Optimising Renal Tumour Management Through Artificial Intelligence Modules

Phase

N/A

Condition

Renal Cell Carcinoma

Urothelial Cancer

Renal Cell Cancer

Treatment

N/A

Clinical Study ID

NCT06714916

2024-SR-961

Ages > 18
All Genders

Study 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.

Eligibility Criteria

Inclusion

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

Exclusion Criteria:

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

Study Design

January 01, 2025

December 31, 2033

Study 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.

Connect with a study center

The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)
Nanjing, Jiangsu 210036
China
Active - Recruiting

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