H‐scan trajectories indicate the progression of specific diseases

Abstract

Purpose 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. Methods 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. Results The multidimensional SVM image classification reached 100% accuracy across the three different studies. Conclusion H‐scan trajectories can be useful to track the progression of multiple classes of diseases, improving diagnosis, staging, and assessing the response to therapy.

Multiparametric ultrasound imaging for early‐stage steatosis: Comparison with magnetic resonance imaging‐based proton density fat fraction

“…To visualize the results of the multiparametric analysis, we propose a multiparametric imaging method to provide a simple color display of the results. Beginning with the concept of disease specific imaging (DSI) 24,27 which utilizes the inner-product and the SVM, we simplify the imaging method. The combined parameter, PC1, is used for color imaging intensity, and we set a threshold to differentiate between normal and fatty tissue (Figure 3a).…”

“…Therefore, recent research utilizes multiparametric analysis, [21][22][23] whereby multiple parameters are measured simultaneously, which can improve the assessment of the degree of steatosis. 20,[24][25][26][27][28] In these studies, the a priori classification of training sets play an important role in defining clusters of parameter values associated with a condition, and the boundaries between different states.…”

Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning

“…Lastly, the disease types and progression levels are assigned unique colors and color intensities overlaid on B-mode images. The DSI framework has been applied to liver diseases, where the SVM can classify liver fibrosis, steatosis, cancers, and normal liver (Baek and Parker 2021a , 2021b ). Once a liver disease category was specified, then its progression level was estimated utilizing the inner product operation within the multidimensional, multiparametric coordinate system.…”

“…Many parameters characterizing tissue signatures have been introduced, for example, several parameters extracted using ultrasound speckle, attenuation from beam propagation, shear wave attenuation and speed, and frequency domain analyses such as the H-scan (Baek et al 2020a , 2020b , 2021b , Basavarajappa et al 2021 , Baek and Parker 2021b ). Multiparametric analysis can be performed to combine more information from these numerous parameters while excluding their dependencies.…”

“…As proposed in our previous work (Baek et al 2021a , Baek and Parker 2021b ), the inner product resulting in projection can compare the similarity of multiple parameters to a reference set of measures associated with a particular disease. This assumes linear trajectories and simple disease models and was found to be effective in animal models.…”