Elastography Imaging Research Articles

A convolutional attention model for predicting response to chemo-immunotherapy from ultrasound elastography in mouse tumor models

BackgroundIn the era of personalized cancer treatment, understanding the intrinsic heterogeneity of tumors is crucial. Despite some patients responding favorably to a particular treatment, others may not benefit, leading to the varied efficacy observed in standard therapies. This study focuses on the prediction of tumor response to chemo-immunotherapy, exploring the potential of tumor mechanics and medical imaging as predictive biomarkers. We have extensively studied “desmoplastic” tumors, characterized by a dense and very stiff stroma, which presents a substantial challenge for treatment. The increased stiffness of such tumors can be restored through pharmacological intervention with mechanotherapeutics.MethodsWe developed a deep learning methodology based on shear wave elastography (SWE) images, which involved a convolutional neural network (CNN) model enhanced with attention modules. The model was developed and evaluated as a predictive biomarker in the setting of detecting responsive, stable, and non-responsive tumors to chemotherapy, immunotherapy, or the combination, following mechanotherapeutics administration. A dataset of 1365 SWE images was obtained from 630 tumors from our previous experiments and used to train and successfully evaluate our methodology. SWE in combination with deep learning models, has demonstrated promising results in disease diagnosis and tumor classification but their potential for predicting tumor response prior to therapy is not yet fully realized.ResultsWe present strong evidence that integrating SWE-derived biomarkers with automatic tumor segmentation algorithms enables accurate tumor detection and prediction of therapeutic outcomes.ConclusionsThis approach can enhance personalized cancer treatment by providing non-invasive, reliable predictions of therapeutic outcomes.

Ultrasound strain elastography to improve diagnostic performance of breast lesions by reclassifying BI-RADS 3 and 4a lesions: a multicenter diagnostic study.

To investigate the added value of strain elastography (SE) by recategorizing ultrasound (US) Breast Imaging Reporting and Data System (BI-RADS) 3 and 4a lesions. A total of 4371 patients underwent US and SE with BI-RADS 2-5 categories solid breast lesions were included from thirty-two hospitals. We evaluated the elastographic images according to elasticity scores (ES) and strain ratios (SR). Three combined methods (BI-RADS+ES, BI-RADS+SR, BI-RADS+ES+SR) and two reclassified methods were used (method one: upgrading BI-RADS 3 and downgrading BI-RADS 4a, method two: downgrading BI-RADS 4a alone). The diagnostic performance and the potential reduction of unnecessary biopsies were evaluated. Combining BI-RADS with SE had a higher area under the curve (AUC) than BI-RADS alone (0.822-0.898 vs. 0.794, P<0.01). For reclassified method one, the sensitivity, specificity, and accuracy were 99.36%, 66.70%, 78.36% for BI-RADS+ES and 98.01%, 66.45%, 77.72% for BI-RADS+SR, and 99.42%, 66.70%, 78.38% for BI-RADS+ES+SR, respectively. For reclassified method two, the sensitivity, specificity, and accuracy were 99.17%, 70.72%, 80.87% for BI-RADS+ES and 97.76%, 81.75%, 87.46% for BI-RADS+SR, and 99.23%, 69.83%, 80.32% for BI-RADS+ES+SR, respectively. Downgrading BI-RADS 4a alone had higher AUC, specificity, and accuracy (P<0.01) and similar sensitivity (P>0.05) to upgrading BI-RADS 3 and downgrading BI-RADS 4a. Combining SE with BI-RADS could help reduce unnecessary biopsies by 17.64%-55.20%. Combining BI-RADS with SE improved the diagnostic performance in distinguishing benign from malignant lesions and could decrease false-positive breast biopsy rates. Downgrading BI-RADS 4a lesions alone might be sufficient for achieving good diagnostic performance. Downgrading BI-RADS category 4a lesions alone had higher AUC, specificity, and accuracy, and similar sensitivity to upgrading or downgrading BI-RADS category 3 and 4a lesions.

Improving the diagnostic strategy for thyroid nodules: a combination of artificial intelligence-based computer-aided diagnosis system and shear wave elastography.

