Contrast-enhanced Ultrasound Data Research Articles

Contrast-Enhanced Ultrasound Reveals Superior Gluteus Medius Muscle Vitality After Total Hip Arthroplasty in a Minimally Invasive Anterolateral Versus Lateral Surgical Approach

ObjectiveTotal hip arthroplasty (THA) is essential for treating severe osteoarthritis. With various surgical approaches available, there has been a shift toward techniques that offer quicker recovery and fewer complications. In this study, contrast-enhanced ultrasound was used to evaluate the impact of the minimally invasive anterolateral versus conventional lateral approach in THA on the gluteus medius muscle, focusing on muscle vitality and functional outcomes. MethodsA retrospective, cross-sectional analysis of 64 patients who underwent unilateral THA (26 lateral and 38 anterolateral) was conducted. Muscle strength was measured alongside joint flexibility and patient-reported outcome measures. Contrast-enhanced ultrasound (CEUS) examinations were conducted to visualize gluteus medius microperfusion. CEUS data were analyzed using VueBox 7.1 software and microperfusion parameters were calculated using peak enhancement (PE), wash-in perfusion index (WiPI) and rise time for muscle vitality evaluation. ResultsPatients from the anterolateral group exhibited higher relative muscle strength in the operated hip compared with the healthy side (96 ± 23% vs. 86 ± 23%, p = 0.048). CEUS revealed superior gluteus medius microperfusion in the anterolateral group (PE 36,300 ± 42,000 arbitrary units [a.u.] vs. 20,400 ± 20,200 a.u., p = 0.024; WiPI 23,600 ± 27,300 a.u. vs. 13,500 ± 13,300 a.u., p = 0.027). A positive Trendelenburg sign was evident in 2 out of 26 patients in the lateral group compared with none in the anterolateral group (χ2 = 0.082), indicating higher rates of gluteal insufficiency in the lateral approach group. ConclusionSignificantly higher muscle perfusion parameters in the anterolateral group, alongside improved muscle strength recovery, hint at a subtle but important advantage regarding post-operative recovery of the anterolateral approach. These findings support the ongoing trend toward minimally invasive surgical approaches in THA.

Clinical impact of CEUS on non-characterizable observations and observations with intermediate probability of malignancy on CT/MRI in patients at risk for HCC

BackgroundHepatocellular carcinoma (HCC) is a unique cancer allowing tumor diagnosis with identification of definitive patterns of enhancement on contrast-enhanced imaging, avoiding invasive biopsy. However, it is still unclear to what extent Contrast-Enhanced Ultrasound (CEUS) is a clinically useful additional step when Computed tomography (CT) or Magnetic resonance imaging (MRI) are inconclusive.MethodsA prospective international multicenter validation study for CEUS Liver Imaging Reporting and Data System (LI-RADS) was conducted between January 2018 and August 2021. 646 patients at risk for HCC with focal liver lesions were enrolled. CEUS was performed using an intravenous ultrasound contrast agent within 4 weeks of CT/MRI. Liver nodules were categorized based on LI-RADS (LR) criteria. Histology or one-year follow-up CT/MRI imaging results were used as the reference standard. The diagnostic performance of CEUS was evaluated for inconclusive CT/MRI scan in two scenarios for which the AASLD recommends repeat imaging or imaging follow-up: observations deemed non-characterizable (LR-NC) or with indeterminate probability of malignancy (LR-3).Results75 observations on CT or MRI were categorized as LR-3 (n = 54) or LR-NC (n = 21) CEUS recategorization of such observations into a different LR category (namely, into one among LR-1, LR-2, LR-5, LR-M, or LR-TIV) resulted in management recommendation changes in 33.3% (25/75) and in all but one (96.0%, 24/25) observation, the new management recommendations were correct.ConclusionCEUS LI-RADS resulted in management recommendations change in substantial number of liver observations with initial indeterminate CT/MRI characterization, identifying both non-malignant lesions and HCC, potentially accelerating the diagnostic process and alleviating the need for biopsy or follow-up imaging.ClinicalTrials.gov number, NCT03318380.

Risk Factors for Early Recurrence After Radical Resection of Hepatocellular Carcinoma Based on Preoperative Contrast-Enhanced Ultrasound and Clinical Features.

