基于MRI机器学习在子宫内膜癌风险分层中的研究进展
Research progress of machine learning based on MRI in endometrial cancer risk stratification
杨立赟 1吴献华1
作者信息
- 1. 南通大学附属医院影像科 江苏 南通 226001
- 折叠
摘要
子宫内膜癌(EC)为常见的妇科恶性肿瘤之一,其发病率在不断升高,且近年来有年轻化趋势.术前精准的风险分层有利于手术方式及术后辅助治疗的制定.机器学习是人工智能(AI)领域重要分支之一,随着计算机硬件的持续提升、机器学习算法的不断进步及海量EC临床数据的累积,使得机器学习在EC早期筛查、诊断及预后预测等领域发挥重要作用.本综述对基于MRI机器学习在EC术前风险分层领域中的研究及应用现状进行总结,以期进一步实现个体化精准治疗.
Abstract
Endometrial carcinoma(EC)is a common gynecological malignancy,its incidence rate is rising,and there is a younger trend.Accurate preoperative risk stratification is beneficial for the development of surgical methods and postoperative adjuvant therapy.Machine learning is one of the important branches in the field of artificial intelligence(AI).Through the continuous improvement of computer hardware,the continuous progress of machine learning algorithms and the accumulation of massive clinical data of endometrial cancer,machine learning plays an important role in early screening,diagnosis,and prognosis prediction of endometrial cancer.This review summarizes the research and application status of machine learning based on MRI in the field of preoperative risk stratification of EC,in order to further realize individualized precision treatment.
关键词
子宫内膜癌/风险分层/磁共振成像/影像组学/机器学习Key words
Endometrial cancer/Risk stratification/Magnetic resonance imaging/Radiomics/Machine learning引用本文复制引用
出版年
2024
NETL
NSTL
万方数据