Thyroid nodules are highly prevalent in the general population, posing a clinical challenge in accurately distinguishing between benign and malignant cases. This study aimed to investigate the diagnostic performance of different strategies, utilizing a combination of a computer-aided diagnosis system (AmCAD) and shear wave elastography (SWE) imaging, to effectively differentiate benign and malignant thyroid nodules in ultrasonography. A total of 126 thyroid nodules with pathological confirmation were prospectively included in this study. The AmCAD was utilized to analyze the ultrasound imaging characteristics of the nodules, while the SWE was employed to measure their stiffness in both transverse and longitudinal thyroid scans. Twelve diagnostic patterns were formed by combining AmCAD diagnosis and SWE values, including isolation, series, parallel, and integration. The diagnostic performance was assessed using the receiver operating characteristic curve and area under the curve (AUC). Sensitivity, specificity, accuracy, missed malignancy rate, and unnecessary biopsy rate were also determined. Various diagnostic schemes have shown specific advantages in terms of diagnostic performance. Overall, integrating AmCAD with SWE imaging in the transverse scan yielded the most favorable diagnostic performance, achieving an AUC of 72.2% (95% confidence interval (CI): 63.0-81.5%), outperforming other diagnostic schemes. Furthermore, in the subgroup analysis of nodules measuring <2 cm or 2-4 cm, the integrated scheme consistently exhibited promising diagnostic performance, with AUCs of 74.2% (95% CI: 61.9-86.4%) and 77.4% (95% CI: 59.4-95.3%) respectively, surpassing other diagnostic schemes. The integrated scheme also effectively addressed thyroid nodule management by reducing the missed malignancy rate to 9.5% and unnecessary biopsy rate to 22.2%. The integration of AmCAD and SWE imaging in the transverse thyroid scan significantly enhances the diagnostic performance for distinguishing benign and malignant thyroid nodules. This strategy offers clinicians the advantage of obtaining more accurate clinical diagnoses and making well-informed decisions regarding patient management.

Multimodal Photoacoustic/Elastography Imaging for the Detection of Acute Radiation Dermatitis in Breast Radiation Therapy

Purpose: This study evaluates whether photoacoustic (PA), sound touch elastography (STE), and viscoelasticity (VE) can distinguish between normal and abnormal post-radiotherapy breast skin and compare these methods with RTOG criteria at a 20 Gy threshold. Methods: Patients meeting inclusion and exclusion criteria underwent PA, STE, and VE on the same day. Collected data included radiation dose, molecular type, RTOG, Fitzpatrick skin type, pathology, neoadjuvant chemotherapy status, TNM classification, surgical procedures, primary breast cancer location, BMI, and age. A sample of 41 patients was determined using a two-sample t-test. Statistical tools like T-tests, variance analysis, rank sum tests, and chi-square tests, along with random forest analysis and ROC curves, were used to evaluate the radiation dose effects. Results: Data from 66 patients showed significant differences in parameters such as dermis and subcutaneous tissue oxygen saturation, dermal thickness, and skin elasticity (p-values < 0.05). However, minimum values of oxygen saturation and some photoacoustic measures were not significantly different. Notably, at a 20 Gy radiation threshold, significant variations in oxygen saturation, abnormal dermal thickness, and skin STE and VE were observed, proving more accurate than RTOG grading. Conclusion: The study confirms the effectiveness of PA and elastography imaging in differentiating normal from abnormal breast tissue and assessing radiation-induced changes, highlighting the potential of these imaging techniques.

Transient elastography and diffusion-weighted magnetic resonance imaging for assessment of liver fibrosis in children with chronic hepatitis C.

