基于深度学习的膀胱癌肌层侵犯预测研究进展
Research Progress on Prediction of Muscular Invasion of Bladder Cancer Based on Deep Learning
李娜 1仇度旺 1赵俊雅1
作者信息
- 1. 250200 山东济南,济南市章丘区人民医院CT室
- 折叠
摘要
膀胱癌是泌尿系统中较为多见的一种恶性肿瘤,如不及时采取治疗措施,则会累及患者的前列腺组织、输尿管、盆腔淋巴结、肝脏、肺、骨等组织器官,对患者的身体健康造成严重威胁.根据肌层侵犯情况可将膀胱癌分为非肌层浸润型和肌层浸润型,明确膀胱癌的分型有助于采取针对性治疗方案,故准确判断患者膀胱肿瘤肌层侵犯类型在临床治疗上具有重要意义.目前,关于膀胱癌检测方法主要为经尿道膀胱肿瘤电切术后活检,但其与医生的操作手法有密切关联,在临床诊断中存在检测不足的情况,有时需要进行二次电切,增加患者膀胱穿孔等风险.随着人工智能的大力发展,其在膀胱癌肌层侵犯诊断中也取得了一定发展,可提高诊断的准确性、客观性,降低患者膀胱穿孔情况,可作为临床医生针对性治疗的依据,故可延长患者的生存.基于此,本文总结了基于深度学习技术在膀胱癌肌层侵犯预测情况中的应用价值.
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引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据