Preoperative Contrast-enhanced Ultrasound Research Articles

Incorporation of pre- and intra-operative sentinel lymph node mapping techniques in dogs with apocrine gland anal sac adenocarcinoma

The high incidence of nodal metastasis, variable lymphatic drainage patterns, and prognostic significance of nodal metastasis in dogs with apocrine gland anal sac adenocarcinoma (AGASACA) highlight the need for development of standardized techniques for lymph node staging in canine AGASACA. The aim of this study was to develop and describe the utility of pre- and intra-operative sentinel lymph node (SLN) mapping techniques for subsequent nodal extirpation and histologic assessment in dogs with AGASACA. A prospective clinical trial was performed as a pilot study. Eight client-owned dogs with unilateral AGASACA were enrolled. Preoperative contrast-enhanced ultrasound (CEUS) via transrectal ultrasound (TRUS) and CT-lymphography (CTL) was followed by intraoperative SLN mapping within 7 days utilizing a visible dye (methylene blue [MB]) and a near-infrared (NIR) fluorescent dye (indocyanine green [ICG]) with subsequent nodal extirpation and routine anal sacculectomy. In all dogs, preoperative CTL and intraoperative SLN mapping identified at least one SLN. Pre- and intra-operative findings differed in 4/8 dogs. Preoperative CEUS identified a SLN in 7/8 dogs, but the exact location of the SLN was indeterminate. Extirpated lymph nodes were metastatic in 2/8 dogs. Preoperative CTL and intraoperative MB and ICG were an effective combination of SLN mapping techniques in dogs with AGASACA. These techniques each provided unique advantages. Combined, they identified and guided extirpation of early metastatic lymph nodes. Large-scale, prospective studies utilizing the techniques described are needed to accurately determine the incidence and significance of early/occult nodal metastasis in canine AGASACA.

Deep learning model based on contrast-enhanced ultrasound for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma.

To establish and validate a non-invasive deep learning (DL) model based on contrast-enhanced ultrasound (CEUS) to predict vessels encapsulating tumor clusters (VETC) patterns in hepatocellular carcinoma (HCC). This retrospective study included consecutive HCC patients with preoperative CEUS images and available tissue specimens. Patients were randomly allocated into the training and test cohorts. CEUS images were analyzed using the ResNet-18 convolutional neural network for the development and validation of the VETC predictive model. The predictive value for postoperative early recurrence (ER) of the proposed model was further evaluated. A total of 242 patients were enrolled finally, including 195 in the training cohort (54.6 ± 11.2 years, 178 males) and 47 in the test cohort (55.1 ± 10.6 years, 40 males). The DL model (DL signature) achieved favorable performance in both the training cohort (area under the receiver operating characteristics curve [AUC]: 0.92, 95% confidence interval [CI]: 0.88-0.96) and test cohort (AUC: 0.90, 95% CI: 0.82-0.99). The stratified analysis demonstrated good discrimination of DL signature regardless of tumor size. Moreover, the DL signature was found independently correlated with postoperative ER (hazard ratio [HR]: 1.99, 95% CI: 1.29-3.06, p = 0.002). C-indexes of 0.70 and 0.73 were achieved when the DL signature was used to predict ER independently and combined with clinical features. The proposed DL signature provides a non-invasive and practical method for VETC-HCC prediction, and contributes to the identification of patients with high risk of postoperative ER. This DL model based on contrast-enhanced US displayed an important role in non-invasive diagnosis and prognostication for patients with VETC-HCC, which was helpful in individualized management. Preoperative biopsy to determine VETC status in HCC patients is limited. The contrast-enhanced DL model provides a non-invasive tool for the prediction of VETC-HCC. The proposed deep-learning signature assisted in identifying patients with a high risk of postoperative ER.

Evaluation of contrast-enhanced ultrasound for predicting tumor grade in small (≤4 cm) clear cell renal cell carcinoma: Qualitative and quantitative analysis.