To investigate risk factors for the early recurrence (ER) of hepatocellular carcinoma (HCC) after radical resection based on preoperative contrast-enhanced ultrasound (CEUS) and clinical features to provide guidance for clinical treatment. The retrospective analysis selected 130 HCC patients who underwent radical tumor resection from October 2019 to November 2021. All patients underwent preoperative routine ultrasound examination and CEUS, and the pathology was confirmed as HCC after surgery. The patients were divided into two groups based on whether there is an ER, namely the ER group and the non-ER group. The general clinical, routine and CEUS data of patients were collected, and the factors were selected by using the least absolute shrinkage and selection operator (LASSO) regression. Multivariate logistic regression was used to screen the independent influencing factors of ER. Then a nomogram model was established to predict the risk of ER, and the application value of nomogram through internal validation was evaluated. Multivariate logistic regression identified several independent factors influencing ER after radical HCC resection. Significant factors included early wash-out phase (95%CI = 0.003-0.206, P = 0.001), liver cirrhosis (95%CI = 2.835-221.224, P = 0.004), incomplete envelope (95%CI = 5.247-1056.130,P = 0.001), multiple lesions (95%CI = 1.110-135.424,P = 0.041), Albumin <40 g/L (95%CI = 2.496-127.223,P = 0.004), and Golgi Protein 73 (GP73) ≥ 85 ng/mL (95%CI = 1.594-30.002, P = 0.010), with all P-values <0.05. The nomogram prediction model constructed based on the results of multivariate logistic regression, demonstrated a ROC curve AUC of 0.879, a sensitivity of 93.5%, a specificity of 66.7%, and a C-index of 0.602, indicating superior diagnostic efficiency compared to independent influencing factors. The ER nomogram prediction model confirmed good discrimination and calibration in internal validation. The CEUS-Clinical combined model effectively monitors the risk of ER in high-risk populations following radical resection of HCC, timely interventions to improve patient prognosis.

Improving sub-pixel accuracy in ultrasound localization microscopy using supervised and self-supervised deep learning

With a spatial resolution of tens of microns, ultrasound localization microscopy (ULM) reconstructs microvascular structures and measures intravascular flows by tracking microbubbles (1–5 μm) in contrast enhanced ultrasound (CEUS) images. Since the size of CEUS bubble traces, e.g. 0.5–1 mm for ultrasound with a wavelength λ = 280 μm, is typically two orders of magnitude larger than the bubble diameter, accurately localizing microbubbles in noisy CEUS data is vital to the fidelity of the ULM results. In this paper, we introduce a residual learning based supervised super-resolution blind deconvolution network (SupBD-net), and a new loss function for a self-supervised blind deconvolution network (SelfBD-net), for detecting bubble centers at a spatial resolution finer than λ/10. Our ultimate purpose is to improve the ability to distinguish closely located microvessels and the accuracy of the velocity profile measurements in macrovessels. Using realistic synthetic data, the performance of these methods is calibrated and compared against several recently introduced deep learning and blind deconvolution techniques. For bubble detection, errors in bubble center location increase with the trace size, noise level, and bubble concentration. For all cases, SupBD-net yields the least error, keeping it below 0.1 λ. For unknown bubble trace morphology, where all the supervised learning methods fail, SelfBD-net can still maintain an error of less than 0.15 λ. SupBD-net also outperforms the other methods in separating closely located bubbles and parallel microvessels. In macrovessels, SupBD-net maintains the least errors in the vessel radius and velocity profile after introducing a procedure that corrects for terminated tracks caused by overlapping traces. Application of these methods is demonstrated by mapping the cerebral microvasculature of a neonatal pig, where neighboring microvessels separated by 0.15 λ can be readily distinguished by SupBD-net and SelfBD-net, but not by the other techniques. Hence, the newly proposed residual learning based methods improve the spatial resolution and accuracy of ULM in micro- and macro-vessels.

Using multimodal ultrasound including full-time-series contrast-enhanced ultrasound cines for identifying the nature of thyroid nodules.