Chronic hepatitis C (CHC) is a health burden with consequent morbidity and mortality. Liver biopsy is the gold standard for evaluating fibrosis and assessing disease severity and prognostic purposes post-treatment. Noninvasive alternatives for liver biopsy such as transient elastography (TE) and diffusion-weighted magnetic resonance imaging (DW-MRI) are critical needs. To evaluate TE and DW-MRI as noninvasive tools for predicting liver fibrosis in children with CHC. This prospective cross-sectional study initially recruited 100 children with CHC virus infection. Sixty-four children completed the full set of investigations including liver stiffness measurement (LSM) using TE and measurement of apparent diffusion coefficient (ADC) of the liver and spleen using DW-MRI. Liver biopsies were evaluated for fibrosis using Ishak scoring system. LSM and liver and spleen ADC were compared in different fibrosis stages and correlation analysis was performed with histopathological findings and other laboratory parameters. Most patients had moderate fibrosis (73.5%) while 26.5% had mild fibrosis. None had severe fibrosis or cirrhosis. The majority (68.8%) had mild activity, while only 7.8% had moderate activity. Ishak scores had a significant direct correlation with LSM (P = 0.008) and were negatively correlated with both liver and spleen ADC but with no statistical significance (P = 0.086 and P = 0.145, respectively). Similarly, histopathological activity correlated significantly with LSM (P = 0.002) but not with liver or spleen ADC (P = 0.84 and 0.98 respectively). LSM and liver ADC were able to significantly discriminate F3 from lower fibrosis stages (area under the curve = 0.700 and 0.747, respectively) with a better performance of liver ADC. TE and liver ADC were helpful in predicting significant fibrosis in children with chronic hepatitis C virus infection with a better performance of liver ADC.

Comparison of US elastography and chemical shift magnetic resonance imaging in multifidus muscle fatty degeneration.

The purpose of this study was to investigate the feasibility of the use of shear wave elastography (SWE) in comparison to chemical shift encoding (CSE) magnetic resonance imaging (MRI) for the evaluation of multifidus muscle fatty degeneration in patients with chronic low back pain. Multifidus muscles were evaluated with the CSE-MRI and SWE examinations in control and patient groups. With the in-phase and out-phase sequences in CSE-MRI, signal intensity index (SII), and signal intensity suppression ratio (SISR) values; with the SWE method, shear wave velocity values were determined. Differences in the mean values of these parameters per level and study group were analyzed by Student's t-test. SWE revealed significantly lower stiffness at the L2-3 level, consistent with the signal index values (SII-SISR) showing increased fatty infiltration on MRI in the patient group. No such relationship was found at the L4-5 level or in control group. SWE may be a promising method to show muscle fatty infiltration at L2-3 level in patients with chronic low back pain.

Predicting clinically significant prostate cancer in elderly patients: A nomogram approach with shear wave elastography.

This study was to construct a nomogram utilizing shear wave elastography and assess its efficacy in detecting clinically significant prostate cancer (csPCa). 290 elderly people with suspected PCa who received prostate biopsy and shear wave elastography (SWE) imaging were respectively registered from April 2022 to December 2023. The elderly participants were stratified into two groups: those with csPCa and those without csPCa, which encompassed cases of clinically insignificant prostate cancer (cisPCa) and non-prostate cancer tissue, as determined by pathology findings. The LASSO algorithm, known as the least absolute shrinkage and selection operator, was utilized to identify features. Logistic regression analysis was utilized to establish models. Receiver operating characteristic (ROC) and calibration curves were utilized to evaluate the discriminatory ability of the nomogram. Bootstrap (1000 bootstrap iterations) was employed for internal validation and comparison with two models. A decision curve and a clinical impact curve were employed to assess the clinical usefulness. Our nomogram, which contained Emean, ΔEmean, prostate volume, prostate-specific antigen density (PSAD), and transrectal ultrasound (TRUS), showed better discrimination (AUC = 0.89; 95% CI: 0.83-0.94), compared to the clinical model without SWE parameters (p = 0.0007). Its accuracy, sensitivity and specificity were 0.83, 0.89 and 0.78, respectively. Based on the analysis of decision curve, the thresholds ranged from 5% to 90%. According to our nomogram, biopsying patients at a 20% probability threshold resulted in a 25% reduction in biopsies without missing any csPCa. The clinical impact curve demonstrated that the nomogram's predicted outcome is closer to the observed outcome when the probability threshold reaches 20% or greater. Our nomogram demonstrates efficacy in identifying elderly individuals with clinically significant prostate cancer, thereby facilitating informed clinical decision-making based on diagnostic outcomes and potential clinical benefits.

Lumbar multifidus layers stiffness at L5-S1 level in prone and sitting posture measured by shear wave elastography.