The study aimed to evaluate the utility of qualitative and quantitative analysis employing contrast-enhanced ultrasound (CEUS) in predicting the WHO/ISUP grade of small (≤4 cm) clear cell renal cell carcinoma (ccRCCs). Patients with small ccRCCs, confirmed by histological examination, underwent preoperative CEUS and were classified into low- (grade I/II) and high-grade (grade III/IV) groups. Qualitative and quantitative assessments of CEUS were conducted and compared between the two groups. Diagnostic performance was assessed using receiver operating characteristic curves. A total of 72 patients were diagnosed with small ccRCCs, comprising 23 individuals in the high-grade group and 49 in the low-grade group. The low-grade group exhibited a significantly greater percentage of hyper-enhancement compared to the high-grade group (79.6% VS 39.1%, P < 0.05). The low-grade group showed significantly higher relative index values for peak enhancement, wash-in area under the curve, wash-in rate, wash-in perfusion index, and wash-out rate compared to the high-grade group (all P < 0.05). The AUC values for qualitative and quantitative parameters in predicting the WHO/ISUP grade of small ccRCCs ranged from 0.676 to 0.756. Both qualitative and quantitative CEUS analysis could help to distinguish the high- from low-grade small ccRCCs.

Whether the use of intravenous contrast-enhanced ultrasound can improve the accuracy of T stage diagnosis of gastric cancer?

To explore whether the use of intravenous contrast-enhanced ultrasound (CEUS) can improve the diagnostic accuracy of T stage of gastric cancer on the basis of oral contrast-enhanced ultrasound (OCEUS). Eighty-one patients with gastric cancer who underwent preoperative CEUS. Criteria for T staging was AJCC Stage 8th Edition. To compare the diagnostic accuracy, sensitivity and specificity of OCEUS and CEUS for T staging of gastric cancer using pathological results as the gold standard. There were differences in the diagnostic accuracy of T2 (P = 0.048), T3(P = 0.002) and T4 (P = 0.000) between OCEUS and CEUS. And there was no difference in diagnostic accuracy for T1 (P = 0.118). CEUS significantly improved diagnostic sensitivity and specificity in T3 (42.86% to 61.90% for sensitivity, 86.96% to 93.75% for specificity), and T4 (64.71% to 76.47% for sensitivity, 78.33% to 95.74% for specificity). Compared with OCEUS, CEUS did improve the diagnostic accuracy of T2, T3, and T4 stages of gastric cancer.

Predicting Ki-67 expression in hepatocellular carcinoma: nomogram based on clinical factors and contrast-enhanced ultrasound radiomics signatures.

To develop a contrast-enhanced ultrasound (CEUS) clinic-radiomics nomogram for individualized assessment of Ki-67 expression in hepatocellular carcinoma (HCC). A retrospective cohort comprising 310 HCC individuals who underwent preoperative CEUS (using SonoVue) at three different centers was partitioned into a training set, a validation set, and an external test set. Radiomics signatures indicating the phenotypes of the Ki-67 were extracted from multiphase CEUS images. The radiomics score (Rad-score) was calculated accordingly after feature selection and the radiomics model was constructed. A clinic-radiomics nomogram was established utilizing multiphase CEUS Rad-score and clinical risk factors. A clinical model only incorporated clinical factors was also developed for comparison. Regarding clinical utility, calibration, and discrimination, the predictive efficiency of the clinic-radiomics nomogram was evaluated. Seven radiomics signatures from multiphase CEUS images were selected to calculate the Rad-score. The clinic-radiomics nomogram, comprising the Rad-score and clinical risk factors, indicated a good calibration and demonstrated a better discriminatory capacity compared to the clinical model (AUCs: 0.870 vs 0.797, 0.872 vs 0.755, 0.856 vs 0.749 in the training, validation, and external test set, respectively) and the radiomics model (AUCs: 0.870 vs 0.752, 0.872 vs 0.733, 0.856 vs 0.729 in the training, validation, and external test set, respectively). Furthermore, both the clinical impact curve and the decision curve analysis displayed good clinical application of the nomogram. The clinic-radiomics nomogram constructed from multiphase CEUS images and clinical risk parameters can distinguish Ki-67 expression in HCC patients and offer useful insights to guide subsequent personalized treatment.