Based on the conventional ultrasound images of thyroid nodules, contrast-enhanced ultrasound (CEUS) videos were analyzed to investigate whether CEUS improves the classification accuracy of benign and malignant thyroid nodules using machine learning (ML) radiomics and compared with radiologists. The B-mode ultrasound (B-US), real-time elastography (RTE), color doppler flow images (CDFI) and CEUS cines of patients from two centers were retrospectively gathered. Then, the region of interest (ROI) was delineated to extract the radiomics features. Seven ML algorithms combined with four kinds of radiomics data (B-US, B-US + CDFI + RTE, CEUS, and B-US + CDFI + RTE + CEUS) were applied to establish 28 models. The diagnostic performance of ML models was compared with interpretations from expert and nonexpert readers. A total of 181 thyroid nodules from 181 patients of 64 men (mean age, 42 years +/- 12) and 117 women (mean age, 46 years +/- 12) were included. Adaptive boosting (AdaBoost) achieved the highest area under the receiver operating characteristic curve (AUC) of 0.89 in the test set among 28 models when combined with B-US + CDFI + RTE + CEUS data and an AUC of 0.72 and 0.66 when combined with B-US and B-US + CDFI + RTE data. The AUC achieved by senior and junior radiologists was 0.78 versus (vs.) 0.69 (p > 0.05), 0.79 vs. 0.64 (p < 0.05), and 0.88 vs. 0.69 (p < 0.05) combined with B-US, B-US+CDFI+RTE and B-US+CDFI+RTE+CEUS, respectively. With the addition of CEUS, the diagnostic performance was enhanced for all seven classifiers and senior radiologists based on conventional ultrasound images, while no enhancement was observed for junior radiologists. The diagnostic performance of ML models was similar to senior radiologists, but superior to those junior radiologists.

Deep learning radiomics of multimodal ultrasound for classifying metastaticcervicallymphadenopathy into primary cancer sites: a feasibility study.

To investigate the feasibility of deep learning radiomics (DLR) based on multimodal ultrasound to differentiate the primary cancer sites of metastatic cervical lymphadenopathy (CLA). This study analyzed 280 biopsy-confirmed metastatic CLAs from 280 cancer patients, including 54 from head and neck squamous cell carcinoma (HNSCC), 58 from thyroid cancer (TC), 92 from lung cancer (LC), and 76 from gastrointestinal cancer (GIC). Before biopsy, patients underwent conventional ultrasound (CUS), ultrasound elastography (UE), and contrast-enhanced ultrasound (CEUS). Based on CUS, DLR models using CUS, CUS+UE, CUS+CEUS, and CUS+UE+CEUS data were developed and compared. The best model was integrated with key clinical indicators selected by univariate analysis to achieve the best classification performance. All DLR models achieved similar performance with respect to classifying four primary tumor sites of metastatic CLA (AUC:0.708~0.755). After integrating key clinical indicators (age, sex, and neck level), the US+UE+CEUS+clinical model yielded the best performance with an overall AUC of 0.822 in the validation cohort, but there was no significance compared with the basal CUS+clinical model (P>0.05), both of which identified metastasis from HNSCC, TC, LC, and GIC with 0.869 and 0.911, 0.838 and 0.916, 0.750 and 0.610, and 0.829 and 0.769, respectively. The ultrasound-based DLR model can be used to classify the primary cancer sites of metastatic CLA, and the CUS combined with clinical indicators is adequate to provide a high discriminatory performance. The addition of the combination of UE and CEUS data is expected to further improve performance.

Predicting T Cell-Inflamed Gene Expression Profile in Hepatocellular Carcinoma Based on Dynamic Contrast-Enhanced Ultrasound Radiomics.

The T cell-inflamed gene expression profile (GEP) quantifies 18 genes' expression indicative of a T-cell immune tumor microenvironment, playing a crucial role in the immunotherapy of hepatocellular carcinoma (HCC). Our study aims to develop a radiomics-based machine learning model using contrast-enhanced ultrasound (CEUS) for predicting T cell-inflamed GEP in HCC. The primary cohort of HCC patients with preoperative CEUS and RNA sequencing data of tumor tissues at the single center was used to construct the model. A total of 5936 radiomics features were extracted from the regions of interest in representative images of each phase, and the least absolute shrinkage and selection operator and logistic regression were used to construct four models including three phase-specific models and an integrated model. The area under the curve (AUC) was calculated to evaluate the performance of the model. The independent cohort of HCC patients with preoperative CEUS and Immunoscore based on immunohistochemistry and digital pathology was used to validate the correlation between model prediction value and T-cell infiltration. There were 268 patients enrolled in the primary cohort and 46 patients enrolled in the independent cohort. Compared with the other three models, the AP model constructed by 36 arterial phase (AP) features showed good performance with a mean AUC of 0.905 in the 5-fold cross-validation and was easier to apply in the clinical setting. The decision curve and calibration curve confirmed the clinical utility of the model. In the independent cohort, patients with high Immunoscores showed significantly higher GEP prediction values than those with low Immunoscores (t=-2.359, p=0.029). The CEUS-based model is a reliable predictive tool for T cell-inflamed GEP in HCC, and might facilitate individualized immunotherapy decision-making.

