In shear wave absolute vibro-elastography (S-WAVE), a steady-state multi-frequency external mechanical excitation is applied to tissue, while a time-series of ultrasound radio-frequency (RF) data are acquired. Our objective is to determine the potential of S-WAVE to classify breast tissue lesions as malignant or benign. We present a new processing pipeline for feature-based classification of breast cancer using S-WAVE data, and we evaluate it on a new data set collected from 40 patients. Novel bi-spectral and Wigner spectrum features are computed directly from the RF time series and are combined with textural and spectral features from B-mode and elasticity images. The Random Forest permutation importance ranking and the Quadratic Mutual Information methods are used to reduce the number of features from 377 to 20. Support Vector Machines and Random Forest classifiers are used with leave-one-patient-out and Monte Carlo cross-validations. Classification results obtained for different feature sets are presented. Our best results (95% confidence interval, Area Under Curve = 95%±1.45%, sensitivity = 95%, and specificity = 93%) outperform the state-of-the-art reported S-WAVE breast cancer classification performance. The effect of feature selection and the sensitivity of the above classification results to changes in breast lesion contours is also studied. We demonstrate that time-series analysis of externally vibrated tissue as an elastography technique, even if the elasticity is not explicitly computed, has promise and should be pursued with larger patient datasets. Our study proposes novel directions in the field of elasticity imaging for tissue classification.

中文翻译：

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使用来自超声剪切波绝对振动弹性成像的多模态时间序列特征检测乳腺癌

在剪切波绝对振动弹性成像 (S-WAVE) 中，对组织施加稳态多频外部机械激励，同时获取时间序列的超声射频 (RF) 数据。我们的目标是确定 S-WAVE 将乳腺组织病变分类为恶性或良性的潜力。我们提出了一种新的处理流程，用于使用 S-WAVE 数据对乳腺癌进行基于特征的分类，并根据从 40 名患者收集的新数据集对其进行评估。新颖的双光谱和 Wigner 光谱特征直接从 RF 时间序列计算，并与 B 模式和弹性图像的纹理和光谱特征相结合。随机森林排列重要性排名和二次互信息方法用于将特征数量从 377 减少到 20。支持向量机和随机森林分类器与留一病人和蒙特卡洛交叉验证一起使用。给出了针对不同特征集获得的分类结果。我们的最佳结果（95% 置信区间，曲线下面积 = 95%±1.45%，灵敏度 = 95%，特异性 = 93%）优于最新报告的 S-WAVE 乳腺癌分类性能。还研究了特征选择的效果和上述分类结果对乳腺病变轮廓变化的敏感性。我们证明，外部振动组织的时间序列分析作为一种弹性成像技术，即使没有明确计算弹性，也有希望，应该在更大的患者数据集上进行。我们的研究为组织分类的弹性成像领域提出了新的方向。