Differential diagnosis of focal nodular hyperplasia with quantitative parametric analysis in contrast-enhanced sonography

Hierarchical Temporal Attention Network for Thyroid Nodule Recognition Using Dynamic CEUS Imaging

Contrast-enhanced ultrasound (CEUS) has emerged as a popular imaging modality in thyroid nodule diagnosis due to its ability to visualize vascular distribution in real time. Recently, a number of learning-based methods are dedicated to mine pathological-related enhancement dynamics and make prediction at one step, ignoring a native diagnostic dependency. In clinics, the differentiation of benign or malignant nodules always precedes the recognition of pathological types. In this paper, we propose a novel hierarchical temporal attention network (HiTAN) for thyroid nodule diagnosis using dynamic CEUS imaging, which unifies dynamic enhancement feature learning and hierarchical nodules classification into a deep framework. Specifically, this method decomposes the diagnosis of nodules into an ordered two-stage classification task, where diagnostic dependency is modeled by Gated Recurrent Units (GRUs). Besides, we design a local-to-global temporal aggregation (LGTA) operator to perform a comprehensive temporal fusion along the hierarchical prediction path. Particularly, local temporal information is defined as typical enhancement patterns identified with the guidance of perfusion representation learned from the differentiation level. Then, we leverage an attention mechanism to embed global enhancement dynamics into each identified salient pattern. In this study, we evaluate the proposed HiTAN method on the collected CEUS dataset of thyroid nodules. Extensive experimental results validate the efficacy of dynamic patterns learning, fusion and hierarchical diagnosis mechanism.

Differentiation of atypical hepatic hemangioma from liver metastases: Diagnostic performance of a novel type of color contrast enhanced ultrasound

BACKGROUND

In clinical practice, the diagnosis is sometimes difficult with contrast-enhanced ultrasound (CEUS) when the case has an atypical perfusion pattern. Color parametric imaging (CPI) is an analysis software for CEUS with better detection of temporal differences in CEUS imaging using arbitrary colors. It measures the differences in arrival time of the contrast agent in lesions so that the perfusion features of atypical hemangioma and colorectal cancer (CRC) liver metastasis can be distinguished.

AIM

To evaluate the role of a novel type of CPI of CEUS in the differential diagnosis of atypical hemangioma from liver metastases in patients with a history of CRC.

METHODS

From January 2016 to July 2018, 42 patients including 20 cases of atypical hemangioma and 22 cases of liver metastases from CRC were enrolled. These patients had a mean age of 60.5 ± 9.3 years (range: 39-75 years). All patients received ultrasound, CEUS and CPI examinations. Resident and staff radiologists independently and retrospectively reviewed CEUS and CPI images. Two sets of criteria were assigned: (1) Routine CEUS alone; and (2) CEUS and CPI. The diagnostic sensitivity, specificity, accuracy and receiver operating characteristic (ROC) curve of resident and staff radiologists were analyzed.

RESULTS

The following CPI features were significantly different between liver hemangioma and liver metastases analyzed by staff and resident radiologists: Peripheral nodular enhancement (65%-70.0% vs 4.5%-13.6%, P < 0.001, P = 0.001), mosaic/chaotic enhancement (5%-10% vs 68.2%-63.6%, P < 0.001, P < 0.001) and feeding artery (20% vs 59.1%-54.5%, P = 0.010, P = 0.021). CPI imaging offered significant improvements in detection rates compared with routine CEUS in both resident and staff groups. By resident radiologists, the specificity and accuracy of CEUS+CPI were significantly increased compared with that of CEUS (77.3% vs 45.5%, P = 0.030; 78.6% vs 50.0%, P = 0.006). In addition, the area under the curve (AUC) of CEUS+CPI was significantly higher than that of CEUS (0.803 vs 0.757, P = 0.036). By staff radiologists, accuracy was improved in CEUS+CPI (81.0% vs 54.8%, P = 0.010), whereas no significant differences in specificity and sensitivity were found (P = 0.144, P = 0.112). The AUC of CEUS+CPI was significantly higher than that of CEUS (0.890 vs 0.825, P = 0.013) by staff radiologists.

CONCLUSION

Compared with routine CEUS, CPI could provide specific information on the hemodynamic features of liver lesions and help to differentiate atypical hemangioma from liver metastases in patients with CRC, even for senior radiologists.

Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling to Machine Learning

Ultrasound contrast agents (UCAs) have opened up immense diagnostic possibilities by combined use of indicator dilution principles and dynamic contrast-enhanced ultrasound (DCE-US) imaging. UCAs are microbubbles encapsulated in a biocompatible shell. With a rheology comparable to that of red blood cells, UCAs provide an intravascular indicator for functional imaging of the (micro)vasculature by quantitative DCE-US. Several models of the UCA intravascular kinetics have been proposed to provide functional quantitative maps, aiding diagnosis of different pathological conditions. This article is a comprehensive review of the available methods for quantitative DCE-US imaging based on temporal, spatial and spatiotemporal analysis of the UCA kinetics. The recent introduction of novel UCAs that are targeted to specific vascular receptors has advanced DCE-US to a molecular imaging modality. In parallel, new kinetic models of increased complexity have been developed. The extraction of multiple quantitative maps, reflecting complementary variables of the underlying physiological processes, requires an integrative approach to their interpretation. A probabilistic framework based on emerging machine-learning methods represents nowadays the ultimate approach, improving the diagnostic accuracy of DCE-US imaging by optimal combination of the extracted complementary information. The current value and future perspective of all these advances are critically discussed.

