Improving the diagnosis of breast cancer based on biophysical ultrasound features utilizing machine learning

TL;DR

This study introduces a machine learning framework utilizing biophysical ultrasound features for breast cancer detection, achieving higher accuracy and providing visual probability maps, surpassing existing deep learning methods and radiologists.

Contribution

The paper presents a novel multiparametric ultrasound analysis combined with SVM classification and visual overlay, improving diagnostic accuracy over previous deep learning approaches.

Findings

Accuracy exceeded 98% for classification

Area under ROC curve was 0.98

Outperformed radiologists and existing deep learning systems

Abstract

The improved diagnostic accuracy of ultrasound breast examinations remains an important goal. In this study, we propose a biophysical feature based machine learning method for breast cancer detection to improve the performance beyond a benchmark deep learning algorithm and to furthermore provide a color overlay visual map of the probability of malignancy within a lesion. This overall framework is termed disease specific imaging. Previously, 150 breast lesions were segmented and classified utilizing a modified fully convolutional network and a modified GoogLeNet, respectively. In this study multiparametric analysis was performed within the contoured lesions. Features were extracted from ultrasound radiofrequency, envelope, and log compressed data based on biophysical and morphological models. The support vector machine with a Gaussian kernel constructed a nonlinear hyperplane, and we calculated the distance between the hyperplane and data point of each feature in multiparametric space. The distance can quantitatively assess a lesion, and suggest the probability of malignancy that is color coded and overlaid onto B mode images. Training and evaluation were performed on in vivo patient data. The overall accuracy for the most common types and sizes of breast lesions in our study exceeded 98.0% for classification and 0.98 for an area under the receiver operating characteristic curve, which is more precise than the performance of radiologists and a deep learning system. Further, the correlation between the probability and BI RADS enables a quantitative guideline to predict breast cancer. Therefore, we anticipate that the proposed framework can help radiologists achieve more accurate and convenient breast cancer classification and detection.