The value of whole volume radiomics machine learning model based on multi-parameter MRI in predicting triple negative breast cancer

43 TNBCs and 84 Non-TNBCs were allocated in this retrospective study. The lesions were manually segmented with ITK-SNAP software then whole volume radiomics features were extracted with Radcloud radiomics platform based on DCE-MRI and ADC maps, respectively. Three prediction models were constructed by using support vector machine (SVM) classifier, including Model A (based on the selected features of ADC maps), Model B (based on the selected features of DCE-MRI), and Model C (based on the selected features of both combined). The radiomics features model combining DCE-MRI and ADC maps can improve the diagnostic performance of predicting TNBC.