Liver Imaging Reporting and Data System Contrast-Enhanced US Nonradiation Treatment Response Assessment Version 2024.

The American College of Radiology Liver Imaging Reporting and Data System (LI-RADS) standardizes the imaging technique, reporting lexicon, disease categorization, and management for patients with or at risk for hepatocellular carcinoma (HCC). LI-RADS encompasses HCC surveillance with US; HCC diagnosis with CT, MRI, or contrast-enhanced US (CEUS); and treatment response assessment (TRA) with CT or MRI. LI-RADS was recently expanded to include CEUS TRA after nonradiation locoregional therapy or surgical resection. This report provides an overview of LI-RADS CEUS Nonradiation TRA v2024, including a lexicon of imaging findings, techniques, and imaging criteria for posttreatment tumor viability assessment. LI-RADS CEUS Nonradiation TRA v2024 takes into consideration differences in the CEUS appearance of viable tumor and posttreatment changes within and in close proximity to a treated lesion. Due to the high sensitivity of CEUS to vascular flow, posttreatment reactive changes commonly manifest as areas of abnormal perilesional enhancement without washout, especially in the first 3 months after treatment. To improve the accuracy of CEUS for nonradiation TRA, different diagnostic criteria are used to evaluate tumor viability within and outside of the treated lesion margin. Broader criteria for intralesional enhancement increase sensitivity for tumor viability detection. Stricter criteria for perilesional enhancement limit miscategorization of posttreatment reactive changes as viable tumor. Finally, the TRA algorithm reconciles intralesional and perilesional tumor viability assessment and assigns a single LI-RADS treatment response (LR-TR) category: LR-TR nonviable, LR-TR equivocal, or LR-TR viable.

Assessment of arterial-phase hyperenhancement and late-phase washout of hepatocellular carcinoma-a meta-analysis of contrast-enhanced ultrasound (CEUS) with SonoVue® and Sonazoid®.

OBJECTIVES: The recognition of arterial phase hyperenhancement (APHE) and washout during the late phase is key for correct diagnosis of hepatocellular carcinoma (HCC) with contrast-enhanced ultrasound (CEUS). This meta-analysis was conducted to compare SonoVue®-enhanced and Sonazoid®-enhanced ultrasound in the assessment of HCC enhancement and diagnosis. METHODS: Studies were included in the analysis if they reported data for HCC enhancement in the arterial phase and late phase for SonoVue® or in the arterial phase and Kupffer phase (KP) for Sonazoid®. Forty-two studies (7502 patients) with use of SonoVue® and 30 studies (2391 patients) with use of Sonazoid® were identified. In a pooled analysis, the comparison between SonoVue® and Sonazoid® CEUS was performed using chi-square test. An inverse variance weighted random-effect model was used to estimate proportion, sensitivity, and specificity along with 95% confidence interval (CI). RESULTS: In the meta-analysis, the proportion of HCC showing APHE with SonoVue®, 93% (95% CI 91-95%), was significantly higher than the proportion of HCC showing APHE with Sonazoid®, 77% (71-83%) (p < 0.0001); similarly, the proportion of HCC showing washout at late phase/KP was significantly higher with SonoVue®, 86% (83-89%), than with Sonazoid®, 76% (70-82%) (p < 0.0001). The sensitivity and specificity for the detection of APHE plus late-phase/KP washout detection in HCC were also higher with SonoVue® than with Sonazoid® (sensitivity 80% vs 52%; specificity 80% vs 73% in studies within unselected patient populations). CONCLUSION: APHE and late washout in HCC are more frequently observed with SonoVue® than with Sonazoid®. This may affect the diagnostic performance of CEUS in the diagnosis of HCCs. CLINICAL RELEVANCE STATEMENT: Meta-analysis data show the presence of key enhancement features for diagnosis of hepatocellular carcinoma is different between ultrasound contrast agents, and arterial hyperenhancement and late washout are more frequently observed at contrast-enhanced ultrasound with SonoVue® than with Sonazoid®. KEY POINTS: • Dynamic enhancement features are key for imaging-based diagnosis of HCC. • Arterial hyperenhancement and late washout are more often observed in HCCs using SonoVue®-enhanced US than with Sonazoid®. • The existing evidence for contrast-enhanced US may need to be considered being specific to the individual contrast agent.

Assessment of Early Therapy Response of Non-Hodgkin's and Hodgkin's Lymphoma Using B-Mode Ultrasound and Dynamic Contrast-Enhanced Ultrasound.

