摘要
Abstract
Bladder cancer is a relatively common malignant tumor in the urinary system.If no timely treatment measures are taken,it will also affect the patient's prostate tissue,ureter,pelvic lymph nodes,liver,lung,bone,and other tissues and organs,posing a serious threat to the patient's health.Bladder cancer can be divided into non-invasive and invasive types according to the situation of muscular invasion.Clear classification of bladder cancer is helpful to adopt targeted treatment plan.Therefore,accurate determination of the types of muscular invasion of patients with bladder tumor is of great significance in clinical treatment.At present,the main detection method for bladder cancer is postoperative biopsy after transurethral resection of bladder tumor,but it is closely related to the doctor's manipulation,and there is insufficient detection in clinical diagnosis,and sometimes secondary resection is necessary which increase the risk of bladder perforation in patients.With the vigorous development of artificial intelligence,it has also made certain progress in the diagnosis of muscular invasion of bladder cancer,which can improve the accuracy and objectivity of diagnosis,reduce the situation of bladder perforation in patients,and can be used as the basis for targeted treatment by clinicians,so it can prolong the survival period of patients.Based on this,this paper summarized the application value of deep learning based technology in the prediction of muscular invasion of bladder cancer.关键词
深度学习/膀胱癌/肌层侵犯类型/研究进展Key words
deep learning/bladder cancers/types of muscle invasion/research progress分类
临床医学
