Optimising Renal Tumour Management Through Artificial Intelligence Modules
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Renal Neoplasms, Pathology, Renal Cell Cancer. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Mutimodal Artificial Intelligence for Optimising Renal Tumour Management: Diagnosis, Surgery and Prognosis
Overview
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.
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.
Primary outcome measures
- Assessing the performance of AI models by the "AUC" comprehensive assessment model [Time frame: From enrollment to the end of 5-years' follow up]
Secondary outcome measures (1)
- 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]
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;
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's H — Nanjing
Identifiers
NCT: NCT06714916 · 2024-SR-961