Ultrasound shear wave elastography imaging of common carotid arteries in patients with Spontaneous Coronary Artery Dissection (SCAD)

Background Shear wave elastography (SWE) is emerging as a valuable clinical tool for a variety of conditions. The aim of this pilot study was to assess the potential of SWE imaging of the common carotid arteries (CCA) in patients with spontaneous coronary artery dissection (SCAD), a rare but potentially life-threatening condition, hypothesized to be linked to changes in vessel wall elasticity. Methods Ultrasound shear wave elastography (SWE) estimates of artery wall elasticity were obtained from the left and right CCAs of 89 confirmed SCAD patients and 38 non-dissection controls. SWE images obtained over multiple cardiac cycles were analysed by a blinded observer to estimate elasticity in the form of a Young’s Modulus (YM) value, across regions of interest (ROI) located within the anterior and posterior CCA walls. Results YM estimates ranged from 17 to 133 kPa in SCAD patients compared to 34 to 87 kPa in non-dissection controls. The mean YM of 55 [standard deviation (SD): 21] kPa in SCAD patients was not significantly different to the mean of 57 [SD: 12] kPa in controls, p = 0.32. The difference between groups was 2 kPa [95% Confidence Interval − 11, 4]. Conclusions SWE imaging of CCAs in SCAD patients is feasible although the clinical benefit is limited by relatively high variability of YM values which may have contributed to our finding of no significant difference between SCAD patients and non-dissection controls.

Diagnostic performance of superb microvascular imaging combined with shear-wave elastography in distinguishing invasive ductal carcinoma molecular subtypes

IntroductionTo explore the diagnostic value of combining superb microvascular imaging (SMI), shear-wave elastography (SWE), and Breast Imaging Reporting and Data System (BI-RADS) to distinguish different molecular subtypes of invasive ductal carcinoma (IDC).Material and methodsA total of 239 surgically confirmed IDC masses in 201 patients underwent conventional ultrasound, SMI, and SWE examination, the information such as echo pattern, posterior features, margins, SMI pixels, and hardness of the masses was recorded. According to the St. Gallen standard, breast masses were classified as Luminal A, Luminal B, HER2 overexpression, and triple-negative subtype. We further explored the differences between different molecular subtypes of IDC.ResultsLuminal A subtype had the following characteristics: low histologic grade, posterior acoustic shadowing (p= 0.019), spiculated margins (p<0.001) , and relatively soft. Luminal B subtype was characterized by low histological grade (p <0.0001), posterior acoustic shadowing or indifference, and indistinct margins. HER2 overexpression breast cancers were characterized by high histological grade, enhanced posterior acoustics or indifference, calcifications (p= 0.005), spiculated or indistinct margins, vascularity (p=0.005), and relative stiffness. Triple-negative breast cancers had the characteristics of high histological grade, posterior echogenic enhancement, lack of calcifications, circumscribed or microlobulated margins, low blood flow signals, and stiff tissue (p=0.013).ConclusionsOur study demonstrated the significant differences and trends among the IDC four subtypes by the combined application of SMI, SWE, and BI-RADS lexicon, which are of great significance for early diagnosis, selection of treatment methods, and evaluation of prognosis of IDC.

Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review

Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further extended its power by facilitating automated segmentation and tumour classification. The objective of this review was to summarize application of the machine learning model to ultrasound elastography systems for breast tumour classification. Review databases included PubMed, Web of Science, CINAHL, and EMBASE. Thirteen (n = 13) articles were eligible for review. Shear-wave elastography was investigated in six articles, whereas seven studies focused on strain elastography (5 freehand and 2 Acoustic Radiation Force). Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks or support vector machines (SVM). Shear-wave elastography often adopts the deep learning model, convolutional neural network (CNN), that integrates functional tasks. All of the reviewed articles achieved sensitivity ³ 80%, while only half of them attained acceptable specificity ³ 95%. Deep learning models did not necessarily perform better than traditional computer vision workflow. Nevertheless, there were inconsistencies and insufficiencies in reporting and calculation, such as the testing dataset, cross-validation, and methods to avoid overfitting. Most of the studies did not report loss or hyperparameters. Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.