Multifidus is an important lumbar muscle with distinct superficial and deep fibers responsible for torque production and stabilization, respectively. Its mechanical properties change when transitioning from lying to sitting positions, necessitating enhanced stability. It holds crucial clinical relevance to assess these layers separately, especially in the sitting posture, which demands increased neuromuscular control compared to the prone position. To compare lumbar multifidus stiffness in lying versus sitting postures, analyzing both superficial and deep layers. Supersonic Shear Imaging captured elastographic images from 26 asymptomatic volunteers in prone and seated positions. Left multifidus shear modulus in lying: 5.98 ± 1.80/7.96 ± 1.59 kPa (deep/superficial) and sitting: 12.58 ± 4.22/16.04 ± 6.65 kPa. Right side lying: 6.08 ± 1.97/7.80 ± 1.76 kPa and sitting: 13.25 ± 4.61/17.95 ± 7.12 kPa. No side differences (lying p= 0.99, sitting p= 0.43). However, significant inter-postural differences occurred. Lumbar multifidus exhibits increased stiffness in sitting, both layers affected, with superior stiffness in superficial versus deep fibers. Applying these findings could enhance assessing multifidus stiffness changes, for classifying tension-induced low back pain stages.

Intraoperative Comparison Between Strain Elastography and Preoperative Magnetic Resonance Imaging Features in High-Grade Gliomas Using Fusion Imaging: A Pilot Study

To compare the elastographic patterns of high-grade gliomas (HGGs) solid portions and those of adjacent healthy brain parenchyma, on intraoperative ultrasound, with magnetic resonance image (MRI) characteristics. Clinical records and images of HGGs patients, operated between June and December 2018, were retrospectively reviewed. Fusion images were used to compare preoperative gadolinium-enhanced T1-weighted MRI/fluid-attenuated inversion recovery images (Gd-T1 MRI/FLAIR) to intraoperative strain elastography (SE). FLAIR/Gd-T1 MRI images were used to define: enhancement patterns (absent/whole lesion/peripheral) and lesions' characteristics (primary and secondary pattern, further subdivided in solid/necrotic/cystic/infiltrating). HGGs SE patterns were categorized as homogeneous/inhomogeneous, while lesions' primary and secondary patterns as stiff/intermediate/elastic. The SE motive of neighboring healthy brain parenchyma was defined similarly. 18 patients (M:F, 11:7; mean age: 53 years) harboring 14 glioblastomas (77.8%, GBMs) and 4 anaplastic astrocytomas (22.2%, AAs) were compared. GBMs typically enhanced peripherally and had a primary necrotic pattern (78.6% and 64.3%, respectively), while AAs did not enhance and were solid (75% both) at T1-Gd MRI and FLAIR images. At SE AAs had a homogeneous stiff primary pattern, whereas the majority of GBMs primary patterns were heterogeneous (85.7%) and intermediate (78.6%). Three major SE patterns defined HGGs and adjacent healthy brain parenchyma. SE patterns varied accordingly to HGG histotypes and Gd-T1 MRI/FLAIR characteristics.

AI-Powered Synthesis of Structured Multimodal Breast Ultrasound Reports Integrating Radiologist Annotations and Deep Learning Analysis.

Breast cancer is the most prevalent cancer among women worldwide. B-mode ultrasound (US) is essential for early detection, offering high sensitivity and specificity without radiation exposure. This study introduces a semi-automatic method to streamline breast US report generation, aiming to reduce the burden on radiologists. Our method synthesizes comprehensive breast US reports by combining the extracted information from radiologists' annotations during routine screenings with the analysis results from deep learning algorithms on multimodal US images. Key modules in our method include image classification using visual features (ICVF), type classification via deep learning (TCDL), and automatic report structuring and compilation (ARSC). Experiments showed that the proposed method reduced the average report generation time to 3.8 min compared to manual processes, even when using relatively low-spec hardware. Generated reports perfectly matched ground truth reports for suspicious masses without a single failure on our evaluation datasets. Additionally, the deep-learning-based algorithm, utilizing DenseNet-121 as its core model, achieved an overall accuracy of 0.865, precision of 0.868, recall of 0.847, F1-score of 0.856, and area under the receiver operating characteristics of 0.92 in classifying tissue stiffness in breast US shear-wave elastography (SWE-mode) images. These improvements not only streamline the report generation process but also allow radiologists to dedicate more time and focus on patient care, ultimately enhancing clinical outcomes and patient satisfaction.

