Ultrasomics differentiation of malignant and benign focal liver lesions based on contrast-enhanced ultrasound.

OBJECTIVES: To establish a nomogram for differentiating malignant and benign focal liver lesions (FLLs) using ultrasomics features derived from contrast-enhanced ultrasound (CEUS). METHODS: 527 patients were retrospectively enrolled. On the training cohort, ultrasomics features were extracted from CEUS and b-mode ultrasound (BUS). Automatic feature selection and model development were performed using the Ultrasomics-Platform software, outputting the corresponding ultrasomics scores. A nomogram based on the ultrasomics scores from artery phase (AP), portal venous phase (PVP) and delayed phase (DP) of CEUS, and clinical factors were established. On the validation cohort, the diagnostic performance of the nomogram was assessed and compared with seniorexpert and resident radiologists. RESULTS: In the training cohort, the AP, PVP and DP scores exhibited better differential performance than BUS score, with area under the curve (AUC) of 84.1-85.1% compared with the BUS (74.6%, P < 0.05). In the validation cohort, the AUC of combined nomogram and expert was significantly higher than that of the resident (91.4% vs. 89.5% vs. 79.3%, P < 0.05). The combined nomogram had a comparable sensitivity with the expert and resident (95.2% vs. 98.4% vs. 97.6%), while the expert had a higher specificity than the nomogram and the resident (80.6% vs. 72.2% vs. 61.1%, P = 0.205). CONCLUSIONS: A CEUS ultrasomics based nomogram had an expert level performance in FLL characterization.

Malignancy diagnosis of liver lesion in contrast enhanced ultrasound using an end-to-end method based on deep learning.

BACKGROUND: Contrast-enhanced ultrasound (CEUS) is considered as an efficient tool for focal liver lesion characterization, given it allows real-time scanning and provides dynamic tissue perfusion information. An accurate diagnosis of liver lesions with CEUS requires a precise interpretation of CEUS images. However,it is a highly experience dependent task which requires amount of training and practice. To help improve the constrains, this study aims to develop an end-to-end method based on deep learning to make malignancy diagnosis of liver lesions using CEUS. METHODS: A total of 420 focal liver lesions with 136 benign cases and 284 malignant cases were included. A deep learning model based on a two-dimensional convolution neural network, a long short-term memory (LSTM), and a linear classifier (with sigmoid) was developed to analyze the CEUS loops from different contrast imaging phases. For comparison, a 3D-CNN based method and a machine-learning (ML)-based time-intensity curve (TIC) method were also implemented for performance evaluation. RESULTS: Results of the 4-fold validation demonstrate that the mean AUC is 0.91, 0.88, and 0.78 for the proposed method, the 3D-CNN based method, and the ML-based TIC method, respectively. CONCLUSIONS: The proposed CNN-LSTM method is promising in making malignancy diagnosis of liver lesions in CEUS without any additional manual features selection.

Diagnostic accuracy of diffusion weighted MRI in differentiating malignant from benign liver lesions taking histopathology as gold standard.

Objective: To determine the diagnostic accuracy of Diffusion Weighted MRI in differentiating malignant from benign liver lesions taking histopathology as gold standard. Methods: This Cross-sectional study was conducted at Departments of Radiology and Medicine, JPMC, Karachi from February 23, 2019 till September 25, 2019. Data was prospectively collected from patients after taking consent. One hundred twenty five patients presenting with hepatic mass who met the inclusion criteria were included. Quantitative data was presented as simple descriptive statistics giving mean and standard deviation and qualitative variables as frequency and percentages. Sensitivity, specificity, positive and negative predictive values and diagnostic accuracy were calculated. P-value of ≤0.05 was considered as significant. Results: Mean age in our study was 59.75±8.57 years. Total 71 (56.8%) were male and 54 (43.2%) were female. Out of 125 patients, sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of DW MRI for the diagnosis of malignant focal liver lesion by taking histopathology as gold standard was found to be 92.3%, 93.6%, 96%, 88% and 92.8% respectively. Conclusion: DW MRI scan has high diagnostic accuracy and being accurate in making a diagnosis and differentiation of benign from malignant focal liver lesion would decrease need of invasive modality of histopathology.