Contrast-enhanced ultrasound liver imaging reporting and data system: clinical validation in a prospective multinational study in North America and Europe.

The objective of this study is to determine the diagnostic accuracy of the American College of Radiology Contrast-Enhanced Ultrasound (CEUS) Liver Imaging Reporting and Data System LR-5 characterization for HCC diagnosis in North American or European patients. A prospective multinational cohort study was performed from January 2018 through November 2022 at 11 academic and nonacademic centers in North America and Europe. Patients at risk for HCC with at least 1 liver observation not previously treated, identified on ultrasound (US), or multiphase CT or MRI performed as a part of standard clinical care were eligible for the study. All participants were examined with CEUS of the liver within 4 weeks of CT/MRI or tissue diagnosis to characterize up to 2 liver nodules per participant using ACR CEUS Liver Imaging Reporting and Data System. Definite HCC diagnosis on the initial CT/MRI, imaging follow-up, or histology for CT/MRI-indeterminate nodules were used as reference standards. A total of 545 nodules had confirmed reference standards in 480 patients, 73.8% were HCC, 5.5% were other malignancies, and 20.7% were nonmalignant. The specificity of CEUS LR-5 for HCC was 95.1% (95% CI 90.1%-97.7%), sensitivity 62.9% (95% CI 57.9%-67.7%), positive predictive value 97.3% (95% CI 94.5%-98.7%), and negative predictive value 47.7% (95% CI 41.7%-53.8%). In addition, benign CEUS characterization (LR-1 or LR-2) had 100% specificity and 100% positive predictive value for nonmalignant liver nodules. CEUS Liver Imaging Reporting and Data System provides an accurate categorization of liver nodules in participants at risk for HCC.

Contrast enhanced ultrasound combined with serology predicts hepatocellular carcinoma recurrence: a retrospective observation cohort study.

To construct a novel model based on contrast-enhanced ultrasound (CEUS) and serological biomarkers to predict the early recurrence (ER) of primary hepatocellular carcinoma within 2 years after hepatectomy. A total of 466 patients who underwent CEUS and curative resection between 2016.1.1 and 2019.1.1 were retrospectively recruited from one institution. The training and testing cohorts comprised 326 and 140 patients, respectively. Data on general characteristics, CEUS Liver Imaging Reporting and Data System (LI-RADS) parameters, and serological were collected. Univariate analysis and multivariate Cox proportional hazards regression model were used to evaluate the independent prognostic factors for tumor recurrence, and the Contrast-enhanced Ultrasound Serological (CEUSS) model was constructed. Different models were compared using prediction error and time-dependent area under the receiver operating characteristic curve (AUC). The CEUSS model's performances in ER prediction were assessed. The baseline data of the training and testing cohorts were equal. LI-RADS category, α-fetoprotein level, tumor maximum diameter, total bilirubin level, starting time, iso-time, and enhancement pattern were independent hazards, and their hazards ratios were 1.417, 1.309, 1.133, 1.036, 0.883, 0.985, and 0.70, respectively. The AUCs of CEUSS, BCLC,TNM, and CNLC were 0.706, 0.641, 0.647, and 0.636, respectively, in the training cohort and 0.680, 0.583, 0.607, and 0.597, respectively, in the testing cohort. The prediction errors of CEUSS, BCLC, TNM, and CNLC were 0.202, 0.205, 0.205, and 0.200, respectively, in the training cohort and 0.204, 0.221, 0.219, and 0.211, respectively, in the testing cohort. The CEUSS model can accurately and individually predict ER before surgery and may represent a new tool for individualized treatment.

Domain knowledge-guided adversarial adaptive fusion of hybrid breast ultrasound data

Contrast-enhanced ultrasound (CEUS), which provides more detailed microvascular information about the tumor, is always taken by radiologists in clinic diagnosis along with B-mode ultrasound (B-mode US). However, automatically analyzing breast CEUS is challenging due to the difference between the CEUS video and the natural video, e.g., sports or action videos, where the CEUS video has no positional displacements. Additionally, most existing methods rarely use the Time Intensity Curve (TIC) information of CEUS and non-imaging clinical (NIC) data. To address these issues, we propose a novel breast cancer diagnosis framework that learns the complementarity and correlation across hybrid modal data, including CEUS, B-mode US, and NIC data, by an adversarial adaptive fusion method. Furthermore, to fully exploit the CEUS information, the proposed method, inspired by the clinical processing of radiologists, first extracts the TIC parameters of CEUS. Then, we select a clip from CEUS using a frame screening strategy and finally get spatio-temporal features from these clips through a critical frame attention network. To our knowledge, this is the first AI system to use TIC parameters, NIC data, and ultrasound imaging in diagnoses. We have validated our method on a dataset collected from 554 patients. The experimental results demonstrate the excellent performance of the proposed method. The result shows that our method can achieve an accuracy of 87.73%, which is higher than that of uni-modal approaches by nearly 5%.