Evaluation of Indirect Methods for Motion Compensation in 2-D Focal Liver Lesion Contrast-Enhanced Ultrasound (CEUS) Imaging

This study investigates the application and evaluation of existing indirect methods, namely point-based registration techniques, for the estimation and compensation of observed motion included in the 2-D image plane of contrast-enhanced ultrasound (CEUS) cine-loops recorded for the characterization and diagnosis of focal liver lesions (FLLs). The value of applying motion compensation in the challenging modality of CEUS is to assist in the quantification of the perfusion dynamics of an FLL in relation to its parenchyma, allowing for a potentially accurate diagnostic suggestion. Towards this end, this study also proposes a novel quantitative multi-level framework for evaluating the quantification of FLLs, which to the best of our knowledge remains undefined, notwithstanding many relevant studies. Following quantitative evaluation of 19 indirect algorithms and configurations, while also considering the requirement for computational efficiency, our results suggest that the "compact and real-time descriptor" (CARD) is the optimal indirect motion compensation method in CEUS.

Computer-Aided Diagnosis of Focal Liver Lesions Using Contrast-Enhanced Ultrasonography With Perflubutane Microbubbles

This paper proposes an automatic classification method based on machine learning in contrast-enhanced ultrasonography (CEUS) of focal liver lesions using the contrast agent Sonazoid. This method yields spatial and temporal features in the arterial phase, portal phase, and post-vascular phase, as well as max-hold images. The lesions are classified as benign or malignant and again as benign, hepatocellular carcinoma (HCC), or metastatic liver tumor using support vector machines (SVM) with a combination of selected optimal features. Experimental results using 98 subjects indicated that the benign and malignant classification has 94.0% sensitivity, 87.1% specificity, and 91.8% accuracy, and the accuracy of the benign, HCC, and metastatic liver tumor classifications are 84.4%, 87.7%, and 85.7%, respectively. The selected features in the SVM indicate that combining features from the three phases are important for classifying FLLs, especially, for the benign and malignant classifications. The experimental results are consistent with CEUS guidelines for diagnosing FLLs. This research can be considered to be a validation study, that confirms the importance of using features from these phases of the examination in a quantitative manner. In addition, the experimental results indicate that for the benign and malignant classifications, the specificity without the post-vascular phase features is significantly lower than the specificity with the post-vascular phase features. We also conducted an experiment on the operator dependency of setting regions of interest and observed that the intra-operator and inter-operator kappa coefficients were 0.45 and 0.77, respectively.

Towards Dynamic Contrast Specific Ultrasound Tomography

We report on the first study demonstrating the ability of a recently-developed, contrast-enhanced, ultrasound imaging method, referred to as cumulative phase delay imaging (CPDI), to image and quantify ultrasound contrast agent (UCA) kinetics. Unlike standard ultrasound tomography, which exploits changes in speed of sound and attenuation, CPDI is based on a marker specific to UCAs, thus enabling dynamic contrast-specific ultrasound tomography (DCS-UST). For breast imaging, DCS-UST will lead to a more practical, faster, and less operator-dependent imaging procedure compared to standard echo-contrast, while preserving accurate imaging of contrast kinetics. Moreover, a linear relation between CPD values and ultrasound second-harmonic intensity was measured (coefficient of determination = 0.87). DCS-UST can find clinical applications as a diagnostic method for breast cancer localization, adding important features to multi-parametric ultrasound tomography of the breast.

Recognizing Focal Liver Lesions in CEUS With Dynamically Trained Latent Structured Models

This work investigates how to automatically classify Focal Liver Lesions (FLLs) into three specific benign or malignant types in Contrast-Enhanced Ultrasound (CEUS) videos, and aims at providing a computational framework to assist clinicians in FLL diagnosis. The main challenge for this task is that FLLs in CEUS videos often show diverse enhancement patterns at different temporal phases. To handle these diverse patterns, we propose a novel structured model, which detects a number of discriminative Regions of Interest (ROIs) for the FLL and recognize the FLL based on these ROIs. Our model incorporates an ensemble of local classifiers in the attempt to identify different enhancement patterns of ROIs, and in particular, we make the model reconfigurable by introducing switch variables to adaptively select appropriate classifiers during inference. We formulate the model learning as a non-convex optimization problem, and present a principled optimization method to solve it in a dynamic manner: the latent structures (e.g. the selections of local classifiers, and the sizes and locations of ROIs) are iteratively determined along with the parameter learning. Given the updated model parameters in each step, the data-driven inference is also proposed to efficiently determine the latent structures by using the sequential pruning and dynamic programming method. In the experiments, we demonstrate superior performances over the state-of-the-art approaches. We also release hundreds of CEUS FLLs videos used to quantitatively evaluate this work, which to the best of our knowledge forms the largest dataset in the literature. Please find more information at "http://vision.sysu.edu.cn/projects/fllrecog/".

Evaluation of the Accuracy of Liver Lesion DCEUS Quantification With Respiratory Gating

Confidence in the accuracy of dynamic contrast enhanced ultrasound (DCEUS) quantification parameters is imperative for the correct diagnosis of liver lesion perfusion characteristics. An important source of uncertainty in liver DCEUS acquisitions is artifacts introduced by respiratory motion. The objective of this study is to construct a respiratory motion simulation model (RMSM) of dual contrast imaging mode acquisitions of liver lesions in order to evaluate an algorithm for automatic respiratory gating (ARG). The respiratory kinetics as well as the perfusion models of the liver lesion and parenchyma used by the RMSM were solely derived from clinical data. The quality of fit (of the DCEUS data onto the bolus kinetics model) depends on the respiration amplitude. Similar trends in terms of quality of fit as a function of respiration amplitude were observed from RMSM and clinical data. The errors introduced on the DCEUS quantification under the influence of respiration were evaluated. The RMSM revealed that the error in the liver lesion DCEUS quantification parameters significantly decreased (p < 0.001) from a maximum of 32.3% to 6.2% when ARG was used. The use of RMSM clearly demonstrates the capability of the ARG algorithm in significantly reducing errors introduced from both in-plane and out-of-plane respiratory motion.

