Contrast-enhanced ultrasound identifies early extrahepatic collateral contributing to residual hepatocellular tumor viability after transarterial chemoembolization.

The mainstay of treatment for unresectable hepatocellular carcinoma is locoregional therapy including percutaneous ablation and transarterial chemo- and radioembolization. While monitoring for tumor response after transarterial chemoembolization is crucial, current imaging strategies are suboptimal. The standard of care is contrast-enhanced magnetic resonance imaging or computed tomography imaging performed at least 4 to 6 weeks after therapy. We present a case in which contrast-enhanced ultrasound identified a specific extra-hepatic collateral from the gastroduodenal artery supplying residual viable tumor and assisting with directed transarterial management

Parametric mapping of contrasted ovarian transvaginal sonography.

The purpose of this study was to assess the accuracy of parametric analysis of transvaginal contrast-enhanced ultrasound (TV-CEUS) for distinguishing benign versus malignant ovarian masses. A total of 48 ovarian masses (37 benign and 11 borderline/malignant) were examined with TV-CEUS (Definity; Lantheus, North Billerica, MA; Philips iU22; Philips Medical Systems, Bothell, WA). Parametric images were created offline with a quantification software (Bracco Suisse SA, Geneva, Switzerland) with map color scales adjusted such that abnormal hemodynamics were represented by the color red and the presence of any red color could be used to differentiate benign and malignant tumors. Using these map color scales, low values of the perfusion parameter were coded in blue, and intermediate values of the perfusion parameter were coded in yellow. Additionally, for each individual color (red, blue, or yellow), a darker shade of that color indicated a higher intensity value. Our study found that the parametric mapping method was considerably more sensitive than standard region of interest (ROI) analysis for the detection of malignant tumors but was also less specific than standard ROI analysis. Parametric mapping allows for stricter cutoff criteria, as hemodynamics are visualized on a finer scale than ROI analyses, and as such, parametric maps are a useful addition to TV-CEUS analysis by allowing ROIs to be limited to areas of the highest malignant potential

Evaluation of Hepatocellular Carcinoma Transarterial Chemoembolization using Quantitative Analysis of 2D and 3D Real-time Contrast Enhanced Ultrasound.

Quantitative 2D and 3D contrast-enhanced ultrasound (CEUS) was assessed to evaluate early transarterial chemoembolization (TACE) treatment response. Seventeen patients scheduled for TACE for the treatment of hepatocellular carcinoma participated in the study. 2D and 3D CEUS were performed for each patient at three time points: Prior to TACE, 1-2 weeks post TACE, and 1 month post TACE. Peak-intensities of the tumor and surrounding liver tissue were calculated from 2D and 3D data before and after TACE and used to evaluate tumor treatment response. Residual tumor percentages were calculated from 2D and 3D CEUS acquired 1-2 weeks and 1 month post TACE and compared with results from MRI 1 month post TACE. Nine subjects had complete response while 8 had incomplete response. Peak-intensities of the tumor from 3D CEUS prior to TACE were similar between the complete and incomplete treatment groups (p = 0.70), while 1-2 weeks (p \u3c 0.01) and 1 month post treatment (p \u3c 0.01) were significantly lower in the complete treatment group than in the incomplete treatment group. For 2D CEUS, only the peak-intensity values of the tumor from 1 month post TACE were significantly different (p \u3c 0.01). The correlation coefficients between 2D and 3D residual tumor estimates 1-2 weeks post TACE and the estimates from MRI were 0.73 and 0.94, respectively, while those from 2D and 3D CEUS 1 month post TACE were 0.66 and 0.91, respectively. Quantitative analysis on 2D and 3D CEUS shows potential to differentiate patients with complete versus incomplete response to TACE as early as 1-2 weeks post treatment

Diagnostic Accuracy of CEUS LI-RADS for the Characterization of Liver Nodules 20 mm or Smaller in Patients at Risk for Hepatocellular Carcinoma.

