Clinical trial · Observational
Optimising Renal Tumour Management Through Artificial Intelligence Modules
Mutimodal Artificial Intelligence for Optimising Renal Tumour Management: Diagnosis, Surgery and Prognosis
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Pathology | — | UNRESOLVED | — |
| Renal Cell Cancer | Renal Cell Carcinoma | CURATED_BROADER | 0.80 |
| Renal Neoplasms | Kidney Neoplasm | ALIAS | 0.90 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Assessing the performance of AI models by the "AUC" comprehensive assessment model
- timeFrame
- From enrollment to the end of 5-years' follow up
- description
- "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.
Secondary outcomes (1)
- measure
- Assessing the model's performance to predict participants' prognosis post-surgery by Kaplan-Meier Survival Analysis
- timeFrame
- From enrollment to the end of 5-years' follow up
- description
- Kaplan-Meier Survival Analysis s a non-parametric statistic mainly used to figure out factors which indicate survival.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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;
References
Publications (0)
Data not yet available