2-tier in-plane motion correction and out-of-plane motion filtering for contrast-enhanced ultrasound

OBJECTIVES

Contrast-enhanced ultrasound (CEUS) cines of focal liver lesions (FLLs) can be quantitatively analyzed to measure tumor perfusion on a pixel-by-pixel basis for diagnostic indication. However, CEUS cines acquired freehand and during free breathing cause nonuniform in-plane and out-of-plane motion from frame to frame. These motions create fluctuations in the time-intensity curves (TICs), reducing the accuracy of quantitative measurements. Out-of-plane motion cannot be corrected by image registration in 2-dimensional CEUS and degrades the quality of in-plane motion correction (IPMC). A 2-tier IPMC strategy and adaptive out-of-plane motion filter (OPMF) are proposed to provide a stable correction of nonuniform motion to reduce the impact of motion on quantitative analyses.

MATERIALS AND METHODS

A total of 22 cines of FLLs were imaged with dual B-mode and contrast specific imaging to acquire a 3-minute TIC. B-mode images were analyzed for motion, and the motion correction was applied to both B-mode and contrast images. For IPMC, the main reference frame was automatically selected for each cine, and subreference frames were selected in each respiratory cycle and sequentially registered toward the main reference frame. All other frames were sequentially registered toward the local subreference frame. Four OPMFs were developed and tested: subsample normalized correlation (NC), subsample sum of absolute differences, mean frame NC, and histogram. The frames that were most dissimilar to the OPMF reference frame using 1 of the 4 above criteria in each respiratory cycle were adaptively removed by thresholding against the low-pass filter of the similarity curve. Out-of-plane motion filter was quantitatively evaluated by an out-of-plane motion metric (OPMM) that measured normalized variance in the high-pass filtered TIC within the tumor region-of-interest with low OPMM being the goal. Results for IPMC and OPMF were qualitatively evaluated by 2 blinded observers who ranked the motion in the cines before and after various combinations of motion correction steps.

RESULTS

Quantitative measurements showed that 2-tier IPMC and OPMF improved imaging stability. With IPMC, the NC B-mode metric increased from 0.504 ± 0.149 to 0.585 ± 0.145 over all cines (P < 0.001). Two-tier IPMC also produced better fits on the contrast-specific TIC than industry standard IPMC techniques did (P < 0.02). In-plane motion correction and OPMF were shown to improve goodness of fit for pixel-by-pixel analysis (P < 0.001). Out-of-plane motion filter reduced variance in the contrast-specific signal as shown by a median decrease of 49.8% in the OPMM. Two-tier IPMC and OPMF were also shown to qualitatively reduce motion. Observers consistently ranked cines with IPMC higher than the same cine before IPMC (P < 0.001) as well as ranked cines with OPMF higher than when they were uncorrected.

CONCLUSION

The 2-tier sequential IPMC and adaptive OPMF significantly reduced motion in 3-minute CEUS cines of FLLs, thereby overcoming the challenges of drift and irregular breathing motion in long cines. The 2-tier IPMC strategy provided stable motion correction tolerant of out-of-plane motion throughout the cine by sequentially registering subreference frames that bypassed the motion cycles, thereby overcoming the lack of a nearly stationary reference point in long cines. Out-of-plane motion filter reduced apparent motion by adaptively removing frames imaged off-plane from the automatically selected OPMF reference frame, thereby tolerating nonuniform breathing motion. Selection of the best OPMF by minimizing OPMM effectively reduced motion under a wide variety of motion patterns applicable to clinical CEUS. These semiautomated processes only required user input for region-of-interest selection and can improve the accuracy of quantitative perfusion measurements.

Dynamic contrast-enhanced ultrasound for quantification of tissue perfusion

Dynamic contrast-enhanced ultrasound (US) imaging, a technique that uses microbubble contrast agents with diagnostic US, has recently been technically summarized and reviewed by a European Federation of Societies for Ultrasound in Medicine and Biology position paper. However, the practical applications of this imaging technique were not included. This article reviews and discusses the published literature on the clinical use of dynamic contrast-enhanced US. This review finds that dynamic contrast-enhanced US imaging is the most sensitive cross-sectional real-time method for measuring the perfusion of parenchymatous organs noninvasively. It can measure parenchymal perfusion and therefore can differentiate between benign and malignant tumors. The most important routine clinical role of dynamic contrast-enhanced US is the prediction of tumor responses to chemotherapy within a very short time, shorter than using Response Evaluation Criteria in Solid Tumors criteria. Other applications found include quantifying the hepatic transit time, diabetic kidneys, transplant grafts, and Crohn disease. In addition, the problems involved in using dynamic contrast-enhanced US are discussed.

Quantitative analysis of contrast-enhanced ultrasonography of the bowel wall can predict disease activity in inflammatory bowel disease

PURPOSE

To evaluate the accuracy of quantitative analysis of bowel wall enhancement in inflammatory bowel disease (IBD) with contrast enhanced ultrasound (CEUS) by comparing the results with vascular density in a biopsy sample from the same area of the intestinal tract, and to determine the usefulness of this analysis for the prediction of disease activity.

MATERIALS AND METHODS

This prospective study was approved by our institute's ethics committee and all patients gave written informed consent. We enrolled 33 consecutive adult patients undergoing colonoscopy and biopsy for IBD. All patients underwent CEUS and the results were quantitatively analyzed. Vessel count per high-power field on biopsy specimens was compared with colonoscopy, baseline ultrasonography, and CEUS findings, and with analysis of peak intensity, time to peak, regional blood volume, mean transit time, and regional blood flow. Results in patients with high and low vascular density were compared using Fisher's test, t-test, Pearson's correlation test, and receiver operating characteristic curve (ROC) analysis. Cutoff values were determined using ROC analysis, and sensitivity and specificity were calculated.

