Optimising Renal Tumour Management Through Artificial Intelligence Modules

Condition(s) studied

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:

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

Primary outcome measure(s)

• Assessing the performance of AI models by the "AUC" comprehensive assessment model — From enrollment to the end of 5-years' follow up
  "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.

Trial sites (1)

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