Background: American College of Radiology contrast agent–enhanced US Liver Imaging Reporting and Data System (CEUS LI-RADS) was developed to improve the accuracy of hepatocellular carcinoma (HCC) diagnosis at contrast agent2enhanced US. However, to the knowledge of the authors, the diagnostic accuracy of the system in characterization of liver nodules 20 mm or smaller has not been fully evaluated. Purpose: To evaluate the diagnostic accuracy of CEUS LI-RADS in diagnosing HCC in liver nodules 20 mm or smaller in patients at risk for HCC. Materials and Methods: Between January 2015 and February 2018, consecutive patients at risk for HCC presenting with untreated liver nodules 20 mm or less were enrolled in this retrospective double-reader study. Each nodule was categorized according to the CEUS LI-RADS and World Federation for Ultrasound in Medicine and Biology (WFUMB)–European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) criteria. Diagnostic performance of CEUS LI-RADS and WFUMB-EFSUMB characterization was evaluated by using tissue histologic analysis, multiphase contrast-enhanced CT and MRI, and imaging follow-up as reference standard and compared by using McNemar test. Results: The study included 175 nodules (mean diameter, 16.1 mm 6 3.4) in 172 patients (mean age, 51.8 years 6 10.6; 136 men). The sensitivity of CEUS LR-5 versus WFUMB-EFSUMB criteria in diagnosing HCC was 73.3% (95% confidence inter-val [CI]: 63.8%, 81.5%) versus 88.6% (95% CI: 80.9%, 94%), respectively (P, .001). The specificity of CEUS LR-5 versus WFUMB-EFSUMB criteria was 97.1% (95% CI: 90.1%, 99.7%) versus 87.1% (95% CI: 77%, 94%), respectively (P = .02). No malignant lesions were found in CEUS LR-1 and LR-2 categories. Only two nodules (of 41; 5%, both HCC) were malignant in CEUS LR-3 category. The incidences of HCC in CEUS LR-4, LR-5, and LR-M were 48% (11 of 23), 98% (77 of 79), and 75% (15 of 20), respectively. Two of 175 (1.1%) histologic analysis2confirmed intrahepatic cholangiocarcinomas were categorized as CEUS LR-M by CEUS LI-RADS and misdiagnosed as HCC by WFUMB-EFSUMB criteria. Conclusion: The contrast-enhanced US Liver Imaging Reporting and Data System (CEUS LI-RADS) algorithm was an effective tool for characterization of small (≤20 mm) liver nodules in patients at risk for hepatocellular carcinoma (HCC). Compared with World Federation for Ultrasound in Medicine and Biology2European Federation of Societies for Ultrasound in Medicine and Biology criteria, CEUS LR-5 demonstrated higher specificity for diagnosing small HCCs with lower sensitivity

Early Detection of Ovarian Cancer with Conventional and Contrast-Enhanced Transvaginal Sonography: Recent Advances and Potential Improvements

Recently, there have been several major technical advances in the sonographic diagnosis of ovarian cancer in its early stages. These include improved assessment of tumor morphology with transvaginal sonography (TVS), and detection and characterization of tumor neovascularity with transvaginal color Doppler sonography (TV-CDS) and contrast-enhanced transvaginal sonography (CE-TVS). This paper will discuss and illustrate these improvements and describe how they enhance detection of early-stage ovarian cancer. Our initial experience with parametric mapping of CE-TVS will also be mentioned

Subharmonic and Endoscopic Contrast Imaging of Pancreatic Masses: A Pilot Study.

OBJECTIVES: To use subharmonic imaging (SHI) to depict the vascularity of pancreatic masses compared to contrast-enhanced endoscopic ultrasound (EUS) and pathologic results. METHODS: Sixteen patients scheduled for biopsy of a pancreatic mass were enrolled in an Institutional Review Board-approved study. Pulse-inversion SHI (transmitting/receiving at 2.5/1.25 MHz) was performed on a LOGIQ 9 system (GE Healthcare, Milwaukee, WI) with a 4C transducer, whereas contrast harmonic EUS (transmitting/receiving at 4.7/9.4 MHz) was performed with a radial endoscope (GF-UTC180; Olympus Corporation, Tokyo, Japan) connected to a ProSound SSD α-10 scanner (Hitachi Aloka, Tokyo, Japan). Two injections of the contrast agent Definity (Lantheus Medical Imaging, North Billerica, MA) were administrated (0.3-0.4 and 0.6-0.8 mL for EUS and SHI, respectively). Contrast-to-tissue ratios (CTRs) in the mass and an adjacent vessel were calculated. Four physicians independently scored the images (benign to malignant) for diagnostic accuracy and inter-reader agreement. RESULTS: One patient dropped out before imaging, leaving 11 adenocarcinomas, 1 gastrointestinal stromal tumor with pancreatic infiltration, and 3 benign masses. Marked subharmonic signals were obtained in all patients, with intratumoral blood flow clearly visualized with SHI. Significantly greater CTRs were obtained in the masses with SHI than with EUS (mean ± SD, 1.71 ± 1.63 versus 0.63 ± 0.89; P = .016). There were no differences in the CTR in the surrounding vessels or when grouped by pathologic results (P \u3e .60). The accuracies for contrast EUS and SHI were low (\u3c53%), albeit with a greater κ value for SHI (0.34) than for EUS (0.13). CONCLUSIONS: Diagnostic accuracy of contrast EUS and transabdominal SHI for assessment of pancreatic masses was quite low in this pilot study. However, SHI had improved tumoral CTRs relative to contrast EUS

Contrast-Enhanced Ultrasound LI-RADS: A Pictorial Review

The American College of Radiology has implemented the Liver Imaging Reporting and Data System (LI-RADS) to help detect, interpret, and guide the management of suspected lesions on surveillance imaging for hepatocellular carcinoma (HCC) in patients with cirrhosis. The classification of indeterminate nodules with a grading algorithm can be used for multiple imaging modalities (US, CT, and MRI) and incorporates multiple imaging features to appropriately classify observations with different likelihood of being HCC. Contrast-enhanced ultrasound (CEUS) LI-RADS has been fully implemented since 2017. The aim of this pictorial article is to provide a comprehensive review of CEUS LI-RADS utilization, discuss its advantages, and highlight areas for potential improvement

