MRI影像组学和深度学习在植入性胎盘谱系疾病中的研究进展  

Research progress of radiomics and deep learning in placenta accrete spectrum disorders

作　　者：许晓阳 刘碧华(审校) 郑昌业 XU Xiaoyang;LIU Bihua;ZHENG Changye(Guangdong Medical University,Zhanjiang 524000,China;Dongguan People’s Hospital)

机构地区：[1]广东医科大学,湛江524000 [2]东莞市人民医院

出　　处：《国际医学放射学杂志》2024年第1期88-91,共4页International Journal of Medical Radiology

基　　金：东莞市社会科技发展项目(201950715001499)。

摘　　要：植入性胎盘谱系疾病(PAS)的产前诊断和预测对于改善孕妇临床结局尤为重要。MRI在PAS的诊断中具有重要价值,但在评估分型和预测预后等方面价值有限。影像组学和深度学习可以从医学影像中提取高通量特征用于定量分析病变异质性,已逐步应用于PAS的诊断、分型、评估预后风险等方面。就影像组学和深度学习在PAS中的研究进展予以综述。Prenatal diagnosis and prediction of prenatal accreta spectrum(PAS)disorders are particularly important for improving clinical outcomes of pregnant women.MRI has significant value in the diagnosis of PAS,but is of limited value in evaluating typing and predicting prognosis.Radiomics and deep learning can extract high throughput features from medical images for quantitative analysis of lesion heterogeneity,and has been gradually applied to the diagnosis,typing,and assessment of prognostic risk of PAS disorders.The research progress of radiomics and deep learning in PAS was reviewed.

关 键 词：胎盘植入 磁共振成像 影像组学 深度学习 

分 类 号：R[医药卫生]