Evaluating the HER-2 status of breast cancer using mammography radiomics features

Purpose: The aim of our study was to evaluate the HER-2 status in breast cancer patients using mammography (MG) radiomics features.

Methods: A total of 306 Chinese female patients with invasive ductal carcinoma of no special type (IDC-NST) enrolled from January 2013 to July 2018 were divided into a training set (n = 244) and a testing set (n = 62). One hundred and eighty-six radiomics features were extracted from digital MG images based on the training set. The least absolute shrinkage and selection operator (LASSO) method was used to select the optimal predictive features for HER-2 status from the training set. Both support vector machine (SVM) and logistic regression models were employed based on the selected features. The area under the receiver operating characteristic (ROC) curves (AUCs) of the training set and testing set were used to evaluate the predictive performance of the models.

Results: Compared with the SVM model, the performance of the logistic regression model using a combination of cranial caudal (CC) and mediolateral oblique (MLO) MG views was optimal. In the training set, the sensitivity, specificity, accuracy and area under the curve (AUC) values of the logistic regression model for evaluating HER-2 status based on quantitative radiomics features were 87.29%, 58.73%, 80.00% and 0.846 (95% confidence interval (CI), 0.800-0.887), respectively, and in the testing set, the values were 73.91%, 68.75%, 77.00% and 0.787 (95% CI, 0.673-0.885), respectively.

Conclusions: Radiomics features could be an efficient tool for the preoperative evaluation of HER-2 status in patients with breast cancer.