MRI of Peliosis Hepatis: A Case Series Presentation With a 2022 Systematic Literature Update.

BACKGROUND: Peliosis hepatis (PH) is a rare benign condition, characterized by hepatic sinusoidal dilatation and blood-filled cystic cavities, often found incidentally, with still challenging diagnosis by imaging due to polymorphic appearance. PURPOSE: Based on a retrospective analysis of our series (12 patients) and systematic literature review (1990-2022), to organize data about PH and identify features to improve characterization. STUDY TYPE: Retrospective case series and systematic review. POPULATION: Twelve patients (mean age 48 years, 55% female) with pathology-proven PH and 49 patients (mean age 52 years, 67% female) identified in 33 studies from the literature (1990-2022). FIELD STRENGTH/SEQUENCE: 1,5-T; T1-weighted (T1W), T2-weighted (T2W), diffusion-weighted (DW), contrast-enhanced (CE) T1W imaging. ASSESSMENT: We compared our series and literature data in terms of demographic (gender/age/ethnicity), clinical characteristics (symptoms/physical examination/liver test), associated conditions (malignancies/infectious/hematologic/genetic or chronic disorders/drugs or toxic exposure) percentage. On magnetic resonance imaging lesion numbers/shape/mean maximum diameter/location/mass effect/signal intensity were compared. PH pathological type/proposed imaging diagnosis/patient follow-up were also considered. STATISTICAL TESTS: Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Case Reports/Series quality assessment. Intraclass correlation and Cohen's kappa coefficients for levels of inter/intrareader agreement in our experience. RESULTS: Patients were mainly asymptomatic (92% vs. 70% in our study and literature) with associated conditions (83% vs. 80%). Lesions showed homogeneous T1W-hypointensity (58% vs. 65%) and T2W-hyperintensity (58% vs. 66%). Heterogeneous nonspecific (25% vs. 51%), centrifugal (34% vs. 8%), or rim-like centripetal (25% vs. 23%) patterns of enhancement were most frequent, with hypointensity on the hepatobiliary phase (HBP), without restricted diffusivity. Good inter- and intrareader agreement was observed in our experience. Concerning JBI Checklist, 19 out of 31 case reports met at least 7 out of 8 criteria, whereas 2 case series fulfilled 5 and 6 out of 10 items respectively. DATA CONCLUSION: A homogeneous, not well-demarcated T1W-hypointense and T2W-hyperintense mass, with heterogeneous nonspecific or rim-like centripetal or centrifugal pattern of enhancement, and hypointensity on HBP, may be helpful for PH diagnosis. Among associated conditions, malignancies and drug exposures were the most frequent. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Can optimized model-based iterative reconstruction improve the contrast of liver lesions in CT?

BACKGROUND: Computed tomography is a standard imaging procedure for the detection of liver lesions, such as metastases, which can often be small and poorly contrasted, and therefore hard to detect. Advances in image reconstruction have shown promise in reducing image noise and improving low-contrast detectability. PURPOSE: To examine a novel, specialized, model-based iterative reconstruction (MBIR) technique for improved low-contrast liver lesion detection. MATERIAL AND METHODS: Patient images with reported poorly contrasted focal liver lesions were retrospectively reconstructed with the low-contrast attenuating algorithm (FIRST-LCD) from primary raw data. Liver-to-lesion contrast, signal-to-noise, and contrast-to-noise ratios for background and liver noise for each lesion were compared for all three FIRST-LCD presets with the established hybrid iterative reconstruction method (AIDR-3D). An additional visual conspicuity score was given by two experienced radiologists for each lesion. RESULTS: A total of 82 lesions in 57 examinations were included in the analysis. All three FIRST-LCD algorithms provided statistically significant increases in liver-to-lesion contrast, with FIRSTMILD showing the largest increase (40.47 HU in AIDR-3D; 45.84 HU in FIRSTMILD; P < 0.001). Substantial improvement was shown in contrast-to-noise metrics. Visual analysis of the lesions shows decreased lesion visibility with all FIRST methods in comparison to AIDR-3D, with FIRSTSTR showing the closest results (P < 0.001). CONCLUSION: Objective image metrics show promise for MBIR methods in improving the detectability of low-contrast liver lesions; however, subjective image quality may be perceived as inferior. Further improvements are necessary to enhance image quality and lesion detection.

