Clinical trials

The detection rate of renal masses smaller than 7 cm has significantly increased in recent years. To preserve postoperative renal function to the greatest extent possible, guidelines from the European Association of Urology (EAU), the National Comprehensive Cancer Network (NCCN), and others have endorsed partial nephrectomy (PN) as the preferred treatment strategy for small renal masses. In conventional PN, it is necessary to clamp the renal artery or its branch arteries and employ a double-layer suturing technique to close the resection bed. This controls bleeding, maintains a clear surgical field, and prevents postoperative urinary leakage. The maximum safe duration of warm ischemia to avoid irreversible renal parenchymal damage remains controversial, though most studies indicate a window of 20-30 minutes. Consequently, the "off-clamp sutureless" concept has gained prominence. Its core principle is to avoid renal artery clamping and replace suturing with novel haemostatic techniques, thereby maximizing the preservation of healthy renal parenchyma. With the diversification of haemostatic material options and the widespread adoption of robotic-assisted systems, the off-clamp sutureless strategy has become technically feasible for small renal masses with low complexity . Multiple retrospective studies demonstrate that the off-clamp sutureless technique is non-inferior, offering safety and surgical outcomes comparable to conventional robot-assisted partial nephrectomy (RAPN). However, it is important to note that current research predominantly focuses on tumors ≤4 cm, is largely retrospective, and suffers from limited sample sizes. More robust, evidence-based medical evidence is required to support its application for larger tumors or those with complex anatomy.

The goal of this study is to develop a real-time artificial intelligence-driven 3D kidney model to assist robotic or laparoscopic partial nephrectomy: • Can this AI-powered model optimize the workflow of partial nephrectomy and enhance surgical benefits?

The goal of this observational study is to develop a CNN-based machine module to predict postoperative fractional renal function in people who are proposed to undergo partial nephrectomy. The main question it aims to answer is: • Does this machine learning model accurately predict renal function after partial nephrectomy?

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.

The aim of this clinical trial is whether artificial intelligence models can be used for accurate clinical preoperative diagnosis and postoperative diagnosis of pathological findings, and will also measure the accuracy of the predictions made by the artificial intelligence models.The main target questions addressed by the model building are: 1. whether the AI model can learn from preoperative MRI and postoperative Whole Slide Images so as to accurately predict information such as benignness or malignancy, aggressiveness, grading, subtypes, genes, etc. for participants suspected of having prostate cancer preoperatively/puncturally. 2. whether the AI model is capable of learning postoperative macropathology slides to enable outcome diagnosis of surgical pathology slides in new participants. Participants will: 1. complete an MRI examination and have their MRI images analysed by the established AI model to make an accurate diagnosis of them. 2. Based on the diagnosis, if prostate cancer is predicted, they will undergo radical prostate cancer surgery and refine their surgical pathology.