RESULTS

High vascular density (>265 vessels per field) on histological examination was significantly correlated with active disease on colonoscopy, baseline ultrasonography, and CEUS (p<.0001). Quantitative analysis showed a higher enhancement peak, a shorter time to peak enhancement, a higher regional blood flow and regional blood volume in patients with high vascular density than in those with low vascular density. Cutoff values to distinguish between active and inactive disease were identified for peak enhancement (>40.5%), and regional blood flow (>54.8 ml/min).

CONCLUSION

Quantitative analysis of CEUS data correlates with disease activity as determined by vascular density. Quantitative parameters of CEUS can be used to predict active disease with high sensitivity and specificity.

Automatic respiratory gating for contrast ultrasound evaluation of liver lesions

Dynamic contrast-enhanced ultrasound (DCEUS) has been used in radiology for many years for lesion detection and characterization. In recent years, more emphasis has been placed on tumor perfusion quantification with DCEUS. To ensure accuracy in both quantitative and qualitative evaluation of liver tumors with DCEUS, sources of noise in clinical data must be identified and, if possible, removed. One of the major sources of such noise is respiratory motion. A new automatic respiratory gating (ARG) algorithm is presented and evaluated with clinical data. The results of the evaluation demonstrate the potential of the ARG algorithm for clinical use as a fast and easy-to-implement method for removing respiratory motion from DCEUS loops.

Regions of interest and parameters for the quantitative analysis of contrast-enhanced ultrasound to evaluate the anti-angiogenic effects of bevacizumab

The aim of the present study was to identify effective regions of interest (ROIs) and parameters for the quantitative analysis of contrast-enhanced ultrasound (CEUS) to evaluate the anti-angiogenic effects of bevacizumab. Thirty mice were subcutaneously injected with CT26 cells and randomly divided into a bevacizumab‑treated (Bev) group and a control group (normal saline-treated). CEUS and quantitative analysis were performed on days 7, 11, 14 and 21 following tumor establishment. ROItotal, which included the whole tumor, and ROIsmall, which included the most enhanced part of the tumor, were selected and outlined. Parameters including time to peak (TTP), maximum intensity (Imax) and area under the curve (AUC; in addition to rates of AUC1, AUC2, AUCfast and AUCslow) were recorded. The tumors were resected on day 21 for microvessel density (MVD) counting. Our results showed that the MVD in the Bev group was significantly lower compared with that in the control group (4.09 vs. 6.41; P=0.001). Additional parameters of ROIsmall were identified to be significantly different between the two groups, compared with those of ROItotal. No significant differences in TTP and Imax were observed between the two groups at the four time‑points examined (P>0.05). For the AUC parameters in ROIsmall, AUC and the rates of AUC2, AUCfast and AUCslow were lower in the Bev group compared with those in the control group on days 7 and 11 (P<0.05). These findings indicate that ROIsmall and AUC parameters in the quantitative analysis of CEUS may be useful for the evaluation of changes in tumor angiogenesis following bevacizumab treatment.

Quantitative analysis of contrast-enhanced ultrasonography: differentiating focal nodular hyperplasia from hepatocellular carcinoma

OBJECTIVE

To explore the potential of quantitative analysis of contrast-enhanced ultrasonography (CEUS) in differentiating focal nodular hyperplasia (FNH) from hepatocellular carcinoma (HCC).

METHODS

34 cases of FNH and 66 cases of HCC (all lesions <5 cm) were studied using CEUS to evaluate enhancement patterns and using analytic software Sonoliver® (Image-Arena™ v.4.0, TomTec Imaging Systems, Munich, Germany) to obtain quantitative features of CEUS in the region of interest. The quantitative features of maximum of intensity (IMAX), rise slope (RS), rise time (RT) and time to peak (TTP) were compared between the two groups and applied to further characterise both FNH and HCC with hypoenhancing patterns in the late phase on CEUS.

RESULTS

The sensitivity and specificity of CEUS for diagnosis of FNH were 67.6% and 93.9%, respectively. For quantitative analysis, IMAX and RS in FNHs were significantly higher than those in HCCs (p<0.05), while RT and TTP in FNHs were significantly shorter (p<0.05). Both the 11 FNHs and 62 HCCs with hypo-enhancing patterns in the late phase were further characterised with their quantitative features, and the sensitivity and specificity of IMAX for diagnosis of FNH were 90.9% and 43.5%, RS 81.8% and 80.6%, RT 90.9% and 71.0%, and TTP 90.9% and 71.0%, respectively.

CONCLUSION

The quantitative features of CEUS in FNH and HCC were significantly different, and they could further differentiate FNH from HCC following conventional CEUS.

ADVANCES IN KNOWLEDGE

Our findings suggest that quantitative analysis of CEUS can improve the accuracy of differentiating FNH from HCC.

A case of focal nodular hyperplasia with a new characteristic finding on contrast-enhanced ultrasonography using Levovist

We used contrast-enhanced ultrasound with Levovist, a microbubble contrast agent, to diagnose a case of hepatic focal nodular hyperplasia (FNH). A new characteristic finding of heartbeat-synchronized centrifugal enhancement was discovered. We call this enhancement pattern the "sonographic fireworks sign." It is expected to be useful for diagnosing FNH, especially when the lesions are small and it is difficult to depict a spoke-wheel pattern.

Contrast-enhanced ultrasonography parameters in neural network diagnosis of liver tumors

AIM: To study the role of time-intensity curve (TIC) analysis parameters in a complex system of neural networks designed to classify liver tumors.