Surveillance for malignant progression of LI-RADS version 2017 category 3/4 nodules using contrast-enhanced ultrasound.

To identify the risk factors for predicting the malignant progression of LR-3/4 observations on the baseline and contrast-enhanced ultrasound (CEUS). In total, 245 liver nodules assigned to LR-3/4 in 192 patients from January 2010 to December 2016 were followed up by baseline US and CEUS. The differences in the rate and time of progression to hepatocellular carcinoma (HCC) among subcategories (defined as P1-P7) of LR-3/4 in CEUS Liver Imaging Reporting and Data System (LI-RADS) were analyzed. The risk factors to predict progression to HCC were analyzed by univariate and multivariate Cox proportional hazard model analysis. A total of 40.3% of LR-3 nodules and 78.9% of LR-4 nodules eventually progressed to HCC. The cumulative incidence of progression was significantly higher for LR-4 than LR-3 (p < 0.001). The rate of progression was 81.2% in nodules with arterial phase hyperenhancement (APHE), 64.7% in nodules with late and mild washout, and 100% in nodules with both characteristics. The overall progression rate and median progression time of subcategory P1 nodules (LR-3a) were lower (38.0% vs. 47.6-100.0%) and later (25.1months vs. 2.0-16.3months) than those of other subcategories. The cumulative incidence of progression of LR-3a (P1), LR-3b (P2/3/4), and LR-4 (P5/6/7) categories were 38.0%, 52.9%, and 78.9%. The risk factors of HCC progression were Visualization score B/C, CEUS characteristics (APHE, washout), LR-4 classification, echo changes, and definite growth. CEUS is a useful surveillance tool for nodules at risk of HCC. CEUS characteristics, LI-RADS classification, and changes in nodules provide useful information for the progress of LR-3/4 nodules. CEUS characteristics, LI-RADS classification, and nodule changes provide important predictions for LR-3/4 nodule progression to HCC, which may stratify the risk of malignant progression to provide a more optimized and refined, more cost-effective, and time-efficient management strategy for patients. • CEUS is a useful surveillance tool for nodules at risk of HCC, CEUS LI-RADS successfully stratified the risks that progress to HCC. • CEUS characteristics, LI-RADS classification, and changes in nodules can provide important information on the progression of LR-3/4 nodules, which may be helpful for a more optimized and refined management strategy.

Correlations between contrast-enhanced ultrasound and microvessel density in non-small cell lung cancer: A prospective study.

Immunohistochemical microvessel density (MVD) is an early indicator of angiogenesis and it could be used to evaluate the therapeutic efficacy of non-small cell lung cancer (NSCLC). We sought to identify the ability of contrast-enhanced ultrasound (CEUS) in evaluating MVD of subpleural NSCLC. We prospectively collected CEUS data of NSCLC confirmed by ultrasound-guided transthoracic needle biopsy from October 2019 to February 2021, The MVD of NSCLC counted by CD34-positive vessels of immunohistochemical staining. Microflow enhancement (MFE) of CEUS was divided into "dead wood", "cotton", and "vascular" patterns. Pathology subgroup and MVD between different MFE patterns were analyzed, respectively. The arrival time, time to peak, peak intensity (PI), and area under curve (AUC) derivefrom time-intensity curve of CEUS with MVD in NSCLC and its pathological subgroups (adenocarcinoma and squamous cell carcinoma) were subjected to correlation analysis. A total of 87 patients were included in this study, consisting of 53 cases of adenocarcinoma and 34 cases of squamous cell carcinoma with a mean MVD of 27.8 ± 12.2 mm-1. There was a significant statistical difference in MFE patterns between two pathological subgroups (p < 0.05). Besides, the MVD of "cotton" and "vascular" patterns were significantly higher than that of "dead wood" pattern (both of p < 0.05), whereas there was no significant difference in MVD between "cotton" pattern and "vascular" pattern. PI and AUC of CEUS were positively correlated with the MVD of NSCLC (r = 0.497, p < 0.001, and r = 0.367, p < 0.001, respectively). Besides, PI and AUC of CEUS were positively correlated with the MVD of squamous cell carcinoma (r = 0.802, and r = 0.663, respectively; both of p < 0.001). Only the PI was positively correlated with the MVD of lung adenocarcinoma (r = 0.288, p = 0.037). MFE patterns and quantitative parameters of CEUS had good correlation with MVD of NSCLC, especially in squamous cell carcinoma.

Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System in Hepatocellular Carcinoma ≤5 cm: Biological Characteristics and Patient Outcomes

Introduction: The present study aimed to evaluate the influence of biological characteristics of hepatocellular carcinoma (HCC) on the Liver Imaging Reporting and Data System (LI-RADS) v2017 category of contrast-enhanced ultrasound (CEUS) in patients with high risk and compare the outcomes among different categories after radical resection. Methods: Between June 2017 and December 2020, standardized CEUS data of liver nodules were prospectively collected from multiple centers across China. We conducted a retrospective analysis of the prospectively collected data on HCCs measuring no more than 5 cm, as diagnosed by pathology. LI-RADS categories were assigned after thorough evaluation of CEUS features. Then, CEUS LI-RADS categories and major features were compared in different differentiation, Ki-67, and microvascular invasion (MVI) statuses. Differences in recurrence-free survival (RFS) among different LI-RADS categories were further analyzed. Results: A total of 293 HCC nodules in 293 patients were included. This study revealed significant differences in the CEUS LI-RADS category of HCCs among differentiation (p < 0.001) and levels of Ki-67 (p = 0.01) and that poor differentiation (32.7% in LR-M, 12% in LR-5, and 6.2% in LR-4) (p < 0.001) and high level of Ki-67 (median value 30%) were more frequently classified into the LR-M category, whereas well differentiation (37.5% in LR-4, 15.1% in LR-5, and 11.5% in LR-M) and low levels of Ki-67 (median value 11%) were more frequently classified into the LR-4 category. No significant differences were found between MVI and CEUS LI-RADS categories (p > 0.05). With a median follow-up of 23 months, HCCs assigned to different CEUS LI-RADS classes showed no significant differences in RFS after resection. Conclusions: Biological characteristics of HCC, including differentiation and level of Ki-67 expression, could influence major features of CEUS and impact the CEUS LI-RADS category. HCCs in different CEUS LI-RADS categories showed no significant differences in RFS after resection.

Intra- and Inter-Observer Variability of Quantitative Parameters Used in Contrast-Enhanced Ultrasound of Kidneys of Healthy Cats.

Contrast-enhanced ultrasound (CEUS) is a non-invasive imaging technique which allows qualitative and quantitative assessment of tissue perfusion. Although CEUS offers numerous advantages, a major challenge remains the variability in tissue perfusion quantification. This study aimed to assess intra- and inter-observer variability for quantification of renal perfusion. Two observers with different levels of expertise performed a quantitative analysis of 36 renal CEUS studies, twice. The CEUS data were collected from 12 healthy cats at 3 different time points with a 7-day interval. The inter- and intra-observer agreement was assessed by the intraclass correlation coefficient. Within and between observers, a good agreement was demonstrated for intensity-related parameters in the cortex, medulla, and interlobular artery. For some parameters, ICCinter was considerably lower than ICCintra, mostly when the ROI encompassed the entire kidney or medulla. With the exception of time to peak (TTP) and mean transit time (mTTI), time-related and slope-related parameters showed poor agreement among observers. In conclusion, it may be advised against having the quantitative assessment of renal perfusion performed by different observers, especially if their experience levels differ. The cortical mTTI seemed to be the most appropriate parameter as it showed a favorable inter-observer agreement and inter-period agreement.

Diagnosis of hepatocellular carcinoma using deep network with multi-view enhanced patterns mined in contrast-enhanced ultrasound data