A modified CEUS risk stratification model for adnexal masses with solid components: prospective multicenter study and risk adjustment.

To evaluate the additional advantages of integrating contrast-enhanced ultrasound (CEUS) into the Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound (US) for the characterization of adnexal lesions with solid components. This prospective multicenter study recruited women suspected of having adnexal lesions with solid components between September 2021 and December 2022. All patients scheduled for surgery underwent preoperative CEUS and US examinations. The lesions were categorized according to the O-RADS US system, and quantitative CEUS indexes were recorded. Pathological results served as the reference standard. Univariable and multivariable analyses were performed to identify risk factors for malignancy in adnexal lesions with solid components. Receiver operating characteristic (ROC) curve analysis was employed to assess diagnostic performance. A total of 180 lesions in 175 women were included in the study. Among these masses, 80 were malignant and 100 were benign. Multivariable analysis revealed that serum CA-125, the presence of acoustic shadowing, and peak intensity (PI) ratio (PImass/PIuterus) of solid components on CEUS were independently associated with adnexal malignancy. The modified CEUS risk stratification model demonstrated superior diagnostic value in assessing adnexal lesions with solid components compared to O-RADS US (AUC: 0.91 vs 0.78, p < 0.001) and exhibited comparable performance to the Assessment of Different NEoplasias in the adnexa (ADNEX) model (AUC 0.91 vs 0.86, p = 0.07). Our findings underscore the potential value of CEUS as an adjunctive tool for enhancing the precision of diagnostic evaluations of O-RADS US. The promising performance of the modified CEUS risk stratification model suggests its potential to mitigate unnecessary surgeries in the characterization of adnexal lesions with solid components. • The additional value of CEUS to O-RADS US in distinguishing between benign and malignant adnexal lesions with solid components requires further evaluation. • The modified CEUS risk stratification model displayed superior diagnostic value and specificity in characterizing adnexal lesions with solid components when compared to O-RADS US. • The inclusion of CEUS demonstrated potential in reducing the need for unnecessary surgeries in the characterization of adnexal lesions with solid components.

Comparison contrast-enhanced CT with contrast-enhanced US in diagnosing combined hepatocellular-cholangiocarcinoma: a propensity score-matched study

ObjectivesTo develop and compare noninvasive models for differentiating between combined hepatocellular-cholangiocarcinoma (cHCC-CCA) and HCC based on serum tumor markers, contrast-enhanced ultrasound (CEUS), and computed tomography (CECT).MethodsFrom January 2010 to December 2021, patients with pathologically confirmed cHCC-CCA or HCC who underwent both preoperative CEUS and CECT were retrospectively enrolled. Propensity scores were calculated to match cHCC-CCA and HCC patients with a near-neighbor ratio of 1:2. Two predicted models, a CEUS-predominant (CEUS features plus tumor markers) and a CECT-predominant model (CECT features plus tumor markers), were constructed using logistic regression analyses. Model performance was evaluated by the area under the curve (AUC), sensitivity, specificity, and accuracy.ResultsA total of 135 patients (mean age, 51.3 years ± 10.9; 122 men) with 135 tumors (45 cHCC-CCA and 90 HCC) were included. By logistic regression analysis, unclear boundary in the intratumoral nonenhanced area, partial washout on CEUS, CA 19-9 > 100 U/mL, lack of cirrhosis, incomplete tumor capsule, and nonrim arterial phase hyperenhancement (APHE) volume < 50% on CECT were independent factors for a diagnosis of cHCC-CCA. The CECT-predominant model showed almost perfect sensitivity for cHCC-CCA, unlike the CEUS-predominant model (93.3% vs. 55.6%, p < 0.001). The CEUS-predominant model showed higher diagnostic specificity than the CECT-predominant model (80.0% vs. 63.3%; p = 0.020), especially in the ≤ 5 cm subgroup (92.0% vs. 70.0%; p = 0.013).ConclusionsThe CECT-predominant model provides higher diagnostic sensitivity than the CEUS-predominant model for CHCC-CCA. Combining CECT features with serum CA 19-9 > 100 U/mL shows excellent sensitivity.Critical relevance statementCombining lack of cirrhosis, incomplete tumor capsule, and nonrim arterial phase hyperenhancement (APHE) volume < 50% on CECT with serum CA 19-9 > 100 U/mL shows excellent sensitivity in differentiating cHCC-CCA from HCC.Key points1. Accurate differentiation between cHCC-CCA and HCC is essential for treatment decisions.2. The CECT-predominant model provides higher accuracy than the CEUS-predominant model for CHCC-CCA.3. Combining CECT features and CA 19-9 levels shows a sensitivity of 93.3% in diagnosing cHCC-CCA.Graphical