METHODS: We prospectively included 112 patients with hepatocellular carcinoma (HCC) (n = 41), hypervascular (n = 20) and hypovascular (n = 12) liver metastases, hepatic hemangiomas (n = 16) or focal fatty changes (n = 23) who underwent contrast-enhanced ultrasonography in the Research Center of Gastroenterology and Hepatology, Craiova, Romania. We recorded full length movies of all contrast uptake phases and post-processed them offline by selecting two areas of interest (one for the tumor and one for the healthy surrounding parenchyma) and consecutive TIC analysis. The difference in maximum intensities, the time to reaching them and the aspect of the late/portal phase, as quantified by the neural network and a ratio between median intensities of the central and peripheral areas were analyzed by a feed forward back propagation multi-layer neural network which was trained to classify data into five distinct classes, corresponding to each type of liver lesion.

RESULTS: The neural network had 94.45% training accuracy (95% CI: 89.31%-97.21%) and 87.12% testing accuracy (95% CI: 86.83%-93.17%). The automatic classification process registered 93.2% sensitivity, 89.7% specificity, 94.42% positive predictive value and 87.57% negative predictive value. The artificial neural networks (ANN) incorrectly classified as hemangyomas three HCC cases and two hypervascular metastases, while in turn misclassifying four liver hemangyomas as HCC (one case) and hypervascular metastases (three cases). Comparatively, human interpretation of TICs showed 94.1% sensitivity, 90.7% specificity, 95.11% positive predictive value and 88.89% negative predictive value. The accuracy and specificity of the ANN diagnosis system was similar to that of human interpretation of the TICs (P = 0.225 and P = 0.451, respectively). Hepatocellular carcinoma cases showed contrast uptake during the arterial phase followed by wash-out in the portal and first seconds of the late phases. For the hypovascular metastases did not show significant contrast uptake during the arterial phase, which resulted in negative differences between the maximum intensities. We registered wash-out in the late phase for most of the hypervascular metastases. Liver hemangiomas had contrast uptake in the arterial phase without agent wash-out in the portal-late phases. The focal fatty changes did not show any differences from surrounding liver parenchyma, resulting in similar TIC patterns and extracted parameters.

CONCLUSION: Neural network analysis of contrast-enhanced ultrasonography - obtained TICs seems a promising field of development for future techniques, providing fast and reliable diagnostic aid for the clinician.

Contrast-enhanced ultrasonography of hepatocellular carcinoma: correlation between quantitative parameters and histological grading

OBJECTIVE

The quantitative parameters in the contrast-enhanced ultrasonography time-intensity curve of hepatocellular carcinoma (HCC) were studied to explore their possible implication for histological grading of HCC.

METHODS

A total of 130 HCC patients (115 males and 15 females; age: 48.13±11.00 years) were studied using contrast-enhanced ultrasonography time-intensity curve and histological pathology. The quantification software Sonoliver® (TomTec Imaging Systems, Unterschleissheim, Germany) was applied to derive time-intensity curves of regions of interest in the interior of HCCs and in reference. Quantitative parameters of 115 patients were successfully obtained, including maximum of intensity (IMAX), rise time (RT), time to peak (TTP), rise slope (RS) and washout time (WT). Histological grading of HCC was performed using haematoxylin-eosin staining, and monoclonal antibodies specific for smooth muscle actin were used to observe unpaired arteries (UAs).

RESULTS

There were significant differences among WTs in the three differentiated HCC groups (p<0.05). However, there were no significant differences among RT, TTP, RS and IMAX in the differentiated HCC groups. Moreover, the number of UAs in the differentiated HCC groups showed no statistical significance.

CONCLUSION

WT plays an important role in predicting well, moderately and poorly differentiated HCC.

Contrast-enhanced ultrasonography of hepatocellular carcinoma: correlation between quantitative parameters and arteries in neoangiogenesis or sinusoidal capillarization

OBJECTIVE

The quantitative parameters in contrast-enhanced ultrasonography-time-intensity curve of hepatocellular carcinoma (HCC) were studied to explore their potential importance in monitoring the effects of anti-angiogenic therapy for HCC.

METHODS

115 HCC patients were studied with contrast-enhanced ultrasonography-time-intensity curve (CEUS-TIC) and with immunohistochemical analysis of tissue sections. The CEUS images were analyzed off-line to obtained quantitative parameters including maximum of intensity (IMAX), rise time (RT), time to peak (TTP), mean transit time (mTT), rise slope (RS), and washout time (WT). Monoclonal antibodies specific for smooth muscle actin and anti-CD34 were used to observe unpaired arteries (UAs) and microvessel area (MVA) of sinusoidal capillarization, respectively. The UAs and MVA of 82 HCC cases were successfully stained.

RESULTS

The number of UAs had moderate correlation with RT (r=-0.446), TTP (r=-0.432), and RS (r=0.431) (P<0.05), and it had mild correlation with IMAX (r=0.303) and WT (r=0.285) (P<0.05). MVA of sinusoidal capillarization had no correlation with perfusion parameters.

CONCLUSION

Quantitative CEUS-TIC parameters reflecting hemodynamics of tumors are correlated with UAs, but not with MVA, and they might be used to monitor the effects of anti-angiogenic therapy on HCC.