Hepatocellular carcinoma, representing the most frequent primary liver cancer, is a common cancer disease that is the fourth leading cause of cancer-related mortality worldwide. In comparison, non-hepatocellular carcinoma liver cancers often present different prognoses and require distinct management which makes the accurate discrimination between hepatocellular carcinoma and non-hepatocellular carcinoma malignant lesions in contrast-enhanced ultrasound data critical for precise intervention. However, different types of liver cancers have similar enhanced patterns against the perfusion stages that raise the difficulty in the classification of hepatocellular carcinoma with the other liver cancers, especially when the contrast-enhanced ultrasound data is collected from different imaging machines. To this end, this paper innovatively proposes to extract perfusion features from a multi-view learning procedure for obtaining the inherent distinguishing features among liver cancers, leading to a more precise deep model in differentiating the hepatocellular carcinoma from other malignant cases. In particular, the proposed network consists of two novel structures for learning the correlation information among the different views to enhance the robustness of the features and fuse them by reducing redundant information. The proposed method is verified on a multi-source dataset collected from 1241 participants and achieves an AUC value of 89% for classification performance. The experimental results demonstrate the effectiveness of the proposed method for the diagnosis of hepatocellular carcinoma with a multi-source contrast-enhanced ultrasound dataset and might provide an effective assistant for clinical radiologists in liver cancer differentiation.

Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis.

To develop and validate a deep learning (DL) model based on quantitative analysis of contrast-enhanced ultrasound (CEUS) images that predicts early recurrence (ER) after thermal ablation (TA) of colorectal cancer liver metastasis (CRLM). Between January 2010 and May 2019, a total of 207 consecutive patients with CRLM with 13,248 slice images at three dynamic phases who received CEUS within 2 weeks before TA were retrospectively enrolled in two centres (153 for the training cohort (TC), 32 for the internal test cohort (ITC), and 22 for the external test cohort (ETC)). Clinical and CEUS data were used to develop and validate the clinical model, DL model, and DL combining with clinical (DL-C) model to predict ER after TA. The performance of these models was compared by the receiver operating characteristic curve (ROC) with the DeLong test. After a median follow-up of 56 months, 49% (99/207) of patients experienced ER. Three key clinical features (preoperative chemotherapy (PC), lymph node metastasis of the primary colorectal cancer (LMPCC), and T stage) were used to develop the clinical model. The DL model yielded better performance than the clinical model in the ETC (AUC: 0.67 for the clinical model, 0.76 for the DL model). The DL-C model significantly outperformed the clinical model and DL model (AUC: 0.78 for the DL-C model in the ETC; both, p < 0.001). The model based on CEUS can achieve satisfactory prediction and assist physicians during the therapeutic decision-making process in clinical practice. • This is an exploratory study in which ablation-related contrast-enhanced ultrasound (CEUS) data from consecutive patients with colorectal cancer liver metastasis (CRLM) were collected simultaneously at multiple institutions. • The deep learning combining with clinical (DL-C) model provided desirable performance for the prediction of early recurrence (ER) after thermal ablation (TA). • The DL-C model based on CEUS provides guidance for TA indication selection and making therapeutic decisions.

Utility of high-frequency B-mode and contrast-enhanced ultrasound for the differential diagnosis of benign and malignant pleural diseases: a prospective study.

Pleural disease is a prevalent condition. As precision therapy advances, noninvasive imaging modalities play even more important roles in the evaluation of pleural diseases. This study investigated the diagnostic capabilities of high-frequency B-mode ultrasound (US) and contrast-enhanced US (CEUS) in terms of differentiating between benign and malignant pleural diseases. Patients with unexplained thickened pleurae were prospectively analyzed via transthoracic US. High-frequency B-mode US was used to derive the pleural thickness, morphology, and echogenicity. We analyzed the high-frequency CEUS data including the enhancement mode and time intensity curve (TIC). The cause of pleural thickening was confirmed by pleural biopsy and follow-up after the biopsy. We analyzed the differences in various ultrasonic features between the malignant and benign groups. Moreover, we plotted receiver operator curves (ROCs) and obtained the area under the curves, sensitivities, and specificities of all significant continuous variables. Multivariate logistic regression was used to assess the combined usefulness of multiple US indicators in terms of predicting malignant pleurae. Thirty malignant and twenty benign thickened pleurae were finally diagnosed via pleural biopsy and at least six months of follow-up. The pleural morphology and enhancement modes significantly differed between the two groups (all P<0.05). The thickness derived from B-mode US and CEUS were significantly thicker in the malignant group (both P<0.05). Arrival time (AT) and the time to peak (TTP) of TIC were significantly shorter in the malignant group, whereas peak intensity and the area under the TIC were significantly higher in the malignant group (all P<0.05). The area under the ROC for pleural thickness derived from B-mode US was 0.819; pleural thickness derived from CEUS was 0.848; AT was 0.804; TTP was 0.750; peak intensity was 0.745; the area under the TIC was 0.743; and the combined various B-mode and CEUS parameter was 0.975. Pleural thickness, morphology, enhancement mode, and the TIC of high-frequency US aided the differentiation of benign from malignant pleural diseases. Combined analysis of US indicators further improved the diagnostic capability.