Recommendations for strain elastography of the uterine cervix

PurposeConventional vaginal strain ultrasound elastography, not based on shear-wave elastography imaging, can assess the biomechanical properties of the uterine cervix. This assessment may inform the risks of preterm birth and failed induction of labor. However, there is considerable variation in the approaches to strain elastography, including the placement of the region of interest (ROI). Therefore, our aim was to provide recommendations for cervical elastography.MethodsWe conducted a literature review on (1) elastography principles, and (2) the cervical anatomy. Subsequently, we performed elastography scanning using a Voluson™ E10 Expert scanner with the BT18 software of (3) polyacrylamide hydrogel simulators, and (4) pregnant women.ResultsIncreasing the distance between the ROI and probe led to a decrease in the obtained strain value; a 53% decrease was observed at 17.5 mm. Similarly, an increased angle between the ROI and probe-centerline resulted in a 59% decrease for 40° angle. Interposition of soft tissue (e.g., cervical canal) between the ROI and the probe induced an artifact with values from the posterior lip being 54% lower than those from the anterior lip, even after adjusting for probe-ROI distance. Equipment and the recording conductance significantly influenced the results.ConclusionOur findings inform recommendations for future studies on strain cervical elastography.

Preoperative Ultrasound Radomics to Predict Posthepatectomy Liver Failure in Patients With Hepatocellular Carcinoma.

Posthepatectomy liver failure (PHLF) is a major cause of postoperative mortality in hepatocellular carcinoma (HCC) patients. The study aimed to develop a method based on the two-dimensional shear wave elastography and clinical data to evaluate the risk of PHLF in HCC patients with chronic hepatitis B. This multicenter study proposed a deep learning model (PHLF-Net) incorporating dual-modal ultrasound features and clinical indicators to predict the PHLF risk. The datasets were divided into a training cohort, an internal validation cohort, an internal independent testing cohort, and three external independent testing cohorts. Based on ResNet50 pretrained on ImageNet, PHLF-Net used a progressive training strategy with images of varying granularity and incorporated conventional B-mode and elastography images and clinical indicators related to liver reserve function. In total, 532 HCC patients who underwent hepatectomy at five hospitals were enrolled. PHLF occurred in 147 patients (27.6%, 147/532). The PHLF-Net combining dual-modal ultrasound and clinical indicators demonstrated high effectiveness for predicting PHLF, with AUCs of 0.957 and 0.923 in the internal validation and testing sets, and AUCs of 0.950, 0.860, and 1.000 in the other three independent external testing sets. The performance of PHLF-Net outperformed models of single- and dual-modal US. Preoperative ultrasound imaging combining clinical indicators can effectively predict the PHLF probability in patients with HCC. In the internal and external validation sets, PHLF-Net demonstrated its usefulness in predicting PHLF.

Optimizing evaluation of endometrial receptivity in recurrent pregnancy loss: a preliminary investigation integrating radiomics from multimodal ultrasound via machine learning.

Recurrent pregnancy loss (RPL) frequently links to a prolonged endometrial receptivity (ER) window, leading to the implantation of non-viable embryos. Existing ER assessment methods face challenges in reliability and invasiveness. Radiomics in medical imaging offers a non-invasive solution for ER analysis, but complex, non-linear radiomic-ER relationships in RPL require advanced analysis. Machine learning (ML) provides precision for interpreting these datasets, although research in integrating radiomics with ML for ER evaluation in RPL is limited. To develop and validate an ML model that employs radiomic features derived from multimodal transvaginal ultrasound images, focusing on improving ER evaluation in RPL. This retrospective, controlled study analyzed data from 346 unexplained RPL patients and 369 controls. The participants were divided into training and testing cohorts for model development and accuracy validation, respectively. Radiomic features derived from grayscale (GS) and shear wave elastography (SWE) images, obtained during the window of implantation, underwent a comprehensive five-step selection process. Five ML classifiers, each trained on either radiomic, clinical, or combined datasets, were trained for RPL risk stratification. The model demonstrating the highest performance in identifying RPL patients was selected for further validation using the testing cohort. The interpretability of this optimal model was augmented by applying Shapley additive explanations (SHAP) analysis. Analysis of the training cohort (242 RPL, 258 controls) identified nine key radiomic features associated with RPL risk. The extreme gradient boosting (XGBoost) model, combining radiomic and clinical data, demonstrated superior discriminatory ability. This was evidenced by its area under the curve (AUC) score of 0.871, outperforming other ML classifiers. Validation in the testing cohort of 215 subjects (104 RPL, 111 controls) confirmed its accuracy (AUC: 0.844) and consistency. SHAP analysis identified four endometrial SWE features and two GS features, along with clinical variables like age, SAPI, and VI, as key determinants in RPL risk stratification. Integrating ML with radiomics from multimodal endometrial ultrasound during the WOI effectively identifies RPL patients. The XGBoost model, merging radiomic and clinical data, offers a non-invasive, accurate method for RPL management, significantly enhancing diagnosis and treatment.