A partial convolution generative adversarial network for lesion synthesis and enhanced liver tumor segmentation.

Lesion segmentation is critical for clinicians to accurately stage the disease and determine treatment strategy. Deep learning based automatic segmentation can improve both the segmentation efficiency and accuracy. However, training a robust deep learning segmentation model requires sufficient training examples with sufficient diversity in lesion location and lesion size. This study is to develop a deep learning framework for generation of synthetic lesions with various locations and sizes that can be included in the training dataset to enhance the lesion segmentation performance. The lesion synthesis network is a modified generative adversarial network (GAN). Specifically, we innovated a partial convolution strategy to construct a U-Net-like generator. The discriminator is designed using Wasserstein GAN with gradient penalty and spectral normalization. A mask generation method based on principal component analysis (PCA) was developed to model various lesion shapes. The generated masks are then converted into liver lesions through a lesion synthesis network. The lesion synthesis framework was evaluated for lesion textures, and the synthetic lesions were used to train a lesion segmentation network to further validate the effectiveness of the lesion synthesis framework. All the networks are trained and tested on the LITS public dataset. Our experiments demonstrate that the synthetic lesions generated by our approach have very similar distributions for the two parameters, GLCM-energy and GLCM-correlation. Including the synthetic lesions in the segmentation network improved the segmentation dice performance from 67.3% to 71.4%. Meanwhile, the precision and sensitivity for lesion segmentation were improved from 74.6% to 76.0% and 66.1% to 70.9%, respectively. The proposed lesion synthesis approach outperforms the other two existing approaches. Including the synthetic lesion data into the training dataset significantly improves the segmentation performance.

Shear-wave elastography combined with contrast-enhanced ultrasound algorithm for noninvasive characterization of focal liver lesions.

PURPOSE: To establish shear-wave elastography (SWE) combined with contrast-enhanced ultrasound (CEUS) algorithm (SCCA) and improve the diagnostic performance in differentiating focal liver lesions (FLLs). MATERIAL AND METHODS: We retrospectively selected patients with FLLs between January 2018 and December 2019 at the First Affiliated Hospital of Sun Yat-sen University. Histopathology was used as a standard criterion except for hemangiomas and focal nodular hyperplasia. CEUS with SonoVue (Bracco Imaging) and SCCA combining CEUS and maximum value of elastography with < 20 kPa and > 90 kPa thresholds were used for the diagnosis of FLLs. The diagnostic performance of CEUS and SCCA was calculated and compared. RESULTS: A total of 171 FLLs were included, with 124 malignant FLLs and 47 benign FLLs. The area under curve (AUC), sensitivity, and specificity in detecting malignant FLLs were 0.83, 91.94%, and 74.47% for CEUS, respectively, and 0.89, 91.94%, and 85.11% for SCCA, respectively. The AUC of SCCA was significantly higher than that of CEUS (P = 0.019). Decision curves indicated that SCCA provided greater clinical benefits. The SCCA provided significantly improved prediction of clinical outcomes, with a net reclassification improvement index of 10.64% (P = 0.018) and integrated discrimination improvement of 0.106 (P = 0.019). For subgroup analysis, we divided the FLLs into a chronic-liver-disease group (n = 88 FLLs) and a normal-liver group (n = 83 FLLs) according to the liver background. In the chronic-liver-disease group, there were no differences between the CEUS-based and SCCA diagnoses. In the normal-liver group, the AUC of SCCA and CEUS in the characterization of FLLs were 0.89 and 0.83, respectively (P = 0.018). CONCLUSION: SCCA is a feasible tool for differentiating FLLs in patients with normal liver backgrounds. Further investigations are necessary to validate the universality of this algorithm.

[Application of Diffusion-Weighted Imaging and Hepatobiliary-Specific Contrast Agent Gd-EOB-DTPA in the Diagnosis and Differential Diagnosis of Focal Liver Lesions].