Quantification of enhancement of focal liver lesions during contrast-enhanced ultrasound (CEUS). Analysis of ten selected frames is more simple but as reliable as the analysis of the entire loop for most parameters

The aim of the study was to evaluate the reliability of the analysis of only 10 frames rather than of a whole clip in performing quantitative assessment of tumor enhancement of focal liver lesions (FLLs) following ultrasound contrast injection. Contrast-enhanced ultrasonography (CEUS) examinations of 31 FLLs (median diameter: 30mm) were performed. All clips were analyzed and quantified with an early prototype of the SonoLiver software (TomTec GmbH, Munich and Bracco Research SA, Geneva), first evaluating the entire clip then selecting only 10 frames at different time intervals. Enhancement measurements obtained from the analysis of the entire clip or of only 10 frames were closely correlated (r=0.931 and p<0.0001 for Area Under the Curve; r=0.944 and p<0.0001 for Perfusion Index). In conclusion, enhancement quantification of FLLs can be reliably obtained from only 10 frames, rather than the entire clip, at least for most parameters, making such procedure easier for potential routine use.

Computer-aided diagnosis for contrast-enhanced ultrasound in the liver

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. The basic concept of CAD is to provide computer output as a second opinion to assist radiologists’ image interpretations by improving the accuracy and consistency of radiologic diagnosis and also by reducing the image-reading time. To date, research on CAD in ultrasound (US)-based diagnosis has been carried out mostly for breast lesions and has been limited in the fields of gastroenterology and hepatology, with most studies being conducted using B-mode US images. Two CAD schemes with contrast-enhanced US (CEUS) that are used in classifying focal liver lesions (FLLs) as liver metastasis, hemangioma, or three histologically differentiated types of hepatocellular carcinoma (HCC) are introduced in this article: one is based on physicians’ subjective pattern classifications (subjective analysis) and the other is a computerized scheme for classification of FLLs (quantitative analysis). Classification accuracies for FLLs for each CAD scheme were 84.8% and 88.5% for metastasis, 93.3% and 93.8% for hemangioma, and 98.6% and 86.9% for all HCCs, respectively. In addition, the classification accuracies for histologic differentiation of HCCs were 65.2% and 79.2% for well-differentiated HCCs, 41.7% and 50.0% for moderately differentiated HCCs, and 80.0% and 77.8% for poorly differentiated HCCs, respectively. There are a number of issues concerning the clinical application of CAD for CEUS, however, it is likely that CAD for CEUS of the liver will make great progress in the future.

Parametric imaging using contrast-enhanced ultrasound with Sonazoid for hepatocellular carcinoma

PURPOSE

To clarify the usefulness of parametric imaging using contrast-enhanced ultrasound (CE-US) with Sonazoid by comparing parametric images of hepatocellular carcinoma (HCC) with histopathological findings.

METHODS

Two patients with HCCs underwent CE-US with Sonazoid before surgical resection. A single focus point was set at the lower margin of the tumor, and a bolus intravenous injection of Sonazoid (0.5 ml) was administered. Images of the ideal scanning plane were displayed in real-time mode for the early vascular phase. We analyzed these images using prototype PC software. The software watches, pixel by pixel, the increase in the intensity due to the inflow of the microbubbles, and displays colors if the intensity becomes larger than a certain threshold. Parametric images were compared with histopathological findings.

RESULTS

The level of blood flow in the tumor could be visually evaluated using a single image by expressing the detailed hemodynamics of the tumor in terms of differences in color using a time axis appropriate for each case.

CONCLUSIONS

Parametric imaging is a very useful way of facilitating straightforward visualization of the level of blood flow within HCC and the distribution of histopathological findings in single static images.

[Contrast-enhanced ultrasound and liver imaging: review of the literature]

The advent of second-generation microbubble ultrasound contrast agents and the development of contrast specific ultrasound techniques improved the ability of contrast enhanced ultrasound (CEUS) in detecting and characterizing focal liver lesions, opening new prospects in liver imaging. A Medline search in June 2008 identified 72 published studies that used CEUS in focal liver lesion detection, characterization, and follow-up to monitor tumor ablation procedures and antiangiogenic treatment. The purpose of this paper, based on literature review, is to describe the technical recommendations when using CEUS for liver imaging and to define the different vascular patterns of the most relevant benign and malignant lesions. Diagnostic performance of CEUS and the important clinical indications are also presented and discussed. CEUS is increasingly accepted in clinical use for diagnostic imaging and post-interventional workup liver imaging. It may replace many computed tomography and magnetic resonance imaging examinations in the near future, according to the European Federation of Societies for Ultrasound in Medicine and Biology guidelines.

Computer-aided diagnosis of focal liver lesions by use of physicians' subjective classification of echogenic patterns in baseline and contrast-enhanced ultrasonography

RATIONAL AND OBJECTIVES

To develop a computer-aided diagnostic (CAD) scheme for classifying focal liver lesions (FLLs) by use of physicians' subjective classification of echogenic patterns of FLLs on baseline and contrast-enhanced ultrasonography (US).

MATERIALS AND METHODS

A total of 137 hepatic lesions in 137 patients were evaluated with B-mode and NC100100 (Sonazoid)-enhanced pulse-inversion US; lesions included 74 hepatocellular carcinomas (HCCs) (23: well-differentiated, 36: moderately differentiated, 15: poorly differentiated HCCs), 33 liver metastases, and 30 liver hemangiomas. Three physicians evaluated single images at B-mode and arterial phases with a cine mode. Physicians were asked to classify each lesion into one of eight B-mode and one of eight enhancement patterns, but did not make a diagnosis. To classify five types of FLLs, we employed a decision tree model with four decision nodes and four artificial neural networks (ANNs). The results of the physicians' pattern classifications were used successively for four different ANNs in making decisions at each of the decision nodes in the decision tree model.

RESULTS

The classification accuracies for the 137 FLLs were 84.8% for metastasis, 93.3% for hemangioma, and 98.6% for all HCCs. In addition, the classification accuracies for histological differentiation types of HCCs were 65.2% for well-differentiated HCC, 41.7% for moderately differentiated HCC, and 80.0% for poorly differentiated HCC.

