Using 3D Kidney Model Based on Artificial Intelligence to... | NCT07020169

Studies
Using 3D Kidney Model Based on Artificial Intelligence to Assist Partial Nephrectomy: A Prospective Validation Study

NCT07020169

Sponsor

Shao Pengfei

Status

Recruiting

Last Update Posted

Jun 13, 2025Actual

Enrollment

232Estimated

Phase

Not Applicable

Conditions

Renal Cell Cancer

Interventions

an AI-based real-time image-guided kidney model system

Countries

China

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Jun 2025

Overall Recruitment Status or Expanded Access Status

Recruiting

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

May 1, 2025Actual

Primary Completion Date

Sep 1, 2026Estimated

Completion Date

Mar 1, 2027Estimated

First Submitted Date

May 24, 2025

First Submission Date that Met QC Criteria

Jun 12, 2025

First Posted Date

Jun 13, 2025Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Jun 12, 2025

Last Update Posted Date

Jun 13, 2025Actual

Name	Class
Southeast University, China	OTHER

Oversight Module

Has Data Monitoring Committee (DMC)

Yes

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

Brief Summary

The goal of this study is to develop a real-time artificial intelligence-driven 3D kidney model to assist robotic or laparoscopic partial nephrectomy:

• Can this AI-powered model optimize the workflow of partial nephrectomy and enhance surgical benefits?

Detailed Description

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.

Design Module

Study Type

Interventional

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Not provided

Target Follow-Up Duration

Not provided

Phases

Not Applicable

Interventional Study Design

Allocation

Biospecimen

No data available

No data is available for this block.

Enrollment

232Estimated

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
Conventional Surgical Approaches	No Intervention	perform conventional laparoscopic or robotic-assisted surgical approaches
AI model group	Experimental	Use the AI-model to locate kidney and tumour, assisting surgeon with the operation	Procedure: an AI-based real-time image-guided kidney model system

Interventions

Name	Type	Description	Arm Group Labels	Other Names
an AI-based real-time image-guided kidney model system	Procedure	Use the AI-model to locate kidney and tumour, assisting surgeon with the operation	AI model group

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Operative Time		Intraoperative

Secondary Outcomes

Measure	Description	Time Frame
Operating Surgeon's Assessment	Scoring for Each Surgery(0-5) by navigation accuracy, image rendering smoothness	immediately after the surgery

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

Ages 18-80 years, regardless of gender
Written informed consent obtained from the patient or legally authorized representative after full protocol disclosure
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)
Localized renal tumors without lymph node/distant metastasis per NCCN Guidelines® (v2023)
Elective minimally invasive partial nephrectomy (laparoscopic/robotic) after comprehensive surgical counseling

Exclusion Criteria:

Multifocal renal tumors (bilateral or unilateral)
Prior systemic anticancer therapy (targeted agents/immunotherapy/chemotherapy) within 6 months
Absolute surgical contraindications (e.g., ASA class ≥IV, uncontrolled coagulopathy)
Intraoperative conversion to radical nephrectomy or open approach
Postoperative adjuvant therapy during protocol-defined follow-up (12 months)
Major comorbidities (e.g., NYHA class III/IV heart failure, eGFR <30 mL/min/1.73m²) affecting outcome assessment
Concurrent enrollment in interventional clinical trials
Investigator-determined ineligibility based on risk-benefit analysis

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

80 Years

Standard Ages

AdultOlder Adult

Study Population

Not provided

Sampling Method

Not provided

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Pengfei Shao, Professor	Contact	+8613851925825		spf032@hotmail.com
Haoqi Miao, Postgraduate	Contact	+8613276636957		mhq@stu.njmu.edu.cn

Overall Officials

Not provided

Locations

Facility	Status	City	State	ZIP	Country	Contacts
The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)	Not yet recruiting	Nanjing	Jiangsu	210036	China

Condition Browse Module

MeSH Terms

ID	Term
D002292	Carcinoma, Renal Cell

Ancestor Terms

ID	Term
D000230	Adenocarcinoma
D002277	Carcinoma
D009375	Neoplasms, Glandular and Epithelial
D009370	Neoplasms by Histologic Type

Browse Leaves

Not provided

Browse Branches

Not provided