There are many types of focal liver lesions (FLL) presenting different lesion signs and their diagnosis and differential diagnosis are relatively difficult. It is of great clinical significance to accurately detect, classify and characterize focal liver lesions as soon as possible. Diffusion-weighted imaging (DWI) provides information on liver cell density, microstructure, and microcirculation perfusion. Gadolinium-ethoxibenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) is a hepatobiliary-specific contrast agent. Gd-EOB-DTPA-enhanced MRI examination of liver provides information on the blood perfusion of lesions and specific information on the uptake function of normal liver cells. The combined application of the two can significantly improve the sensitivity and diagnostic accuracy in the detection of FLL. Herein, we reviewed the research findings on the application of DWI and Gd-EOB-DTPA in FLL diagnosis in order to provide reference for further clinical application. Most of the existing studies only made comparison and discussion of the DWI image quality of different b values and their fitted apparent diffusion coefficient (ADC) values before and after Gd-EOB-DTPA enhancement, and the reported findings are not only varied, but also inconsistent. Whether Gd-EOB-DTPA will affect DWI images is still been debated. Future research should focus on quantitative comparison, discussion and verification of the enhancement effect after injection of Gd-EOB-DTPA, as well as the changes in the ADC value corresponding to different b values before and after enhancement, in order to provide more objective and consistent research results for clinical application.

Presence of Color Transition in Biopsy Specimens Predicts Outcome of Liver Lesion Biopsies.

Background: The aim of this study was to evaluate the usefulness of the presence of tissue transition in liver lesion biopsies to predict a successful outcome, as observed by modified macroscopic on-site evaluation (MOSE). Methods: This is a retrospective analysis of 264 ultrasound-guided liver lesion biopsies, examining the influence the presence of tissue transition (visible color changes in biopsy specimens as evaluated visually) has on two endpoints (1) material retrieval, (2) attaining a definitive diagnosis) representing successful liver lesion biopsies, compared to previously evaluated variables in this context. Uni- and multivariate analyses were performed using SPSS 21.0. Results: Material retrieval and a definitive diagnosis occurred in 224/264 (84.8%) and 217/264 (82.2%) cases, the latter occurring more often when visual inspection revealed macroscopic tissue transition (92/96 [95.8%]) than when not (124/165 [75.2%]), P < 0.001. Tissue transition in biopsies was more common in secondary (74/162 [45.7%]) than (18/54 [33.3%]) primary liver lesions, though this was not significant (P = 0.112). On multivariate analysis, tissue transition in biopsies was an independent predictor of a definitive diagnosis and material retrieval. Conclusion: In liver lesion biopsies, MOSE of color transition in biopsies can indicate success. This is easily incorporated into clinical practice and can help overcome the lack of an on-site pathologist.

Echinococcal hepatic lesion mimicking metastasis from colon cancer: two case reports.

BACKGROUND: Echinococcus is a worldwide zoonosis, primarily causing liver lesions. Accidentally detected, these lesions enter the differential diagnosis of a tumor, including metastasis. This situation is especially challenging in patients with colorectal cancer, as both diseases affect mainly the liver. CASE PRESENTATION: We report two patients with a newly diagnosed colorectal cancer. Pre- and intraoperatively radiological imaging revealed hepatic lesions which were resected on suspicion of colorectal cancer metastasis. Histology showed granulomatous lesions with characteristic parasitic membrane consistent with an echinococcal cyst. The diagnosis was confirmed by specific polymerase chain reaction. CONCLUSIONS: Focal hypoechoic liver lesion in patients with colorectal cancer should be primarily considered as a liver metastasis and resected whenever feasible. Other uncommon etiologies, including parasitic lesion as echinococcal cysts, should be taken in consideration, as this could lead to major changes of the management and prognosis of the affected patients.

Non-invasive multi-channel deep learning convolutional neural networks for localization and classification of common hepatic lesions.

PURPOSE: Machine learning techniques, especially convolutional neural networks (CNN), have revolutionized the spectrum of computer vision tasks with a primary focus on supervised and labelled image datasets. We aimed to assess a novel method to segment the liver from the abdomen computed tomography (CT) image using the CNN network, and to train a unique method to locate and classify liver lesion pre-histological findings using multi-channel deep learning CNN (MDL-CNN). MATERIAL AND METHODS: The post-contrast CT images of the liver with a resolution of 0.625 mm were chosen for the study. In a random method, 50 examples of each hepatocellular carcinomas, metastases tumours, haemangiomas, hepatic cysts were chosen and evaluated. RESULTS: The dice score quantitatively analyses the similarity of segmentation results with the training dataset. In the first CNN model for segmenting the liver, the dice score was 96.18%. The MDL-CNN model yielded 98.78% accuracy in classification, and the dice score for locating liver lesions was 95.70%. Additionally, the performance of this model was compared to various other existing models. CONCLUSIONS: According to our study, the machine learning approach can be successfully implemented to segment the liver and classify lesions, which will help radiologists impart better diagnosis.

