This study aims to predict expression of estrogen receptor (ER) in breast cancer by radiomics. Firstly, breast cancer images are segmented automatically by phase-based active contour (PBAC) method. Secondly, high-throughput features of ultrasound images are extracted and quantized. A total of 404 high-throughput features are divided into three categories, such as morphology, texture and wavelet. Then, the features are selected by R language and genetic algorithm combining minimum-redundancy-maximum-relevance (mRMR) criterion. Finally, support vector machine (SVM) and AdaBoost are used as classifiers, achieving the goal of predicting ER by breast ultrasound image. One hundred and four cases of breast cancer patients were conducted in the experiment and optimal indicator was obtained using AdaBoost. The prediction accuracy of molecular marker ER could achieve 75.96% and the highest area under the receiver operating characteristic curve (AUC) was 79.39%. According to the results of experiment, the feasibility of predicting expression of ER in breast cancer using radiomics was verified.
本文利用影像组学的方法预测乳腺肿瘤分子标记物雌激素受体（ER）。首先采用基于相位信息的动态轮廓模型（PBAC）对乳腺图像进行分割，其次对乳腺超声图像中肿瘤的形态、纹理、小波三个方面的 404 个高通量特征进行提取并予以量化，然后利用 R 语言以及结合最大相关最小冗余（mRMR）准则的遗传算法进行特征筛选，最后利用支持向量机（SVM）和 AdaBoost 进行分类判别，实现根据乳腺超声图像预测分子病理指标 ER 的目的。对 104 例临床乳腺肿瘤超声图像数据进行实验，在使用 AdaBoost 作为分类器的情况下得到了最优指标，即分子标记物 ER 的预测准确率最高可以达到 75.96%，受试者操作特性曲线下的面积（AUC）最高达到 79.39%。实验结果证明了利用影像组学方法预测乳腺癌 ER 表达情况的可行性。.
Keywords: R language; estrogen receptor; high level features; molecular marker; radiomics.