Bilinear modelling and estimation of displacement for thyroid cancer elasticity imaging

In this study we present a tissue motion modelling and estimation method for elasticity imaging with ultrasound applied to thyroid cancer. Elasticity image of tumor and surrounding tissue of thyroid gland is acquired under a freehand tissue compression using a standard ultrasound probe. The complexity of the movements to analyze requires the development of a parametric model of the displacement and a specific estimation method adapted to sub-pixel displacement. The motion model is a bilinear model with 8 parameters. The model parameters are estimated using a multi-scale iterative approach. This approach was tested on simulation images and clinical data. The first clinical results show the interest and the potential of such imaging technique for the visualization of thyroid tumors.Nous proposons dans cette étude une modélisation et une estimation du mouvement pour l’imagerie échographique de l’élasticité des tissus mous appliquée au diagnostic du cancer de la thyroïde. L’image de l’élasticité de la tumeur et des tissus environnants s’obtient par compression progressive de la thyroïde du patient à l’aide de la sonde échographique. La complexité des mouvements à analyser nécessite le développement d’un modèle paramétrique du déplacement et d’une méthode d’estimation des paramètres du modèle adaptée aux déplacements sub-pixels. Le modèle de mouvement est un modèle bilinéaire à 8 paramètres. L’estimation des paramètres du modèle s’effectue par une approche itérative multi-échelle. Cette approche est testée sur des images de simulation puis, sur des données cliniques. Les premiers résultats cliniques indiquent l’intérêt et le potentiel de cette technique d’imagerie pour la visualisation des tumeurs de la thyroïde

Advances in Modern Clinical Ultrasound

Advances in modern clinical ultrasound include developments in ultrasound signal processing, imaging techniques and clinical applications. Improvements in ultrasound processing include contrast and high-fidelity ultrasound imaging to expand B-mode imaging and microvascular (or microluminal) discrimination. Similarly, volumetric sonography, automated or intelligent ultrasound, and fusion imaging developed from the innate limitations of planar ultrasound, including user-operator technical dependencies and complex anatomic spatial prerequisites. Additionally, ultrasound techniques and instrumentation have evolved towards expanding access amongst clinicians and patients. To that end, portability of ultrasound systems has become paramount. This has afforded growth into the point-of-care ultrasound and remote or tele-ultrasound arenas. In parallel, advanced applications of ultrasound imaging have arisen. These include high frequency superficial sonograms to diagnose dermatologic pathologies as well as various intra-cavitary or lesional interrogations by contrast-enhanced ultrasound. Properties such as real­time definition and ease-of-access have spumed procedural and interventional applications for vascular access. This narrative review provides an overview of these advances and potential future directions of ultrasound

Ultrasonographic risk stratification of indeterminate thyroid nodules; a comparison of an artificial intelligence algorithm with radiologist performance

Background, Motivation and Objective: Thyroid nodules with indeterminate or suspicious cytology are commonly encountered in clinical practice and their clinical management is controversial. Recently, genetical analysis of thyroid fine needle aspiration (FNAs) was implemented at some institutions to differentiate thyroid nodules as high and low risk based on the presence of certain oncogenes commonly associated with aggressive tumor behavior and poor patient outcomes. Our group recently detailed the performance of a machine-learning model based on ultrasonography images of thyroid nodules for the prediction of high and low risk mutations. This study evaluated the performance of a second-generation machine-learning algorithm incorporating both object detection analysis and image classification and subsequently compared performance against blinded radiologists. Statement of Contribution/Methods: This retrospective study was conducted at Thomas Jefferson University and included an evaluation of 262 thyroid nodules that underwent ultrasound imaging, ultrasound-guided FNA and next-generation sequencing (NGS) or surgical pathology after resection. An object detection and image classification model were employed to first identify the location of nodules and then to assess the malignancy. A Google cloud platform (AutoML Vision; Google LLC) was used for this purpose. Either NGS or surgical pathology was considered as reference standard upon availability. 211 nodules were used for model development and the unused 51 nodules for model testing. Diagnostic performance in 47 nodules for which pathology or NGS were available was compared to blinded reads by 3 radiologists and performance expressed as mean ± standard deviation %. Results/Discussion: The algorithm achieved positive predictive value (PPV) of 68.31% and sensitivity of 86.81% within the training model. The model was tested on images of 51 unused nodules and all 51 nodules were correctly located (100%). For risk stratification, the model demonstrated a sensitivity of 73.9%, specificity of 70.8%, positive predictive value (PPV) of 70.8%, negative predictive value (NPV) of 73.9% and overall accuracy of 66.7% in the 47 nodules. For comparison, the 3 radiologist performance in this same dataset demonstrated a sensitivity of, specificity of, PPV of, NPV of, and overall accuracy of This work demonstrates that a machine-learning algorithm using image classification performed similarly, if not slightly better than 3 experienced radiologists. Future research will focus on incorporating machine learning findings within radiologist interpretation to potentially improve diagnostic accuracy