Contrast-Enhanced Ultrasound of Congenital and Infantile Hemangiomas: Preliminary Results From a Case Series.

OBJECTIVE. The contrast-enhanced ultrasound (CEUS) imaging features of hepatic vascular tumors in infants, including infantile hemangioma (IH) and congenital hemangioma (CH), are not well reported. Frequent inaccurate use of lesion terminology in the literature has created diagnostic confusion. The purpose of this study is to describe the CEUS features of IH and CH. MATERIALS AND METHODS. Ten patients, ranging in age from 8 days to 16 months, with hepatic vascular tumors were included for retrospective analysis. Gray-scale ultrasound, color Doppler ultrasound, and CEUS features were reviewed, and interobserver kappa coefficients were calculated. Final diagnoses were clinically determined by a pediatrician with expertise in vascular anomalies except in one patient who underwent surgical excision. RESULTS. Of the 10 patients, five had CHs and five had IHs. All 10 lesions were hyperenhancing in the early arterial phase. In the portal venous phase, four of five (80%) CHs showed hyperenhancement relative to normal liver parenchyma, whereas four of five (80%) IHs showed isoenhancement. In the late phase, washout of contrast material was seen in three of five (60%) IHs, whereas one IH remained isoenhancing and one IH was hyperenhancing. None of the CHs showed late washout. Interobserver kappa coefficients for CEUS features ranged from 0.60 to 1.00. CONCLUSION. Except for the CEUS feature portal venous phase enhancement (κ = 0.60), good to excellent (κ = 0.74-1.00) agreement about CEUS features of IHs and CHs was observed. A significant proportion of IHs (60%) showed washout at delayed phase imaging, which has also been reported with malignancies. Recognition of the overlap in imaging appearance of these two entities is vital to preventing misdiagnosis of malignancy.

Contrast-Enhanced Ultrasound Features of Hepatic Reactive Lymphoid Hyperplasia: Correlation With Histopathologic Findings.

OBJECTIVES: To find certain specifics of hepatic reactive lymphoid hyperplasia (HRLH) on contrast-enhanced ultrasound (CEUS) imaging as diagnostic imaging clues by retrospectively analyzing its enhancement features. METHODS: From June 2010 to June 2017, 18 histopathologically confirmed HRLH lesions in 18 patients were included in this retrospective study. The lesion's location, maximum diameter, shape, margin, echogenicity, and color flow signal on conventional ultrasound (US) imaging and enhancement pattern, presence of a feeding artery, and donutlike enhancement on CEUS imaging were observed and recorded. The lesion size on CEUS imaging at peak enhancement and that on conventional US imaging were compared and recorded. RESULTS: All of the lesions showed homogeneous hypoechogenicity with a regular well-defined margin on conventional US imaging, with a mean diameter ± SD of 14.3 ± 4.6 mm (range, 8-24 mm). On CEUS imaging, all of the lesions showed "quick-wash-in and quick-wash-out," which showed complete homogeneous hyperenhancement in the arterial phase and wash-out in the second half of the arterial phase or first half of the portal phase. In 83.3% (15 of 18) of the lesions, the lesion size that was enhanced at peak was enlarged compared with the hypoechoic area on conventional US imaging, and transient donutlike enhancement appeared when the lesion showed wash-out. In 55.6% (10 of 18) of cases, the feeding artery was detected. CONCLUSIONS: Enlarged complete homogeneous hyperenhancement in the arterial phase, consequently followed by quick wash-out of the lesion and the appearance of donutlike enhancement, may be the CEUS features of HRLH.

Comparison of Sonazoid and SonoVue in the Diagnosis of Focal Liver Lesions: A Preliminary Study.