OBJECTIVES: Here we asked, whether contrast enhanced ultrasound (CEUS) enables to judge early treatment response in malignant lymphoma as a potential guidance for further treatment. METHODS: From May 2017 to May 2018, 21 patients with histologically confirmed diagnosis of lymphoma were examined by B-mode ultrasound (B-US) and CEUS at fixed early time points after commencing therapy (days [d] 0, 15 and 30 after therapy start) and contrast enhancement patterns in target lymphoma lesions were quantified using Bracco-VUE Box® (DCE-US). To estimate the potential value of CEUS-enhancement patterns for early response prediction, patients were grouped according to their best achieved actual response into complete remission (CR) patients, partial remission (PR) patients or progressive disease (PD) patients. RESULTS: Between d0, d15 and d30, CR-patients showed a median lymphoma shrinking by 34% in B-US. PD-patients experienced a median lymphoma size reduction by 44% on day 15, but lymphoma mass again increased by 20% between d15 and d30. In contrast, the median CEUS enhancement intensity, as assessed by the area under the curve (AUC) was increasing at d15 in CR and PD patients (CR to 152%, PD: to 126%), but decreased at d30 to 14% in CR patients and 22% in PD patients. CONCLUSIONS: While early response assessment using B-US might be useful to predict treatment response in lymphoma, CEUS and DCE-US-although often feasible-do not provide additional value in this regard.

Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer.

OBJECTIVES: To develop and validate an ultrasound elastography radiomics nomogram for preoperative evaluation of the axillary lymph node (ALN) burden in early-stage breast cancer. METHODS: Data of 303 patients from hospital #1 (training cohort) and 130 cases from hospital #2 (external validation cohort) between Jun 2016 and May 2019 were enrolled. Radiomics features were extracted from shear-wave elastography (SWE) and corresponding B-mode ultrasound (BMUS) images. The minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select ALN status-related features. Proportional odds ordinal logistic regression was performed using the radiomics signature together with clinical data, and an ordinal nomogram was subsequently developed. We evaluated its performance using C-index and calibration. RESULTS: SWE signature, US-reported LN status, and molecular subtype were independent risk factors associated with ALN status. The nomogram based on these variables showed good discrimination in the training (overall C-index: 0.842; 95%CI, 0.773-0.879) and the validation set (overall C-index: 0.822; 95%CI, 0.765-0.838). For discriminating between disease-free axilla (N0) and any axillary metastasis (N + (≥ 1)), it achieved a C-index of 0.845 (95%CI, 0.777-0.914) for the training cohort and 0.817 (95%CI, 0.769-0.865) for the validation cohort. The tool could also discriminate between low (N + (1-2)) and heavy metastatic ALN burden (N + (≥ 3)), with a C-index of 0.827 (95%CI, 0.742-0.913) in the training cohort and 0.810 (95%CI, 0.755-0.864) in the validation cohort. CONCLUSION: The radiomics model shows favourable predictive ability for ALN staging in patients with early-stage breast cancer, which could provide incremental information for decision-making. KEY POINTS: • Radiomics analysis helps radiologists to evaluate the axillary lymph node status of breast cancer with accuracy. • This multicentre retrospective study showed that radiomics nomogram based on shear-wave elastography provides incremental information for risk stratification. • Treatment can be given with more precision based on the model.

Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer.

PURPOSE: The aim of the study was to develop and validate a deep learning radiomic nomogram (DLRN) for preoperatively assessing breast cancer pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) based on the pre- and post-treatment ultrasound. METHODS: Patients with locally advanced breast cancer (LABC) proved by biopsy who proceeded to undergo preoperative NAC were enrolled from hospital #1 (training cohort, 356 cases) and hospital #2 (independent external validation cohort, 236 cases). Deep learning and handcrafted radiomic features reflecting the phenotypes of the pre-treatment (radiomic signature [RS] 1) and post-treatment tumour (RS2) were extracted. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression were used for feature selection and RS construction. A DLRN was then developed based on the RSs and independent clinicopathological risk factors. The performance of the model was assessed with regard to calibration, discrimination and clinical usefulness. RESULTS: The DLRN predicted the pCR status with accuracy, yielded an area under the receiver operator characteristic curve of 0.94 (95% confidence interval, 0.91-0.97) in the validation cohort, with good calibration. The DLRN outperformed the clinical model and single RS within both cohorts (P < 0.05, as per the DeLong test) and performed better than two experts' prediction of pCR (both P < 0.01 for comparison of total accuracy). Besides, prediction within the hormone receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative, HER2+ and triple-negative subgroups also achieved good discrimination performance, with an AUC of 0.90, 0.95 and 0.93, respectively, in the external validation cohort. Decision curve analysis confirmed that the model was clinically useful. CONCLUSION: A deep learning-based radiomic nomogram had good predictive value for pCR in LABC, which could provide valuable information for individual treatment.

Deep learning with convolutional neural network in the assessment of breast cancer molecular subtypes based on US images: a multicenter retrospective study.