CONCLUSIONS

This CAD scheme has the potential to improve the diagnostic accuracy of liver lesions. However, the accuracy in the histologic differential diagnosis of HCC based on baseline and contrast-enhanced US is still limited.

Quantitative contrast enhanced ultrasound of the liver for time intensity curves-Reliability and potential sources of errors

INTRODUCTION

Time intensity curves for real-time contrast enhanced low MI ultrasound is a promising technique since it adds objective data to the more subjective conventional contrast enhanced technique. Current developments showed that the amount of uptake in modern targeted therapy strategies correlates with therapy response. Nevertheless no basic research has been done concerning the reliability and validity of the method.

PATIENTS AND METHODS

Videos sequences of 31 consecutive patients for at least 60s were recorded. Parameters analysed: area under the curve, maximum intensity, mean transit time, perfusion index, time to peak, rise time. The influence of depth, lateral shift as well as size and shape of the region of interest was analysed.

RESULTS

The parameters time to peak and rise time showed a good stability in different depths. Overall there was a variation >50% for all other parameters. Mean transit time, time to peak and rise time were stable from 3 to 10cm depths, whereas all other parameters showed only satisfying results at 4-6cm. Time to peak and rise time were stable as well against lateral shifting whereas all other parameters had again variations over 50%. Size and shape of the region of interest did not influence the results.

DISCUSSION

(1) It is important to compare regions of interest, e.g. in a tumour vs. representative parenchyma in the same depths. (2) Time intensity curves should not be analysed in a depth of less than 4cm. (3) The parameters area under the curve, perfusion index and maximum intensity should not be analysed in a depth more than 6cm. (4) Size and shape of a region of interest in liver parenchyma do not affect time intensity curves.

Behavior of hepatocellular adenoma on real-time low-mechanical index contrast-enhanced ultrasonography with a second-generation contrast agent

OBJECTIVE

The purpose of this study was to describe the behavior of histologically proven hepatocellular adenoma (HCA) on low-mechanical index (MI) contrast-enhanced ultrasonography (CEUS).

METHODS

A review of the databases from 4 academic hospitals revealed 18 patients (15 female and 3 male; mean age, 40 years; range, 25-71 years) with 25 histologically proven HCA lesions who were studied with CEUS at a low MI (0.04-0.1).

RESULTS

Twenty-four of 25 lesions (96%; 95% confidence interval [CI], 80.5%-99.3%) showed high-intensity enhancement, scored as 3 on a scale of 0 to 3, whereas only 1 lesion (4%; 95% CI, 0.7%-19.5%) was scored as 2. The time of peak enhancement ranged between 10 and 19 seconds (average, 13 seconds). All but 1 of the 25 lesions (96%; 95% CI, 80.5%-99.3%) showed early homogeneous and centripetal enhancement during the hepatic arterial phase. No portal venous phase enhancement was observed in any lesion because all showed rapid wash-out (100%; 95% CI, 86.7%-100%). Twenty lesions (80%; 95% CI, 60.9%-91.1%) were found to be isoechoic to slightly hypoechoic during the portal phase, and 19 (76%; 95% CI, 56.6%-88.5%) were isoechoic to mildly hypoechoic, whereas 7 (24%; 95% CI, 11.5%-43.4%) were hypoechoic during the late phase.

CONCLUSIONS

Contrast-enhanced ultrasonography is an effective technique for identifying the microvascular and macrovascular characteristics of HCA. Typically, HCA shows early (10-19 seconds) and centripetal enhancement during the arterial phase and isoechogenicity or mild hypoechogenicity during the portal phase, remaining slightly hypoechoic or isoechoic during the late phase in most cases.

Characterization of malignant focal liver lesions with contrast-enhanced 40 MHz ultrasound imaging in hepatitis B virus X transgenic mice: a feasibility study

Contrast-enhanced ultrasound (CEUS) imaging has been a reliable clinical method of detecting three vascular contrast phases and characterizing focal liver lesions. Previous results were all from human (i.e., clinical studies). The main purpose of this study was to extend this to small animals and to investigate the feasibility of using CEUS in preclinical research. Specifically, high-frequency (40 MHz) ultrasound liver imaging with albumin-shelled microbubbles was employed to detect the three vascular contrast phases and characterize focal liver lesions that developed in thirteen Hepatitis B virus X (HBx) transgenic mice at around 14 to 16 months of age. Previous studies indicated that 90-100% incidence of hepatocellular carcinoma (HCC) was observed in HBx transgenic male mice. After injecting the contrast agent, the time-intensity curves (TICs) of focal liver lesions, vessels in focal liver lesions and surrounding liver parenchyma tissues were measured for 30 minutes. The peak of mean intensity relative to the baseline increased 7.36 dB (p < 0.02). On the other hand, the mean contrast between the focal liver lesion and the liver parenchyma increased by 7.74 (p < 0.05) dB, thus allowing clear detection ofthe lesion margin. Histopathology investigations confirmed the development of the lesion in these mice. In addition, guidelines of European Federation of Societies for Ultrasound in Medicine and Biology were followed as an attempt to characterize features of the TICs in mice. The arterial phase was defined as 2 to 60 seconds post contrast injection, and the parenchyma phase was defined as the time period from 10 to 30 minutes post contrast injection. Comparing the imaging with the pathology results, the sensitivity, specificity and accuracy of CEUS for the detection of malignant focal liver lesion in HBx transgenic mice were 91%, 100% and 92%. These results demonstrated that high-frequency CEUS imaging potentially can be used for detecting the three vascular contrast phases of malignant focal liver lesions and characterizing malignant focal liver lesions in mice. Thus can be a valuable tool in preclinical research.