Diagnostic value of CEUS LI-RADS and serum tumor markers for combined hepatocellular-cholangiocarcinoma

PurposeTo analyze the contrast-enhanced ultrasound (CEUS) manifestations of combined hepatocellular-cholangiocarcinoma (CHC) and to investigate the diagnostic value of the 2017 version of the CEUS Liver Imaging Reporting and Data System (LI-RADS) and serum tumor markers in CHC. MethodsA total of 66 pathologically confirmed CHC nodules were retrospectively analyzed. We summarized the CEUS manifestations of CHC and analyzed the relationship between serum tumor markers and the enhancement pattern of CHC. We also classified CHC according to CEUS LI-RADS criteria. The Kappa test was used to assess the interreader agreement of CEUS LI-RADS between radiologists. ResultAccording to the results, 52 of 62 (83.9%) patients had elevated alpha-fetoprotein (AFP), 19 of 61 (31.1%) had elevated carbohydrate antigen 199 (CA 199), and 13 of 61 (21.3%) had both elevated AFP and CA 199. Of the 66 CHC nodules, 64 (97.0%) were identified as malignant lesions by CEUS, 13 (19.7%) showed a hepatocellular carcinoma-like enhancement pattern, and 21 (31.8%) showed a cholangiocarcinoma-like enhancement pattern. For the CEUS LI-RADS categories, 39 of 53 (73.6%) CHC nodules were classified as LR-M, 12 (22.6%) were classified as LR-5, and 2 (3.8%) were classified as LR-4. The interreader agreement for the LI-RADS categories was 0.60. ConclusionsAlthough CHC lacks specific CEUS features, CEUS LI-RADS and serum tumor markers can be useful tools for reducing the misdiagnosis of CHC. In addition, due to the relative complexity of the CEUS features involved in CHC, it is necessary for beginning radiologists to learn more about CEUS features.

Qualitative and Quantitative Contrast-Enhanced Ultrasound Combined with Conventional Ultrasound for Predicting the Malignancy of Soft Tissue Tumors.

This study was aimed at evaluating the performance of perfusion patterns and the quantitative parameters of contrast-enhanced ultrasound (CEUS) in the detection of soft tissue tumors (STTs) and establishing a US workflow for STTs to improve patient management. Conventional ultrasound (US) and CEUS data were retrospectively collected from 156 soft tissue masses. Six perfusion patterns (P1-P6) were applied for CEUS qualitative analysis. Multivariate logistic regression was used to evaluate the performance of conventional US and qualitative and quantitative CEUS in distinguishing benign and malignant STTs. The malignancy rates of P1-P6 in STTs were 0%, 50.0%, 9.1%, 33.3%, 73.4% and 61.0%, respectively. For "non-P1" STTs, the predictive model combining quantitative CEUS parameters with conventional US features, including margin (odds ratio [OR]=4.490, p=0.000), vascular density (OR=2.307, p=0.013), 50% wash-out intensity (OR=1.904, p=0.032) and 50% wash-out time (OR=1.031, p=0.019), performed favorably in predicting malignancy, with an accuracy of 81.0% and an area under the receiver operating characteristic curve of 0.868. Furthermore, a US workflow for the detection of STTs based on conventional US and CEUS was established. CEUS with qualitative and quantitative analyses could be an effective tool for STT diagnosis. The US workflow in this study may improve the management of STT patients.

Role of Contrast-Enhanced Ultrasound in Pulmonary Lesions: 5-Year Experience at a Single Center.

Contrast-enhanced ultrasound (CEUS) for the differentiation of benign and malignant peripheral pulmonary lesions has been considered experimental for many years. This study was aimed at evaluating the feasibility of CEUS as a diagnostic modality in this area of discussion. CEUS diagnostic accuracy was explored by comparison with contrast-enhanced computed tomography (CECT). The collective included 449 patients with 449 definitive diagnoses (benign, 178; malignant, 271). Logistic regression analysis of CEUS data revealed that delayed time to enhancement, chaotic pattern of distribution of vessels and mild extent of enhancement were independent risk factors for predicting malignancy. Time to wash-out and homogeneity of enhancement did not differ between the two groups (p > 0.05). Based on histopathology or clinical follow-up as a reference standard, CEUS and CECT had similar diagnostic accuracies of 80.16% and 81.75%, respectively. CEUS is a potentially useful imaging tool for diagnosing peripheral pulmonary lesions.