Optimising Renal Tumour Management Through Artificial Int... | NCT06714916

Studies
Optimising Renal Tumour Management Through Artificial Intelligence Modules

NCT06714916

Sponsor

Shao Pengfei

Status

Recruiting

Last Update Posted

Mar 19, 2025Actual

Enrollment

2,100Estimated

Phase

Not provided

Conditions

Renal Neoplasms
Pathology
Renal Cell Cancer

Interventions

Not provided

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

Dec 2024

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

Jan 1, 2025Actual

Primary Completion Date

Jan 1, 2028Estimated

Completion Date

Dec 31, 2033Estimated

First Submitted Date

Dec 2, 2024

First Submission Date that Met QC Criteria

Dec 2, 2024

First Posted Date

Dec 4, 2024Actual

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

Mar 16, 2025

Last Update Posted Date

Mar 19, 2025Actual

Sponsor/Collaborators Module

Responsible Party, by Official Title

Shao Pengfei, chief physician, The First Affiliated Hospital with Nanjing Medical UniversitySponsor-Investigator

Lead Sponsor

Shao PengfeiOTHER

Collaborators

Not provided

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

FDAAA801 Violation

Not provided

Description Module

Brief Summary

The goal of this observational study is to improve the management of people with renal tumour by multimodal artificial intelligence(AI). It will also measure the accuracy of the predictions from AI models. The main questions it aims to answer are:

whether the AI module can accurately provide tumor-related information such as Benign or malignant, subtypes, grading, stage, etc. by learning from preoperative CT images.
whether the AI module can help clinicians find out the most suitable surgical programme for people with renal tumor.
whether the AI module can integrate CT images and pathology slides, offering supplementary prognostic information to improve postoperative survival.

Participants who complete a CT(usually Contrast-enhanced CT, CECT) examination and undergo radical or partial nephrectomy will carry out active surveillance and record postoperative survival data for 5 years.

Detailed Description

In this study, AI model will explore and clarify features in renal tumor CT images and pathological images that are difficult to detect manually, and then correlate them with clinical outcomes, thereby improving the diagnosis and treatment process for renal tumors. Firstly, the model can accurately distinguish renal tumor subtypes and predict stage, grade, and complexity so as to svoid misdiagnosis and assist clinicians in formulating treatment plans. Secondly, by learning from surgical videos, the model can provide additional information during surgerys, such as important anatomical landmarks, location of tumors. Finally, combining radiomics and pathomics, the model can differentiate between high-risk and low-risk patients after surgery, thus providing personalized prognostic guidance.

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Yes

Target Follow-Up Duration

5 Years

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

2,100Estimated

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Assessing the performance of AI models by the "AUC" comprehensive assessment model	"AUC" refers to the area under the ROC (Receiver Operating Characteristic) curve, which indicates the performance of the model in predicting immunohistochemistry-related pathological information of prostate cancer after surgery, and the AUC ranges from 0-1, with the larger value indicating the better prediction effect.	From enrollment to the end of 5-years' follow up

Secondary Outcomes

Measure	Description	Time Frame
Assessing the model's performance to predict participants' prognosis post-surgery by Kaplan-Meier Survival Analysis	Kaplan-Meier Survival Analysis s a non-parametric statistic mainly used to figure out factors which indicate survival.	From enrollment to the end of 5-years' follow up

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

Patients with renal tumor which can be treated by surgery;
Complete CECT within 30 days before surgery;
Patients who fully understand this study and sign the informed consent;

Exclusion Criteria:

Patients with any item missing from the baseline clinical and pathological information;
Patients who has already metastasized by the time the tumor is discovered;
Previous treatment in any form, including surgery, targeted therapy and immunotherapy;

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

Not provided

Standard Ages

AdultOlder Adult

Study Population

Patients with renal tumors on imaging examinations who underwent surgery

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Shao Pengfei, Professor	Contact	+8613851925825		spf032@hotmail.com
Miao Haoqi, 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)	Recruiting	Nanjing	Jiangsu	210036	China

Condition Browse Module

MeSH Terms

ID	Term
D007680	Kidney Neoplasms
D002292	Carcinoma, Renal Cell

Ancestor Terms

ID	Term
D014571	Urologic Neoplasms
D014565	Urogenital Neoplasms
D009371	Neoplasms by Site
D009369	Neoplasms

Browse Leaves

Not provided

Browse Branches

Not provided