Value of Machine Learning with CE-MRI Radiomics for Early Prediction of Pathological Complete Response to Neoadjuvant Therapy in HER2-positive Invasive Breast Cancer

Abstract Backgrouds To assess the value of radiomics based on multiphases contrast-enhanced magnetic resonance imaging(CE-MRI) for early prediction of pathological complete response(pCR) to neoadjuvant therapy(NAT) in patients with human epithelial growth factor receptor 2(HER2) positive invasive breast cancer.Methods A total of 127 patients with newly diagnosed primary HER2 positive invasive breast cancer underwent CE-MRI before NAT and performed surgery after NAT. Radiomic features were extracted from the 1st postcontrast CE-MRI phase(CE1) and multi-phases CE-MRI(CEm). With selected features using a forward stepwise regression, 23 machine learning classifiers based on CE1 and CEm were constructed respectively. The performence of classifiers were assessed and compared. Results For the task of pCR classification, 6 radiomic features from CE1 and 6 from CEm were selected for the final machine learning models respectively. The linear SVM based on CEm outperformed the Logistic regression model using CE1 with an AUC of 0.84 vers 0.69. In mass and non-mass enhancement groups, the accuracy of linear SVM achieved 76% and 84%. Whereas in unifocal and unilateral multifocal cases, 79% and 75% accuracy were achieved by linear SVM. Conclusion Multiphases CE-MRI imaging may offer more heterogeneity information in the tumor and provide a non-invasive approach for early prediction of pCR to NAT in patients with HER2 positive invasive breast cancer.