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.

Clinical value of contrast-enhanced ultrasound quantitative analysis for differentiating thyroid lesions in Hashimoto's thyroiditis patients.

The role of quantitative contrast-enhanced ultrasound (CEUS) in the evaluation of thyroid nodules with Hashimoto's thyroiditis (HT) has received little attention. This was a retrospective cohort study. We consecutively enrolled 242 patients (49 males, 193 females, average age 52 years) with a combined total of 248 thyroid nodules coexisting with HT who underwent biopsy/resection-proven pathology from December 2016 to June 2021. All patients underwent preoperative ultrasound (US) and CEUS examinations performed by 2 radiologists independently. Quantitative analysis of CEUS using time-intensity curves (TIC) was measured by an expert radiologist from the thyroid intra-nodule and the surrounding parenchyma and their ratios. Receiver operating characteristic (ROC) analysis was performed to evaluate their diagnostic performance. The patients were divided into the nodular HT (NHT) group (n=42), the papillary thyroid carcinoma (PTC) group (n=154), and the primary thyroid lymphoma (PTL) group (n=52) according to their pathological results. TIC parameters revealed that PTC and PTL showed faster time to peak (TTP) (P=0.044, P=0.049), lower peak intensity (PI) (both P<0.001), and smaller areas under the curve (both P<0.001) than those of NHT. The intra nodule of PTL showed an obviously slower perfusion (ratio =0.90, P<0.001) and lower PI (ratio =0.84, P<0.001) compared with the thyroid parenchyma. TIC improved performance in distinguishing PTL from NHT [area under the curve (AUC): 0.947, 95% confidence interval (CI): 0.903-0.991], but inferior performance in differentiating PTC and NHT (AUC: 0.838, 95% CI: 0.759-0.917). CEUS quantitative analysis could be valuable in differentiating thyroid malignancies in patients with HT.

Score based on contrast-enhanced ultrasound predict central lymph node metastasis in papillary thyroid cancer.

To investigate the association between contrast-enhanced ultrasound (CEUS) features of PTC and central lymph node metastasis (CLNM) and to develop a predictive model for the preoperative identification of CLNM. This retrospective study evaluated 750 consecutive patients with PTC from August 2020 to April 2023. Conventional ultrasound and qualitative CEUS features were analyzed for the PTC with or without CLNM using univariate and multivariate logistic regression analysis. A nomogram integrating the predictors was constructed to identify CLNM in PTC. The predictive nomogram was validated using a validation cohort. A total of 684 patients were enrolled. The 495 patients in training cohort were divided into two groups according to whether they had CLNM (pCLNM, n= 191) or not (nCLNM, n= 304). There were significant differences in terms of tumor size, shape, echogenic foci, enhancement direction, peak intensity, and score based on CEUS TI-RADS between the two groups. Independent predictive US features included irregular shape, larger tumor size (≥ 1.0cm), and score. Nomogram integrating these predictive features showed good discrimination and calibration in both training and validation cohort with an AUC of 0.72 (95% CI: 0.68, 0.77) and 0.79 (95% CI: 0.72, 0.85), respectively. In the subgroup with larger tumor size, age ≤ 35 years, irregular shape, and score > 6 were independent risk factors for CLNM. The score based on preoperative CEUS features of PTC may help to identify CLNM. The nomogram developed in this study provides a convenient and effective tool for clinicians to determine an optimal treatment regimen for patients with PTC.

