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.
Age range
30 Years
Sex
MALE
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A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
Prediction of postradical prostate cancer pathology after radical prostatectomy using the 'AUC' comprehensive assessment model
Timeframe: From subject enrolment to initial post-surgery, usually 30-90 days.
Predicting the performance of post-radical pathology by the 'AUC' comprehensive assessment model
Timeframe: From subject enrolment to initial post-surgery, usually 30-90 days.
'F1 Score' to assess performance of preoperative 3D modelling
Timeframe: From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days.