Optimising Renal Tumour Management Through Artificial Intelligence Modules
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorShao Pengfei
About this trial
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. 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. whether the AI module can help clinicians find out the most suitable surgical programme for people with renal tumor. 3. 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
This trial does not accept healthy volunteersQualifiers
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;
Disqualifiers
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;
Trial population
Patients with renal tumors on imaging examinations who underwent surgery
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Not listed
Trial groups
No trial groups listed
Trial outcomes
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.
Time frame
From enrollment to the end of 5-years' follow upSecondary outcomes
1
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.
Time frame
From enrollment to the end of 5-years' follow upSponsors and contacts
Click on the lead sponsor to view all of their trials.
Shao Pengfei
Lead sponsor
The First Affiliated Hospital with Nanjing Medical University
Sponsor institution
Source ClinicalTrials.gov