Preoperative Contrast-Enhanced Ultrasound Predicts Microvascular Invasion in Hepatocellular Carcinoma as Accurately as Contrast-Enhanced MR.

Both contrast-enhanced ultrasound (CEUS) and contrast-enhanced magnetic resonance (CEMR) are important imaging methods for hepatocellular carcinoma (HCC). This study aimed to establish a model using preoperative CEUS parameters to predict microvascular invasion (MVI) in HCC, and compare its predictive efficiency with that of CEMR model. A total of 93 patients with HCC (39 cases in MVI positive group and 54 cases in MVI negative group) who underwent surgery in our hospital from January 2020 to June 2021 were retrospectively analyzed. Their clinical and imaging data were collected to establish CEUS and CEMR models for predicting MVI. The predictive efficiencies of both models were compared. By the univariate and multivariate regression analyses of patients' clinical information, preoperative CEUS static and dynamic images, we found that serrated edge and time to peak were independent predictors of MVI. The CEUS prediction model achieved a sensitivity of 92.3%, a specificity of 83.3%, and an accuracy of 84.6% (Az: 0.934). By analyzing the clinical and CEMR information, we found that tumor morphology, fast-in and fast-out, peritumoral enhancement, and capsule were independent predictors of MVI. The CEMR prediction model achieved a sensitivity of 97.4%, a specificity of 77.8%, and an accuracy of 83.2% (Az: 0.900). The combination of the two models achieved a sensitivity of 84.6%, a specificity of 87.0%, and an accuracy of 86.2% (Az: 0.884). There was no significant statistical difference in the areas under the ROC curve of the three models. The CEUS model and the CEMR model have similar predictive efficiencies for MVI of HCC. CEUS is also an effective method to predict MVI before operation.

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.

Relationships between CD34-, CD105- and bcl-2-Expression Levels and Contrast-Enhanced Ultrasound-Based Differential Diagnosis of Adnexal Tumours

The relationships between CEUS parameters of adnexal tumours and postoperative immunohistochemical assessments of CD34, CD105 and bcl-2 were analysed. This study aimed to investigate whether contrast-enhanced ultrasonography (CEUS) parameters depend on the microvascular density of the tumour lesion found after surgery. Fifty-one patients with a diagnosis of adnexal tumours were included in this single-centre, prospective study. Participants underwent preoperative CEUS (contrast-enhanced ultrasound). Colour Doppler enhancement characterisation parameters (Ystart, Ymax and S) were determined. Immunohistochemical examination of histological specimens of the adnexal lesions was then carried out to determine the expression levels of the CD34, CD105 and bcl-2 proteins. Relationships between the aforementioned parameters were investigated. No significant statistical correlations were observed between CD34, CD105 and bcl2 expression levels and CEUS parameters, independently of whether the operated lesion was malignant or benign. Transvaginal CEUS is diagnostic for the detection of pathological neoplastic vascularisation of an adnexal lesion independent of the density of microcapillaries found postoperatively.

Quantitative evaluation of colorectal tumour vasculature using contrast-enhanced ultrasound: Correlation with angiogenesis and prognostic significance.