OBJECTIVE: This study aimed to compare the efficacy of Sonazoid and SonoVue in subjects with focal liver lesions. METHODS: The patients who had untreated focal solid liver lesions confirmed by B-mode ultrasonography were eligible for the study. The target lesion and whole liver were scanned by gray scale ultrasonography; then, contrast-enhanced ultrasonography was performed, and the results were evaluated blindly. The main end point was accuracy improvement with postcontrast versus precontrast ultrasound examination for diagnosis of the target lesion of interest as malignant or benign against the reference standard. RESULTS: There were 65 patients with 65 hepatic tumors enrolled in the study. The improvement of diagnostic accuracy was 0.30 in the Sonazoid group and 0.16 in the SonoVue group (95% confidence interval, -0.828-0.168; P = .24). Using 20% as the noninferiority margin, the upper limit of the 95% confidence interval (0.168) was less than 0.20. The number of lesions detected during the whole-liver scanning in the Sonazoid group was significantly more than that detected in the SonoVue group (P = .024). CONCLUSION: The diagnosis value of Sonazoid is noninferior to SonoVue, and this new contrast agent can improves the whole-liver image quality.

Added diagnostic value of respiratory-gated 4D 18F-FDG PET/CT in the detection of liver lesions: a multicenter study.

PURPOSE: The aim of the present study was to evaluate the added diagnostic value of respiratory-gated 4D18F-FDG PET/CT in liver lesion detection and characterization in a European multicenter retrospective study. METHODS: Fifty-six oncological patients (29 males and 27 females, mean age, 61.2 ± 11.2 years) from five European centers, submitted to standard 3D-PET/CT and liver 4D-PET/CT were retrospectively evaluated. Based on visual analysis, liver PET/CT findings were scored as positive, negative, or equivocal both in 3D and 4D PET/CT. The impact of 4D-PET/CT on the confidence in classifying liver lesions was assessed. PET/CT findings were compared to histology and clinical follow-up as standard reference and diagnostic accuracy was calculated for both techniques. At semi-quantitative analysis, SUVmax was calculated for each detected lesion in 3D and 4D-PET/CT. RESULTS: Overall, 72 liver lesions were considered for the analysis. Based on visual analysis in 3D-PET/CT, 32/72 (44.4%) lesions were considered positive, 21/72 (29.2%) negative, and 19/72 (26.4%) equivocal, while in 4D-PET/CT 48/72 (66.7%) lesions were defined positive, 23/72 (31.9%) negative, and 1/72 (1.4%) equivocal. 4D-PET/CT findings increased the confidence in lesion definition in 37/72 lesions (51.4%). Considering 3D equivocal lesions as positive, sensitivity, specificity, and accuracy were 88.9, 70.0, and 83.1%, respectively, while the same figures were 67.7, 90.0, and 73.8% if 3D equivocal findings were included as negative. 4D-PET/CT sensitivity, specificity, and accuracy were 97.8, 90.0, and 95.4%, respectively, considering equivocal lesions as positive and 95.6, 90.0, and 93.8% considering equivocal lesions as negative. The SUVmax of the liver lesions in 4D-PET (mean ± SD, 6.9 ± 3.2) was significantly higher (p < 0.001) than SUVmax in 3D-PET (mean ± SD, 5.2 ± 2.3). CONCLUSIONS: Respiratory-gated PET/CT technique is a valuable clinical tool in diagnosing liver lesions, reducing 3D undetermined findings, improving diagnostic accuracy, and confidence in reporting. 4D-PET/CT also improved the quantification of SUVmax of liver lesions.

[Exploring focal liver lesions with contrastenhanced ultrasonography]. / Comment j'explore... Une lésion hépatique focale par une échographie de contraste.

Noninvasive diagnosis of focal liver lesions is usually based on unenhanced ultrasonography, computed tomography, or magnetic resonance. Contrast-enhanced ultrasonography (CEUS) can be used among the other imaging modalities in evaluating these lesions. The technique requires the intravenous injection of contrast media. Ultrasound contrast agents are very safe and the technique doesn't involve any radiation. A large body of evidence supports and clarifies the role of CEUS in evaluating focal liver lesions. Hemangiomas, focal nodular hyperplasia, focal fatty change will be diagnosed in a majority of cases and CEUS is also helpful in distinguishing malignant from benign lesions.