Reliability of Shear Wave Elastography for the Assessment of Gastrocnemius Fascia Elasticity in Healthy Individual

The mechanical properties of deep fascia (i.e. an index of stiffness) strongly affect the development of muscle pathologies, and muscular actions, such as compartment syndromes. Actually, a clear understanding of the mechanical characterization of muscle deep fascia still lacks. The present study focuses on examining the reliability of ultrasonic shear wave elastography device (USWE) in quantifying the shear modulus of gastrocnemius fascia in healthy individual and the device’s abilities to examine the shear modulus of gastrocnemius deep fascia during ankle dorsiflexion. Twenty-one healthy males participated in the study (age: 21.48±1.17 years). The shear modulus of the medial gastrocnemius fascia (MGF) and lateral gastrocnemius fascia (LGF) were quantified at different angles using USWE during passive lengthening. The operators took turns to measure each subject’s MGF and LGF over 1-hour period and by operator B with a 2-hour interval. In the intra-operator test, the same subjects participated at the same time 5 days later. The intra-rater [ Intra-class correlation coefficient (ICC) = 0.846-0.965)] and inter-rater (ICC = 0.877-0.961) reliabilities for measuring the shear modulus of the MGF and LGF were rated as both excellent, and the standard error in measurement (SEM) was 3.49 kPa, the minimal detectable change (MDC) was 9.68 kPa. Regardless of the ankle angle, the shear modulus of the LGF were significant greater than that of the MGF (p < 0.001). The significant increase in the shear modulus both of the MGF and LGF were observed at neutral position compared to the relaxed position. This results indicate that the USWE is a technique to assess the shear modulus of gastrocnemius fascia and detect its dynamic changes during ankle dorsiflexion. USWE can be used for biomechanical study and intervention experiments of deep fascia.

Shear Wave Elastography-Assisted Ultrasound Breast Image Analysis and Identification of Abnormal Data

In this paper, shear wave elastography was used to study and analyze the images of the breast in-depth and identify the abnormal image data. Sixty breast lesions were evaluated, and quantitative metrics were reproducible in the static and dynamic modes of shear wave elastography with a higher interobserver agreement in dynamic qualitative metrics than in the static mode. There were no statistically significant differences between the two modes of imaging in quantitative metrics, and quantitative metrics were more effective than qualitative metrics. Postoperative immunohistochemical expression of ER, PR, HER-2, Ki-67, molecular typing, pathological type, histological grading, and axillary lymph node status of breast cancer was obtained based on pathological results. The correlation between mass size, patient age, and WiMAX values of breast cancer masses was analyzed using Pearson correlation, and the differences in SWVmax values of breast cancer masses between different expressions of immunohistochemical parameters ER, PR, HER-2, Ki-67, and axillary lymph node status were compared using tests. The variables with correlations and differences were included in the multiple linear regression analysis to assess the factors influencing the SWVmax values. The performance of TDPM, SPM, and TSPM was compared using PVA body models with different freeze-thaw cycles. The results showed that TSPM performed better than SPM in general, and TDPM showed excellent performance because of high temporal resolution and low random error, especially when the number of freeze-thaw cycles increased and the hardness of the PVA body mold increased. Measurements at different depths of inhomogeneous body models also showed that the TDPM method was less affected by depth, and the results were more stable. Finally, the reliability of the shear wave velocity (SWS) measured by the TDPM and SPM methods was investigated using porcine ligament tissue, and the results showed that the mean values of SWS goodness of fit for TDPM and SPM were 0.94 and 0.87, respectively, and the estimated elastic modulus of TDPM was very close to the mechanical test results.