Transperineal Three-Dimensional Ultrasound Combined with Real-Time Shear Wave Elastography Imaging to Assess Changes in Pelvic Floor Structure During Pregnancy and Delivery

Transperineal Three-Dimensional Ultrasound Combined with Real-Time Shear Wave Elastography Imaging to Assess Changes in Pelvic Floor Structure During Pregnancy and Delivery

Assessment of the utility of two-dimensional shear wave elastography and superb microvascular imaging in postoperative patients with biliary atresia.

We aimed to investigate whether prediction of liver fibrosis using two-dimensional shear wave elastography (2D-SWE) and vascular tree grading using superb microvascular imaging (SMI) are useful for postoperative follow-up in patients with biliary atresia (BA). We retrospectively collected data from medical records of 134 patients who underwent ultrasound examination with 2D-SWE or SMI, including 13 postoperative patients with BA and 121 non-BA patients. We investigated the distribution of liver stiffness values with SWE and vascular tree grading with SMI and evaluated correlations between these findings and biochemical indices of liver fibrosis in postoperative BA patients. The SWE values of the BA group were not significantly different from that of any other disease groups in non-BA patients. In postoperative BA patients, SWE values correlated significantly with aspartate aminotransferase to platelet ratio index (Spearman rank correlation coefficient [rs] = 0.6380, p = 0.0256) and with the Fib-4 index (rs = 0.6526, p = 0.0214). SMI vascular tree grading of the BA group was significantly higher than that of the choledochal cyst group (p = 0.0008) and other hepatobiliary disorder group (p = 0.0030). In postoperative BA patients, SMI vascular tree grading was not positively correlated with any biochemical marker of fibrosis. 2D-SWE appears to be useful for follow-up in postoperative BA patients.

Prevalence, clinical significance and prognosis value of liver stiffness measurement anomalies in transthyretin cardiac amyloidosis

Background – Laboratory liver anomalies are common in cardiac amyloidosis; however, their significance regarding liver stiffness is unknown. The aim of this study was to investigate the prevalence, clinical significance, and prognostic value of liver stiffness measurement (LSM) anomalies in transthyretin cardiac amyloidosis (ATTR-CA). Methods – Consecutive patients diagnosed with ATTR-CA who underwent liver stiffness assessment were included in the study. Demographic, clinical, laboratory, transthoracic echocardiography and liver stiffness data were retrospectively collected. LSM was obtained through either transient elastography or supersonic shear imaging. Patient cohort was divided in two groups according to a 10 kPa threshold. Follow up data were collected for the occurrence of hospitalization for heart failure and all-cause death. Results – Two hundred and eighty-four patients with ATTR-CA – 26 (9 %) hereditary variant ATTR, 258 (91 %) wild-type ATTR – were included. A LSM over 10 kPa was found in 4 (15 %) and 98 (38 %) patients with ATTRv and ATTRwt respectively (p = 0.02). Among patients with ATTRwt, high LSM was more frequent in advanced stages of ATTR-CA and was associated with increased risk of hospitalization for heart failure after multivariate analysis with a hazard ratio of 2.41 [1.05–5.55] (p = 0.04). Among patients with NYHA stage 1, 28 % presented high LSM associated with high NT-proBNP levels. Integration of high LSM with NT-proBNP and estimated glomerular filtration rate provided a better estimate of patient survival. Conclusion – LSM over 10 kPa is found in up to 36 % of patients with ATTR-CA and is associated with advanced stages of cardiomyopathy and increased risk of hospitalization for heart failure in ATTRwt patients.

Shear-wave elastographic imaging in choroidal melanomas: clinical and hemodynamic correlations.

