduate', 'role': 'CONTACT', 'email': 'mhq@stu.njmu.edu.cn', 'phone': '+8613276636957'}], 'facility': "The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)", 'geoPoint': {'lat': 32.06167, 'lon': 118.77778}}, {'zip': '210036', 'city': 'Nanjing', 'state': 'Jiangsu', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Pengfei Shao, Professor', 'role': 'CONTACT', 'email': 'spf032@hotmail.com', 'phone': '+86 13851925825'}], 'facility': "The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)", 'geoPoint': {'lat': 32.06167, 'lon': 118.77778}}], 'centralContacts': [{'name': 'Pengfei Shao, Professor', 'role': 'CONTACT', 'email': 'spf032@hotmail.com', 'phone': '+8613851925825'}, {'name': 'Haoqi Miao, Postgraduate', 'role': 'CONTACT', 'email': 'mhq@stu.njmu.edu.cn', 'phone': '+8613276636957'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Shao Pengfei', 'class': 'OTHER'}, 'collaborators': [{'name': 'Southeast University, China', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'chief physician', 'investigatorFullName': 'Shao Pengfei', 'investigatorAffiliation': 'The First Affiliated Hospital with Nanjing Medical University'}}}}