OBJECTIVES: To evaluate the prediction performance of deep convolutional neural network (DCNN) based on ultrasound (US) images for the assessment of breast cancer molecular subtypes. METHODS: A dataset of 4828 US images from 1275 patients with primary breast cancer were used as the training samples. DCNN models were constructed primarily to predict the four St. Gallen molecular subtypes and secondarily to identify luminal disease from non-luminal disease based on the ground truth from immunohistochemical of whole tumor surgical specimen. US images from two other institutions were retained as independent test sets to validate the system. The models' performance was analyzed using per-class accuracy, positive predictive value (PPV), and Matthews correlation coefficient (MCC). RESULTS: The model achieved good performance in identifying the four breast cancer molecular subtypes in the two test sets, with accuracy ranging from 80.07% (95% CI, 76.49-83.23%) to 97.02% (95% CI, 95.22-98.16%) and 87.94% (95% CI, 85.08-90.31%) to 98.83% (95% CI, 97.60-99.43) for the two test cohorts for each sub-category, respectively. In terms of 4-class weighted average MCC, the model achieved 0.59 for test cohort A and 0.79 for test cohort B. Specifically, the DCNN also yielded good diagnostic performance in discriminating luminal disease from non-luminal disease, with a PPV of 93.29% (95% CI, 90.63-95.23%) and 88.21% (95% CI, 85.12-90.73%) for the two test cohorts, respectively. CONCLUSION: Using pretreatment US images of the breast cancer, deep learning model enables the assessment of molecular subtypes with high diagnostic accuracy. TRIAL REGISTRATION: Clinical trial number: ChiCTR1900027676 KEY POINTS: • Deep convolutional neural network (DCNN) helps clinicians assess tumor features with accuracy. • Multicenter retrospective study shows that DCNN derived from pretreatment ultrasound imagine improves the prediction of breast cancer molecular subtypes. • Management of patients becomes more precise based on the DCNN model.

Activity-Based Cost Analysis of Including Contrast-Enhanced Ultrasound (CEUS) in the Diagnostic Pathway of Focal Pancreatic Lesions Detected by Abdominal Ultrasound.

PURPOSE: To perform an activity-based cost analysis of the inclusion of contrast-enhanced ultrasound (CEUS) in the diagnostic pathway of newly detected focal pancreatic lesions revealed by abdominal ultrasound (US) in comparison to computed tomography (CT) and magnetic resonance imaging (MRI). MATERIALS AND METHODS: Over a 14-year period, 977 patients with newly detected focal pancreatic lesions on US and subsequently studied with CEUS and/or CT and MRI were included. The cost of equipment, materials and human resources for every imaging method was calculated. We analyzed the costs in different scenarios considering whether or not CT or MRI was required in the diagnostic pathway. The savings (R) were calculated by subtracting the differential cost of CEUS from the eliminated third-level exam (CCEUS: CEUS cost; CCT: CT cost; CMRI: MRI cost) compared to conventional ultrasound (CCEUS-CUS): R = CCT-(CCEUS-CUS) or R = CMRI-(CCEUS-CUS). RESULTS: Total costs were: US 28.39 €; CEUS 70.50 €; CT 106.23 €; MRI 219.61 €. In 388/563 patients CEUS characterized the pancreatic lesion as solid, with only CT being performed as a second-level investigation: the savings were 68 870.36 €. In 266/414 patients CEUS diagnosed lesions as cystic, with only MRI being performed as a second-level examination: the savings were 16 825.07 €. Considering the whole diagnostic pathway of the patients, the cost savings were 76 809.35 € for solid lesions and 26 242.49 € for cystic lesions, with overall savings of 103 051.84 €. CONCLUSION: CEUS represents a cost-effective imaging method for the differentiation of focal pancreatic lesions and could guide the selection of the best imaging modality for preoperative assessment, thereby optimizing resources and securing the diagnostic pathway.

Advantages and Limitations of Focal Liver Lesion Assessment with Ultrasound Contrast Agents: Comments on the European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) Guidelines.

Contrast-enhanced ultrasound (CEUS) represents a significant breakthrough in sonography. Due to US contrast agents (UCAs) and contrast-specific techniques, sonography offers the potential to show enhancement of liver lesions in a similar way as contrast-enhanced cross-sectional imaging techniques. The real-time assessment of liver perfusion throughout the vascular phases, without any risk of nephrotoxicity, represents one of the major advantages that this technique offers. CEUS has led to a dramatic improvement in the diagnostic accuracy of US and subsequently has been included in current guidelines as an important step in the diagnostic workup of focal liver lesions (FLLs), resulting in a better patient management and cost-effective therapy. The purpose of this review was to provide a detailed description of contrast agents used in different cross-sectional imaging procedures for the study of FLLs, focusing on characteristics, indications and advantages of UCAs in clinical practice.

Vascular phases in imaging and their role in focal liver lesions assessment.

