作者
Zhiying He, Yitao Mao, Shanhong Lu, Lei Tan, Juxiong Xiao, Pingqing Tan, Hailin Zhang, Guo Li, Helei Yan, Jiaqi Tan, Donghai Huang, Yuanzheng Qiu, Xin Zhang, Xingwei Wang, Yong Liu
发表日期
2022/6/24
期刊
European Radiology
页码范围
1-12
出版商
Springer Berlin Heidelberg
简介
Objectives
To evaluate the effectiveness of machine learning models based on morphological magnetic resonance imaging (MRI) radiomics in the classification of parotid tumors.
Methods
In total, 298 patients with parotid tumors were randomly assigned to a training and test set at a ratio of 7:3. Radiomics features were extracted from the morphological MRI images and screened using the Select K Best and LASSO algorithm. Three-step machine learning models with XGBoost, SVM, and DT algorithms were developed to classify the parotid neoplasms into four subtypes. The ROC curve was used to measure the performance in each step. Diagnostic confusion matrices of these models were calculated for the test cohort and compared with those of the radiologists.
Results
Six, twelve, and eight optimal features were selected in each step of the three-step process, respectively. XGBoost produced the highest area under …
引用总数