Optimising Renal Tumour Management Through Artificial Intelligence Modules

NCT06714916 | Recruiting DMC

• 1 other identifier: 2024-SR-961
• Study Type: observational
• Target: 2,100
• Locations: 1 country
• Sites: 1

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:

1. 1.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.
2. 2.whether the AI module can help clinicians find out the most suitable surgical programme for people with renal tumor.
3. 3.whether the AI module can integrate CT images and pathology slides, offering supplementary prognostic information to improve postoperative survival.

Conditions

Renal Neoplasms; Pathology; Renal Cell Cancer

Keywords

Articicial Intelligence; Renal tumors; Prediction Model; Surgery; Radiomics; Pathomics

Outcome Measures

Primary Outcomes (1)

• 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 (1)

• Assessing the model's performance to predict participants' prognosis post-surgery by Kaplan-Meier Survival Analysis

  From enrollment to the end of 5-years' follow up

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with renal tumors on imaging examinations who underwent surgery

You may qualify if:

• 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;

You may not qualify if:

• 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;

Sponsors & Collaborators

• Shao Pengfei: lead

Study Sites (1)

The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)

Nanjing, Jiangsu, 210036, China

RECRUITING

MeSH Terms

Conditions

Kidney Neoplasms; Carcinoma, Renal Cell

Condition Hierarchy (Ancestors)

Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Kidney Diseases; Urologic Diseases; Male Urogenital Diseases; Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type

Central Study Contacts

Shao Pengfei, Professor

CONTACT

+8613851925825; spf032@hotmail.com

Miao Haoqi, Postgraduate

CONTACT

+8613276636957; mhq@stu.njmu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 5 Years
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: chief physician

Study Record Dates

• First Submitted: December 2, 2024
• First Posted: December 4, 2024
• Study Start: January 1, 2025
• Primary Completion (Estimated): January 1, 2028
• Study Completion (Estimated): December 31, 2033
• Last Updated: March 19, 2025

Record last verified: 2024-12

Locations

CN (1)