Le diagnostic non invasif des lésions hépatiques focales repose habituellement sur l'échographie conventionnelle, la tomodensitométrie ou l'imagerie par résonance magnétique. L'échographie de contraste vient s'ajouter à l'arsenal des techniques d'imagerie disponibles et peut être utilisée dans la mise au point de ces lésions. Elle requiert l'utilisation de produits de contraste ultrasonores qui sont injectés en intraveineux. Ceux-ci présentent un excellent profil de sécurité et le caractère non irradiant de la technique est un avantage supplémentaire. De nombreuses publications illustrent la contribution et la place de l'échographie de contraste dans le bilan des lésions hépatiques focales. Elle est performante dans le diagnostic d'hémangiome, d'hyperplasie nodulaire focale et de stéatose hétérogène, et s'avère également contributive dans l'exploration des lésions malignes.

Liver Masses: What Physicians Need to Know About Ordering and Interpreting Liver Imaging.

PURPOSE OF REVIEW: This paper reviews diagnostic imaging techniques used to characterize liver masses and the imaging characteristics of the most common liver masses. RECENT FINDINGS: The role of recently adopted ultrasound and magnetic resonance imaging contrast agents will be emphasized. Contrast-enhanced ultrasound is an inexpensive exam which can confirm benignity of certain liver masses without ionizing radiation. Magnetic resonance imaging using hepatocyte-specific gadolinium-based contrast agents can help confirm or narrow the differential diagnosis of liver masses.

Focal Liver Lesions: Real-time 3-Dimensional Contrast-Enhanced Ultrasonography Compared With 2-Dimensional Contrast-Enhanced Ultrasonography and Magnetic Resonance Imaging.

OBJECTIVES: This study sought to evaluate the application of real-time 3-dimensional (3D) contrast-enhanced ultrasonography (US) to diagnose focal liver lesions and to compare these results with those from 2-dimensional (2D) contrast-enhanced US and contrast-enhanced magnetic resonance imaging (MRI). METHODS: Patients with focal liver lesions were examined by 2D contrast-enhanced US, 3D contrast-enhanced US, and contrast-enhanced MRI for lesion characterization, and biopsies and comprehensive clinical diagnoses served as reference standards. The sensitivity, specificity, area under the receiver operating characteristic curve, and intermodality agreement were assessed. The number of contrast agent injections and lesions observed per injection were calculated for 3D and 2D contrast-enhanced US. The number and display quality of the feeding arteries observed with 3D and 2D contrast-enhanced US were assessed. RESULTS: A total of 117 patients with 151 focal liver lesions were enrolled, including 67 cases of hepatocellular carcinoma, 51 cases of liver metastasis, and 33 cases of benign liver lesions. No significant differences were found among the modalities. The sensitivity values for 3D contrast-enhanced US, 2D contrast-enhanced US, and contrast-enhanced MRI were 96%, 95%, and 93%, respectively; the specificity values were 87%, 84%, and 89%; and the area under the receiver operating characteristic curve values were 0.92, 0.90, and 0.92. The intermodality agreement was excellent (κ > 0.77). Fewer contrast agent injections were needed, and more lesions and feeding arteries were more clearly displayed on 3D than 2D contrast-enhanced US (P < .001). CONCLUSIONS: Real-time 3D contrast-enhanced US is useful for diagnosing focal liver lesions and for observing feeding arteries with fewer contrast agent injections.

Impact of Modified Anisotropic Diffusion-based Enhancement Method in Computer-Aided Classification of Focal Liver Lesions.

This work is presented with the objective to assess quantitatively the impact of modified anisotropic diffusion-based enhancement method of Mittal et al. in computer-aided classification of focal liver lesions. This assessment was made before and after enhancement of clinically acquired ultrasound images with the comparison of (a) discrimination capability of radiologically important texture contrast feature using box plot and p-value statistics and (b) test results of designed computer-aided classification schemes to detect/classify focal liver tissues using receiver operating characteristic curves. The results reveal that the application of enhancement method on clinically acquired ultrasound image may effectively improve the confidence of clinicians/radiologists in computer-aided diagnostic solutions to detect and classify focal liver lesions.