Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York, USA.
Med Phys. 2021 Sep;48(9):5047-5058. doi: 10.1002/mp.15108. Epub 2021 Aug 3.
The ability of ultrasound to assess pathology is increasing with the development of quantitative parameters. Among these are a set of parameters derived from the recent H-scan analysis of subresolvable scattering. The emergence of these quantitative measures of tissue/ultrasound interactions now enables a study of the unique trajectories of multiparametric features in multidimensional space, representing the progression of specific diseases over time. We develop the mathematical and visual tools that are effective for classifying, quantifying, and visualizing the steady progression of several diseases from independent studies, all within a uniform framework.
After applying the H-scan analysis of ultrasound echoes, we trained a support vector machine (SVM) to classify the unique trajectories of progressive liver disease from fibrosis, steatosis, and pancreatic ductal adenocarcinoma (PDAC) metastasis. Our approaches include the development of trajectory maps and disease-specific color imaging stains.
The multidimensional SVM image classification reached 100% accuracy across the three different studies.
H-scan trajectories can be useful to track the progression of multiple classes of diseases, improving diagnosis, staging, and assessing the response to therapy.
随着定量参数的发展，超声评估病理学的能力不断提高。其中包括最近从亚分辨散射的 H 扫描分析中得出的一组参数。这些组织/超声相互作用的定量测量方法的出现，现在可以研究多维空间中多参数特征的独特轨迹，这些轨迹代表了特定疾病随时间的进展。我们开发了有效的数学和可视化工具，用于在统一框架内对来自独立研究的几种疾病的稳定进展进行分类、量化和可视化。
在对超声回波进行 H 扫描分析后，我们训练了一个支持向量机 (SVM)，以对纤维化、脂肪变性和胰腺导管腺癌 (PDAC) 转移的进行性肝病的独特轨迹进行分类。我们的方法包括轨迹图和疾病特异性彩色成像染色的开发。
三种不同研究中，多维 SVM 图像分类的准确率达到 100%。
H 扫描轨迹可用于跟踪多种疾病的进展，从而改善诊断、分期和评估治疗反应。