Ultrasound is a vital tool for the diagnosis and management of colorectal cancer (CRC). Contrast-enhanced ultrasound (CEUS) is a non-invasive, safe, and cost-effective method for evaluating tumour blood vessels, that play a crucial role in tumour growth and progression. To explore CEUS's role in the quantitative evaluation of CRC blood vessels and their correlation with angiogenesis markers and prognosis. This study prospectively enrolled 100 patients with CRC confirmed by histopathology. All patients received preoperative CEUS examinations. Quantitative parameters, such as peak intensity (PI), time to peak (TTP), and area under the curve (AUC), were derived from time-intensity curve (TIC) analysis. Tumour tissue samples were obtained during surgery and examined immunohistochemically to assess the expression of angiogenesis markers, including vascular endothelial growth factor (VEGF) and microvessel density (MVD). The correlation between CEUS parameters, angiogenesis markers, and clinicopathological features was evaluated using appropriate statistical tests. Quantitative CEUS parameters (PI, TTP, and AUC) showed significant correlations with VEGF expression (P < 0.001) and MVD (P < 0.001), indicating a strong link between tumour blood vessels and angiogenesis. Increased PI, reduced TTP, and expanded AUC values were significantly related to higher tumour stage (P < 0.001), lymph node metastasis (P < 0.001), and distant metastasis (P < 0.001). Furthermore, these parameters were recognized as independent predictors of overall survival and disease-free survival in multivariate analysis (P < 0.001). CEUS has a high potential in guiding treatment planning and predicting patient outcomes. However, more comprehensive, multicentre studies are required to validate the clinical utility of CEUS in CRC management.

Neovascularization From the Carotid Artery Lumen Into the Carotid Plaque Confirmed by Contrast-Enhanced Ultrasound and Histology.

This study was aimed at assessing intraplaque neovessels, focusing on neovascularization from the vascular luminal side using contrast-enhanced ultrasound (CEUS) and determining that this contrast effect indicates that the neovessel is connected to the vessel lumen histopathologically. Whether plaque vulnerability can be assessed more accurately was also investigated. We enrolled consecutive patients with internal carotid artery stenosis who underwent carotid endarterectomy (CEA) and pre-operative CEUS with perflubutane of the carotid arteries. We graded the contrast effect semi-quantitatively from the vascular luminal and adventitial sides. We compared the contrast effect with the pathological findings, especially the neovascularization of the CEA specimens. In total, 68 carotid arterial atheromatous plaques (47 symptomatic) were analyzed. Symptomatic plaques were significantly correlated with stronger contrast effects from the luminal side than from the adventitial side (p=0.0095). Microbubbles from the luminal side appeared to flow mainly into the plaque shoulder. The contrast effect value for the plaque shoulder and neovessel density were significantly correlated (ρ=0.35, p=0.031). Neovessel density was significantly higher in symptomatic than in asymptomatic plaques (56.2 ± 43.7/mm2 and 18.1 ± 15.2/mm2, respectively, p < 0.0001). Serial histological sections of CEA specimens in a symptomatic plaque with a strong contrast effect from the luminal side revealed multiple neovessels fenestrated to the vessel lumen with endothelial cells, consistent with the CEUS findings. Contrast-enhanced ultrasound can be used to evaluate neovessels originating from the luminal side, histopathologically confirmed in serial sections. Symptomatic vulnerable plaque is correlated more significantly with intraplaque neovascularization from the luminal side than with neovascularization from the adventitia.

Prognostic and pathological implications of contrast-enhanced ultrasound features in hepatocellular carcinoma.

