Optimising Renal Tumour Management Through Artificial Intelligence Modules

March 16, 2025 updated by: Shao Pengfei

Mutimodal Artificial Intelligence for Optimising Renal Tumour Management: Diagnosis, Surgery and Prognosis

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.

Study Overview

Status

Recruiting

Conditions

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.

Study Type

Observational

Enrollment (Estimated)

2100

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Name: Shao Pengfei, Professor
Phone Number: +8613851925825
Email: spf032@hotmail.com

Study Contact Backup

Name: Miao Haoqi, Postgraduate
Phone Number: +8613276636957
Email: mhq@stu.njmu.edu.cn

Study Locations

China
- Jiangsu
  - Nanjing, Jiangsu, China, 210036
    - Recruiting
    - The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)
    - Contact:
      
      Shao Pengfei, Professor
      
      Phone Number: +8613851925825
      
      Email: spf032@hotmail.com
    - Contact:
      
      Miao Haoqi, Postgraduate
      
      Phone Number: +8613276636957
      
      Email: mhq@stu.njmu.edu.cn

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

Adult
Older Adult

Accepts Healthy Volunteers

Sampling Method

Non-Probability Sample

Study Population

Patients with renal tumors on imaging examinations who underwent surgery

Description

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;

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Assessing the performance of AI models by the "AUC" comprehensive assessment model Time Frame: From enrollment to the end of 5-years' follow up	"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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Assessing the model's performance to predict participants' prognosis post-surgery by Kaplan-Meier Survival Analysis Time Frame: From enrollment to the end of 5-years' follow up	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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Shao Pengfei

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2025

Primary Completion (Estimated)

January 1, 2028

Study Completion (Estimated)

December 31, 2033

Study Registration Dates

First Submitted

December 2, 2024

First Submitted That Met QC Criteria