This study evaluated the role of shear wave elastography imaging (SWEΙ) in uveal melanomas and the associations between SWEI and clinical and hemodynamic findings. Prospective, clinical study METHODS: Twelve patients with uveal melanomas, scheduled to undergo Ru-106 brachytherapy, were prospectively recruited from the Department of Ophthalmology of the University Hospital of Heraklion (September-December 2022). B-mode, hemodynamic and SWEI ultrasonography examinations were performed with the HiScan (OPTIKON 2000) and the LOGIQ E9 (GE Healthcare) sonographic systems, respectively. Differences in SWEI scores (kPa) between tumor (TS) and adjacent non-affected choroid (CS), as well as between TS and orbital fat (FS) were examined. Correlations between SWEI and intra-tumoral hemodynamic parameters, including peak systolic and end diastolic velocities and resistivity index (RI) were also examined. TS was significantly correlated with intra-tumoral RI (Pearson's bivariate correlation coefficient 0.681, p=0.015) and with maximal tumor height (Pearson's bivariate correlation coefficient 0.620, p=0.031). TS was significantly higher than both FS and CS scores (paired-samples t-test, p=0.003 and p=0.006, respectively). SWEI score is applicable as a quantitative biomechanical marker in the assessment of choroidal melanoma. Choroidal melanomas are stiffer than both adjacent choroid and orbital fat. Moreover, choroidal melanomas with higher RI as well as those with higher apical elevations display higher SWEI scores.

AI-Driven Elastography Image Analysis for Accurate Breast Cancer Diagnosis

AI-Driven Elastography Image Analysis for Accurate Breast Cancer Diagnosis

Assessment of the effects of direct myofascial release in the lateral gastrocnemius muscle of tennis athletes using ShearWave™ elastography

ObjectivesThe purpose of the present study was to utilize ShearWave™ Elastography to assess tissue stiffness by measuring shear wave speed before and after applying the direct Myofascial release (MFR) technique to the lateral gastrocnemius muscle of competitive tennis athletes. MethodsShear wave speed values were measured in the regions of interest within the lateral gastrocnemius fascia and muscle areas from the elastographic images. Measurements were taken in three different situations: before, immediately after and 5 min after MFR protocol. Subsequently, the results from each situation were statistically compared. ResultsThe protocol was administered to a group of eighteen tennis players with a mean age of 18 ± 1.23 years. The shear wave speed values increased for both fascia and muscles sites, after applying the MFR technique: From 3.01 ± 0.64 to 3.33 ± 0.86 m/s in the deep fascia and from 2.23 ± 0.41 to 2.57 ± 0.51 m/s in superficial fascia. In the deep and superficial muscles sites the increases were from 4.12 ± 0.95 to 4.40 ± 1.22 m/s, and from 2.94 ± 0.71 to 3.38 ± 0.95 m/s, respectively. ConclusionThis study showed that after the myofascial manipulation in tennis athletes, the lateral gastrocnemius muscle and fascia tissues stiffness increased, according to elastography, and possible mechanisms were discussed. ShearWave™ Elastography could be utilized to assess the acute effects of applying a direct myofascial release (MFR) technique.

Cystic fibrosis hepatobiliary involvement: an update on imaging in diagnosis and monitoring.

Analysis of the liver using imaging for persons with cystic fibrosis (CF) continues to evolve as new medical therapies are developed improving and extending life. In the 2010s, therapies targeted at modulating protein folding became available to those with CF. Therapeutic options have continued to expand, now providing both correction of protein folding and stabilization for most gene mutations that code for the CF transmembrane receptor protein (CFTR). Today, approximately 80% of persons with CF are eligible for highly effective modulator therapy. With these advancements, the impact of CF on the liver has become more complex, adding metabolism of CFTR modulators to intrinsic CF hepatobiliary involvement (CFHBI) and adding not previously appreciated vascular changes within the liver due to increased longevity in persons with CF. A combination of serum biomarkers and imaging is needed to add clarity to the diagnosis and monitoring of the severity of liver disease. A substantial portion of persons with CF will develop at least CFHBI and a subset will develop advanced cystic fibrosis-associated liver disease (aCFLD); therefore, diagnosis and monitoring need to begin in childhood. In this review, we cover the use of and need for imaging, including elastography, ultrasound, and magnetic resonance imaging (MRI), in diagnosing and monitoring CFHBI and its associated complications.