Added Value of Radiomics on Mammography for Breast Cancer Diagnosis: A Feasibility Study

Abstract

Background

This study aimed to evaluate whether radiomics can improve the diagnostic performance of mammography compared with that obtained by experienced radiologists.

Methods

This retrospective study included 173 patients (with 74 benign and 99 malignant lesions) who underwent mammography examination before neoadjuvant chemotherapy. Radiomic features were extracted from the mammography image of each patient. Several preprocessing methods, including centering and normalization, were used along with statistical analysis to reduce and select radiomic features. Four machine learning algorithms, namely, support vector machine, logistic regression, K-nearest neighbor, and Bayes classification, were applied to construct a predictive model. An independent testing data set was used to validate the prediction ability of the model. The classification performance was compared with the diagnostic predictions of two breast radiologists who had access to the same mammography cases.

Results

A total of 51 radiomic features remained after the preprocessing. Logistic regression classification presented the best differentiation ability among the four regression models. The diagnostic accuracy, specificity, and sensitivity of the logistic regression model for the training data set were 0.978, 0.975, and 0.983, respectively. The diagnostic accuracy, specificity, and sensitivity for the testing data set were 0.886, 0.900, and 0.867, respectively. The accuracy, specificity, and sensitivity of the combined reading of the two radiologists were 0.772, 0.710, 0.862 in the training data set and 0.769, 0.695, 0.858 in the testing data set, respectively.

Conclusions

Mammography images could be captured and quantified by radiomics, which offers a good diagnostic ability for benign and malignant breast tumors and provides complementary information to radiologists.