1. School of Biological Sciences and Medical Engineering, Southeast University, Nanjing Jiangsu 210096, China丨
2. Department of Radiology, Shanghai General Hospital, Shanghai 201620, China
Abstract：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.
