基于影像组学的肝脏脂肪变性研究

目的 利用影像组学与集成学习进行肝脏脂肪变性分级研究.方法 回顾性分析2018年6月至8月于上海市第一人民医院进行MR上腹部mDixon成像序列扫描的成人患者资料,将患者的MRI数据利用影像组学特征提取方法和机器学习进行建模,研究采用3项指标对三种集成学习分类算法(AdaBoost、GBDT与XGBoost)的性能进行...

Analysis and Classification Hepatic Steatosis Using Radiomics Analysis of MRI

Objective To study the classification of hepatic steatosis using radiomics and ensemble learning.Methods A retrospective study was conducted on the datum of adult patients who underwent abdomen MRI with mDixon sequence scanning from June 2018 to August 2018 in Shanghai General Hospital.The MRI data of patients were modeled by using the method of radiomics feature extraction and machine learning.Three indexes were used to evaluate the performance of three ensemble learning classification algorithms(AdaBoost,GBDT and XGBoost),including the rate of accuracy and recall.Results The XGBoost algorithm had the best performance,the classification accuracy was 81.9%,and the sum of the importance of the five features was greater than 19%,which meant that the weight of the total liver fat deformation degree classification model was close to 1/5.Conclusion The method of combining radiomics and ensemble learning provides a more reliable auxiliary diagnostic means for the classification of steatosis.The study of mild to moderate steatosis can also provide a certain reference value for clinical intervention or treatment opportunity of lipid metabolism related diseases.