An Efficient Algorithm for Distinguishing Triple Negative Breast Cancer with Ultrasound Image and High-dimensional Radiomics Data

Abstract

Triple negative breast cancer develops rapidly with high mortality and limited treatment. Therefore, it is important to detect and diagnose breast cancer in a timely manner. However, breast tumors in ultrasound images are characterized by irregular shapes, large-scale changes, and blurry boundaries, which bring great challenges to the segmentation and classification of breast ultrasound images. In this paper, we propose an approach for ultrasound image segmentation and classification of Triple Negative Breast Cancer (TNBC) and Non-Triple Negative Breast Cancer (NTNBC) based on deep learning and radiomics. Regarding the deep learning framework, we propose a benign and malignant segmentation model based on the U-Net model for Channel and Spatial Efficient U-Net (CSE-U-Net) breast ultrasounds. First, during the skip connection stage, to obtain more spatial and contextual semantic information, we implemented a channel attention module (CM) and spatial awareness module (SM). Then, Efficient Channel Attention (ECA) is added to the feature extraction part of the U-Net backbone network to ensure model robustness. Regarding the radiomics, we extract image features using histogram, texture, and filter features, and compare them using six machine learning classification algorithms. Experimental results indicate that CSE-U-Net is more effective and accurate in many evaluation indexes; among the six Machine Learning (ML) algorithms evaluated, the extreme gradient boosting algorithm was able to classify breast cancer more accurately into TNBC and NTNBC than the other five algorithms evaluated. The prediction results show that the CSE-U-Net and ML algorithms are efficient and can be used for the segmentation and classification of breast cancer into TNBC and NTNBC.