Triple-negative and non-triple-negative breast cancer prediction by mammographic radiomics features

Abstract

Objective To develop and validate a radiomics predictive model based on mammogram for preoperative predicting triple-negative breast cancer (TNBC) or non-triple-negative breast cancer (NTNBC). Methods We retrospectively analyzed 459 Chinese women who were diagnosed with invasive breast cancer (confirmed by pathology) during August 2015 to November 2015. Our cohort included 34 TNBC and random selected 102 NTNBC cases. Regions of interest (ROIs) were manually selected from craniocaudal and mediolateral oblique mammograms by radiologists through manual lesion segmentation, and 43 radiomics features were evaluated. Craniocaudal (CC) single-view, mediolateral oblique (MLO) single-view and CC and MLO double-view classification model were constructed respectively. Classification performance was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. Kruskal-Walls U test and t test were used to compare the radiomics features between TNBC and UTNBC. Results The model that used the combination of both the CC and MLO view images achieved the overall best performance than using either of the two views alone, yielding an AUC of 0.791, accuracy of 0.798, sensitivity of 0.776 and specificity of 0.806 for TNBC comparing with NTNBC. Three features were selected by the model (gray scale span and inverse different moment for CC, roundness for MLO) showed a statistical significance (P 0.6 in the subtype classification. Conclusion This research constructed model based on mammograms classification model can effectively distinguish between TNBC and NTNBC. This model has potential value for breast cancer molecular subtype classification and clinical treatment. Key words: Breast neoplasms; Triple-negative breast cancer; Molecular subtypes; Radiomics; Machine learning; Mammogram