Contrast-enhanced ultrasound (CEUS) plays a vital role in diagnosing hepatocellular carcinoma (HCC) and, to some extent, reflects tumor prognosis. This suggests that some pathological features of HCC may be associated with CEUS features. This study aimed to verify the prognostic significance of four CEUS features and further explore their pathological significance. This study included 243 HCC patients who underwent a preoperative CEUS examination. All pathological diagnoses and immunohistochemical information were obtained from the pathological report. The prognostic significance of four CEUS features, including nodule-in-nodule architecture, mosaic architecture, intratumoral feeding arteries, and peritumoral arterial phase (AP) hyperenhancement, was analyzed. The correlation between prognostic-related features and immunohistochemical information was further analyzed. The disease-free survival (DFS) of HCC was significantly affected by mosaic architecture or intratumoral feeding arteries (HR = 1.79; 95% confidence interval (95% CI), 1.09-2.95; P = 0.004; HR = 1.70; 95% CI, 1.07-2.71; P = 0.025, respectively). Intratumoral feeding arteries were positively correlated with the expression of serum alpha-fetoprotein (AFP), microvascular invasion (MVI), differentiation, size, and Ki-67, among which the correlation with size was the strongest, followed by Ki-67 and MVI. The mosaic architecture was positively correlated with serum AFP, MVI, differentiation, and size, among which the correlation with size was strongest, followed by MVI. The mosaic architecture and intratumoral feeding arteries of CEUS were closely related to the postoperative progression of HCC. Mosaic architecture had a good correlation with tumor size and MVI, whereas intratumoral feeding arteries were closely associated with tumor size and Ki-67 expression.

Combining primary tumor features derived from conventional and contrast-enhanced ultrasound facilitates the prediction of positive axillary lymph nodes in Breast Imaging Reporting and Data System category 4 malignant breast lesions.

To determine whether the primary tumor features derived from conventional ultrasound (US) and contrast-enhanced US (CEUS) facilitate the prediction of positive axillary lymph nodes (ALNs) in breast cancer diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 4. A total of 240 women with breast cancer who underwent preoperative conventional US, strain elastography, and CEUS between September 2016 and December 2019 were included. The multiple parameters of the primary tumor were obtained, and univariate and multivariate analyses were performed to predict positive ALNs. Then three prediction models (conventional US features, CEUS features, and the combined features) were developed, and the diagnostic performance was evaluated with receiver operating characteristic curves. On conventional US, the traits of large size and the non-circumscribed margin of the primary tumor were marked as two independent predictors. On CEUS, the features of vessel perforation or distortion and the enhanced range of the primary tumor were marked as two independent predictors for positive ALNs. Three prediction models were then developed: model A (conventional US features), model B (CEUS features), and model C (model A plus B). Model C yielded the highest area under the curve (AUC) of 0.82 [95% confidence interval (CI), 0.75-0.88] compared with model A (AUC 0.74; 95% CI, 0.68-0.81; P = 0.008) and model B (AUC 0.72; 95% CI, 0.65-0.80; P < 0.001) as per the DeLong test. CEUS, as a non-invasive examination technique, can be used to predict ALN metastasis. Combining conventional US and CEUS may produce favorable predictive accuracy for positive ALNs in BI-RADS category 4 breast cancer.

A nomogram based on combining clinical features and contrast enhanced ultrasound is not able to identify Her-2 over-expressing cancer from other breast cancers.

The aim of this study was to evaluate whether a predictive model based on a contrast enhanced ultrasound (CEUS)-based nomogram and clinical features (Clin) could differentiate Her-2-overexpressing breast cancers from other breast cancers. A total of 152 pathology-proven breast cancers including 55 Her-2-overexpressing cancers and 97 other cancers from two units that underwent preoperative CEUS examination, were included and divided into training (n = 102) and validation cohorts (n = 50). Multivariate regression analysis was utilized to identify independent indicators for developing predictive nomogram models. The area under the receiver operating characteristic (AUC) curve was also calculated to establish the diagnostic performance of different predictive models. The corresponding sensitivities and specificities of different models at the cutoff nomogram value were compared. In the training cohort, 7 clinical features (menstruation, larger tumor size, higher CA153 level, BMI, diastolic pressure, heart rate and outer upper quarter (OUQ)) + enlargement in CEUS with P < 0.2 according to the univariate analysis were submitted to the multivariate analysis. By incorporating clinical information and enlargement on the CEUS pattern, independently significant indicators for Her-2-overexpression were used for further predictive modeling as follows: Model I, nomogram model based on clinical features (Clin); Model II, nomogram model combining enlargement (Clin + Enlargement); Model III, nomogram model based on typical clinical features combining enlargement (MC + BMI + diastolic pressure (DP) + outer upper quarter (OUQ) + Enlargement). Model II achieved an AUC value of 0.776 at nomogram cutoff score value of 190, which was higher than that of the other models in the training cohort without significant differences (all P>0.05). In the test cohort, the diagnostic efficiency of predictive model was poor (all AUC<0.6). In addition, the sensitivity and specificity were not significantly different between Models I and II (all P>0.05), in either the training or the test cohort. In addition, Clin exhibited an AUC similar to that of model III (P=0.12). Moreover, model III exhibited a higher sensitivity (70.0%) than the other models with similar AUC and specificity, only in the test cohort. The main finding of the study was that the predictive model based on a CEUS-based nomogram and clinical features could not differentiate Her-2-overexpressing breast cancers from other breast cancers.