Computer-aided diagnosis for the classification of focal liver lesions by use of contrast-enhanced ultrasonography

The authors developed a computer-aided diagnostic (CAD) scheme for classifying focal liver lesions (FLLs) as liver metastasis, hemangioma, and three histologic differentiation types of hepatocellular carcinoma (HCC), by use of microflow imaging (MFI) of contrast-enhanced ultrasonography. One hundred and three FLLs obtained from 97 cases used in this study consisted of 26 metastases (15 hyper- and 11 hypovascularity types), 16 hemangiomas (five hyper- and 11 hypovascularity types) and 61 HCCs: 24 well differentiated (w-HCC), 28 moderately differentiated (m-HCC), and nine poorly differentiated (p-HCC). Pathologies of all cases were determined based on biopsy or surgical specimens. Locations and contours of FLLs on contrast-enhanced images were determined manually by an experienced physician. MFI was obtained with contrast-enhanced low-mechanical-index (MI) pulse subtraction imaging at a fixed plane which included a distinctive cross section of the FLL. In MFI, the inflow high signals in the plane, which were due to the vascular patterns and the contrast agent, were accumulated following flash scanning with a high-MI ultrasound exposure. In the initial step of our computerized scheme, a series of the MFI images was extracted from the original cine clip (AVI format). We applied a smoothing filter and time-sequential running average techniques in order to reduce signal noise on the single MFI image and cyclic noise on the sequential MFI images, respectively. A kidney, vessels, and a liver parenchyma region were segmented automatically by use of the last image of a series of MFI images. The authors estimated time-intensity curves for an FLL by use of a series of the temporally averaged MFI images in order to determine temporal features such as estimated replenishment times at early and delayed phases, flow rates, and peak times. In addition, they extracted morphologic and gray-level image features which were determined based on the physicians' knowledge of the diagnosis of the FLL, such as the size of lesion, vascular patterns, and the presence of hypoechoic regions. They employed a cascade of six independent artificial neural networks (ANNs) by use of extracted temporal and image features for classifying five types of liver diseases. A total of 16 temporal and image features, which were selected from 43 initially extracted features, were used for six different ANNs for making decisions at each decision in the cascade. The ANNs were trained and tested with a leave-one-lesion-out test method. The classification accuracies for the 103 FLLs were 88.5% for metastasis, 93.8% for hemangioma, and 86.9% for all HCCs. In addition, the classification accuracies for histologic differentiation types of HCCs were 79.2% for w-HCC, 50.0% for m-HCC, and 77.8% for p-HCC. The CAD scheme for classifying FLLs by use of the MFI on contrast-enhanced ultrasonography has the potential to improve the diagnostic accuracy in the histologic diagnosis of HCCs and the other liver diseases.

Correlation between parametric imaging using contrast ultrasound and the histological differentiation of hepatocellular carcinoma

AIM

To determine whether parametric imaging correlates with the degree of histological differentiation of hepatocellular carcinoma (HCC).

METHODS

The samples comprised 49 nodules diagnosed histologically as HCC: 19 well differentiated (w-HCC), 22 moderately differentiated (m-HCC), and eight poorly differentiated (p-HCC). The ultrasound (US) equipment used was SSA-770 A (Toshiba Medical Systems, Otawara, Japan) and the contrast agent was SonoVue (Bracco, Milan, Italy). After 1.5 mL of SonoVue was injected intravenously and staining of the tumors and parenchyma was confirmed, microbubbles in the scanned volume were eliminated using high mechanical index (MI) scanning frames. The "arrival time (T(A)) images," reflecting beta-values, were displayed with color codes at the phase after reperfusion. Images at the phase when the staining reached a plateau (90-180 s) were used as "A images," reflecting A values. These images were compared between each histological grade of differentiation.

RESULTS

Analysis of T(A) images indicated that beta-values in m-HCC were higher than those in the adjacent non-tumor parenchyma in all 22 samples and also were significantly higher than in the other HCCs (P < 0.001 for w-HCC; P < 0.05 for p-HCC). Furthermore, beta-values in p-HCC samples had significantly larger variations in terms of time and space than in the other HCCs (P < 0.001 for w-HCC; P < 0.01 for m-HCC). Analysis of A images indicated that the A value for w-HCC was significantly higher than those for either m-HCC or p-HCC (P < 0.001).

CONCLUSION

Both T(A) and A images were useful for diagnosing the histological differentiation of HCC.

Focal liver lesions: can SonoVue-enhanced ultrasound be used to differentiate malignant from benign lesions?

OBJECTIVE

To evaluate whether contrast-enhanced ultrasound (CEUS) with SonoVue could differentiate malignant focal liver lesions (FLLs) from benign lesions and provide lesion type diagnoses.

MATERIALS AND METHODS

Four hundred fifty-six patients with 554 FLLs were examined by CEUS with SonoVue using low mechanical index, nonlinear imaging techniques. Each lesion was characterized by 2 independent off-site readers as malignant or benign and given specific lesion type diagnosis, if possible, both at baseline ultrasound (US) and after SonoVue administration (CEUS). The final diagnosis was achieved by histopathology obtained from biopsy or surgical specimens, or by typical manifestation on contrast-enhanced CT or MRI.

RESULTS

The diagnostic accuracies of the 2 readers were 41.9% and 35.2% for baseline US, which improved significantly to 87.2% and 87.9% for CEUS (P < 0.05). Interreader agreement also increased with CEUS compared with baseline US (ê value changed from 0.49 to 0.77). The accuracy for lesion type diagnosis was 38.4% and 32.5% for baseline US, which increased to 77.6% and 78.0% for CEUS (P < 0.05).

CONCLUSIONS

CEUS with SonoVue improves differentiation between malignant and benign FLLs, and also provides improved lesion type (differential) diagnosis.