The incidental finding of a liver lesion with basic ultrasound is one of the most common clinical issues. Some of the liver lesions which present typical morphological B-mode features (e.g. cysts, typically localized focal fatty sparing/accumulations, hyperechoic hemangiomas) can be easily diagnosed by conventional ultrasound without the need of further diagnostic procedures. Others frequently necessitate further investigation with contrast-enhanced imaging techniques or biopsy in order to differentiate benign from malignant lesions and obtain a final diagnosis. This paper will discuss differences between vascular phases of different cross-sectional contrast-enhanced methods, as well as their subsequent benefits for focal liver lesions (FLLs) assessment, adding also a particular emphasis on small FLLs detection and characterization.

Hepatosplenic sarcoidosis: contrast-enhanced ultrasound findings and implications for clinical practice.

Sarcoidosis is a complex granulomatous disease that affects virtually every organ and tissue, with a prevalence that varies significantly among the sites involved. The role of conventional imaging, such as computed tomography and magnetic resonance imaging, in the assessment of hepatosplenic sarcoidosis is well established by revealing organ enlargement, multiple discrete nodules, and lymphadenopathy. In this review, we aim to describe contrast-enhanced ultrasound (CEUS) findings in liver and spleen involvement by sarcoidosis, reporting evidence from the literature and cases from our experience, after a brief update on safety profile, cost-effectiveness, and clinical indications of this novel technique. Furthermore, we highlight potential advantages of CEUS in assessing hepatosplenic sarcoidosis that may be useful in the clinical practice.

Liver fibrosis in viral hepatitis: noninvasive assessment with acoustic radiation force impulse imaging versus transient elastography.

PURPOSE: To compare, in a pilot study, acoustic radiation force impulse (ARFI) imaging technology integrated into a conventional ultrasonography (US) system with both transient elastography (TE) and serologic fibrosis marker testing for the noninvasive assessment of liver fibrosis. MATERIALS AND METHODS: Informed consent was obtained from all subjects, and the local ethics committee approved the study. ARFI imaging involved the mechanical excitation of tissue with use of short-duration acoustic pulses to generate localized displacements in tissue. The displacements resulted in shear-wave propagation, which was tracked by using US correlation-based methods and recorded in meters per second. Eighty-six patients with chronic viral hepatitis underwent TE, ARFI imaging, and serum fibrosis marker testing. Results were compared with liver biopsy findings, which served as the reference standard. RESULTS: ARFI imaging (rho = 0.71), TE (rho = 0.73), and serum fibrosis marker test (rho = 0.66) results correlated significantly with histologic fibrosis stage (P < .001). Median ARFI velocities ranged from 0.84 to 3.83 m/sec. Areas under the receiver operating characteristic curve for the accuracy of ARFI imaging, TE, and serum fibrosis marker testing were 0.82, 0.84, and 0.82, respectively, for the diagnosis of moderate fibrosis (histologic fibrosis stage, > or = 2) and 0.91, 0.91, and 0.82, respectively, for the diagnosis of cirrhosis. CONCLUSION: ARFI imaging is a promising US-based method for assessing liver fibrosis in chronic viral hepatitis, with diagnostic accuracy comparable to that of TE in this preliminary study. SUPPLEMENTAL MATERIAL: http://radiology.rsnajnls.org/cgi/content/full/252/2/595/DC1.

Assessment of metastatic liver disease in patients with primary extrahepatic tumors by contrast-enhanced sonography versus CT and MRI.

AIM: To evaluate contrast-enhanced ultrasonography (CEUS) using SonoVue in the detection of liver metastases in patients with known extrahepatic primary tumors versus the combined gold standard comprising CT, MRI and clinical/histological data. METHODS: It is an international multicenter study, and there were 12 centres and 125 patients (64 males, 61 females, aged 59+/-11 years) involved, with 102 patients per protocol. Primary tumors were colorectal in 35%, breast in 27%, pancreatic in 17% and others in 21%. CEUS using SonoVue was employed with a low-mechanical-index technique and contrast-specific software using Siemens Elegra, Philips HDI 5000 and Acuson Sequoia; continuous scanning for at least five minutes. RESULTS: CEUS with SonoVue increased significantly the number of focal liver lesions detected versus unenhanced sonography. In 31.4% of the patients, more lesions were found after contrast enhancement. The total numbers of lesions detected were comparable with CEUS (55), triple-phase spiral CT (61) and MRI with a liver-specific contrast agent (53). Accuracy of detection of metastatic disease (i.e. at least one metastatic lesion) was significantly higher for CEUS (91.2%) than for unenhanced sonography (81.4%) and was similar to that of triple-phase spiral CT (89.2%). In 53 patients whose CEUS examination was negative, a follow-up examination 3-6 mo later confirmed the absence of metastatic lesions in 50 patients (94.4%). CONCLUSION: CEUS is proved to be reliable in the detection of liver metastases in patients with known extrahepatic primary tumors and suspected liver lesions.