Value of CEUS features in diagnosing thyroid nodules with halo sign on B-mode ultrasound

BackgroundThe results of halo sign in the differential diagnosis of thyroid nodules were conflicting, and the value of contrast-enhanced ultrasound (CEUS) in characterization of thyroid nodules with halo has not been fully evaluated. This study was therefore designed to investigate the value of contrast-enhanced ultrasound features in the differential diagnosis of thyroid nodules with halo sign on B-mode ultrasound.Material and methodsSeventy-four consecutive thyroid nodules with halo sign on B-mode ultrasound were pathologically confirmed by surgery or fine needle aspiration, including 43 benign and 31 malignant lesions. All these lesions underwent pre-operative CEUS examination. The CEUS features, including enhanced time, enhanced intensity and homogeneity, and presence of enhancing ring, were compared between benign and malignant ones.ResultsEnhanced intensity was significant different between benign and malignant lesions with halo. Hypo-enhancement was more frequently detected in malignant nodules than that in benign ones, compared with iso-enhancement and hyper-enhancement (p = 0.013, and = 0.014, respectively). Detection rate of high-enhancing ring was significantly higher in benign nodules than that in malignant group (p = 0.001). While in nodules > 10 mm, only high-enhancing ring was the distinguishing feature between benign and malignant nodules.ConclusionsEnhanced intensity and high-enhancing ring may be helpful in the differential diagnosis of thyroid nodules with halo sign on B-mode ultrasound.

Upper gastrointestinal tract perforation assessed by contrast-enhanced ultrasonography after oral Sonazoid administration in a stomach leakage mouse model.

The purpose of this study was to elucidate whether contrast-enhanced ultrasonography (CEUS) can visualize orally administered Sonazoid leaking into the peritoneal cavity in a postoperative stomach leakage mouse model. Adult female mice (n=33, 9-10 weeks old) were used. Preoperative CEUS was performed after delivering Sonazoid via intraperitoneal injection and the per oral route. A gastric leakage model was then generated by making a surgical incision of about 0.5 cm at the stomach wall, and CEUS with per oral Sonazoid administration was performed. A region of interest was drawn on the CEUS images and the signal intensity was quantitatively measured. Statistical analysis was performed using a mixed model to compare the signal intensity sampled from the pre-contrast images with those of the post-contrast images obtained at different time points. CEUS after Sonazoid intraperitoneal injection in normal mice and after oral administration in mice with gastric perforation visualized the contrast medium spreading within the liver interlobar fissures continuous to the peritoneal cavity. A quantitative analysis showed that in the mice with gastric perforation, the orally delivered Sonazoid leaking into the peritoneal cavity induced a statistically significant (P<0.05) increase in signal intensity in all CEUS images obtained 10 seconds or longer after contrast delivery. However, enhancement was not observed before gastric perforation surgery (P=0.167). CEUS with oral Sonazoid administration efficiently visualized the contrast medium spreading within the peritoneal cavity in a postoperative stomach leakage mouse model.