Contrast-enhanced Ultrasound Imaging Research Articles

A new agent for contrast-enhanced intravascular ultrasound imaging in vitro: polybutylcyanoacrylate nanoparticles with drug-carrying capacity

This study prepared and evaluated polymeric polybutylcyanoacrylate (PBCA) nanoparticles (NPs) that can be used as a new agent for contrast-enhanced intravascular ultrasound (IVUS) imaging with drug delivery capacity. The nanoformulation was successfully developed using suspension polymerisation and characterised in terms of size, size distribution, zeta potential, morphology, stability, toxicity effects, imaging effects and drug release study. The results showed that the nanoparticles were round and hollow, with a particle diameter of 215.8 ± 25.3 nm and a zeta potential of −22.2 ± 4.1 mV. In vitro experiments, the nanoparticles were safe, non-toxic, and stable in nature with the capacity to carry and release drug (ant-miR-126). Moreover, the nanoparticles can match the high-frequency probe of commercially IVUS as a contrast agent to improve the resolution of imaging (the mean echo intensity ratio in the vascular wall increased significantly from 10.89 ± 1.10 at baseline, to 24.51 ± 1.91 during injection and 43.70 ± 0.88 after injection, respectively p < .0001). Overall, a new nano agent with drug-carrying capacity was prepared, which can be used in combination with IVUS for simultaneous diagnosis and targeted therapy of coronary atherosclerosis.

Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames.

To apply machine learning to extract radiomics features from thyroid two-dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasound (CEUS) images to classify and predict benign and malignant thyroid nodules, classified according to the Chinese version of the thyroid imaging reporting and data system (C-TIRADS) as category 4. This retrospective study included 313 pathologically diagnosed thyroid nodules (203 malignant and 110 benign). Two 2D-US images and five CEUS key frames ("2nd second after the arrival time" frame, "time to peak" frame, "2nd second after peak" frame, "first-flash" frame, and "second-flash" frame) were selected to manually label the region of interest using the "Labelme" tool. A total of 7 images of each nodule and their annotates were imported into the Darwin Research Platform for radiomics analysis. The datasets were randomly split into training and test cohorts in a 9:1 ratio. Six classifiers, namely, support vector machine, logistic regression, decision tree, random forest (RF), gradient boosting decision tree and extreme gradient boosting, were used to construct and test the models. Performance was evaluated using a receiver operating characteristic curve analysis. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), and F1-score were calculated. One junior radiologist and one senior radiologist reviewed the 2D-US image and CEUS videos of each nodule and made a diagnosis. We then compared their AUC and ACC with those of our best model. The AUC of the diagnosis of US, CEUS and US combined CEUS by junior radiologist and senior radiologist were 0.755, 0.750, 0.784, 0.800, 0.873, 0.890, respectively. The RF classifier performed better than the other five, with an AUC of 1 for the training cohort and 0.94 (95% confidence interval 0.88-1) for the test cohort. The sensitivity, specificity, accuracy, PPV, NPV, and F1-score of the RF model in the test cohort were 0.82, 0.93, 0.90, 0.85, 0.92, and 0.84, respectively. The RF model with 2D-US combined with CEUS key frames achieved equivalent performance as the senior radiologist (AUC: 0.94 vs. 0.92, P = 0.798; ACC: 0.90 vs. 0.92) and outperformed the junior radiologist (AUC: 0.94 vs. 0.80, P = 0.039, ACC: 0.90 vs. 0.81) in the test cohort. Our model, based on 2D-US and CEUS key frames radiomics features, had good diagnostic efficacy for thyroid nodules, which are classified as C-TIRADS 4. It shows promising potential in assisting less experienced junior radiologists.

The efficacy of modified contrast-enhanced ultrasound Liver Imaging Reporting and Data System (CEUS LI-RADS) using Sonazoid in diagnosis of hepatocellular carcinoma: a systematic review and meta-analysis.

The contrast-enhanced ultrasound Liver Imaging Reporting and Data System (CEUS LI-RADS) is an algorithm for the diagnosis of hepatocellular carcinoma (HCC) in high-risk populations. Previous studies have shown the algorithm to have high specificity and moderate sensitivity. Nevertheless, it is designated for utilization solely with blood pool contrast agents. Sonazoid, a contrast agent that combines blood pools and Kupffer cells properties, has recently gained approval for marketing in an increased number of countries. Enhanced sensitivity in diagnosing HCC may be achieved through the distinctive Kupffer phase (KP) exhibited by Sonazoid. Certain academics have suggested the modified CEUS LI-RADS using Sonazoid. The main criteria of mild and late (≥60 seconds) washout in CEUS LI-RADS LR-5 were replaced by KP (>10 minutes) defects as the primary criteria. The purpose of this research was to evaluate the effectiveness of the modified CEUS LI-RADS using Sonazoid in diagnosing HCC. Original studies on Sonazoid and CEUS LI-RADS were searched in the PubMed, Embase, Cochrane Library, and Web of Science databases until 13 July 2023, with no restrictions on language. We enrolled studies that applied Sonazoid for CEUS in patients at high risk of HCC and modified CEUS LI-RADS for the diagnosis of intrahepatic nodules. Meta-analyses, evaluations, case studies, correspondences, remarks, and summaries of conferences were excluded. Additionally, studies that fell outside the scope of this study and contained data on the same patients were also excluded. We evaluated the quality of research by employing the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. A bivariate mixed effects model was utilized to conduct a meta-analysis, summarizing the sensitivity and specificity in the diagnosis of HCC. The investigation of potential factors contributing to study heterogeneity was conducted using meta-regression analysis. Out of the 103 studies screened, 6 studies (835 lesions) were included in the final results. Modified CEUS LR-5 exhibited a sensitivity of 0.77 [95% confidence interval (CI): 0.70-0.82; I2=71.98%; P=0.00] and a specificity of 0.88 (95% CI: 0.83-0.92; I2=0.00; P=0.47) for HCC diagnosis, with heterogeneity in sensitivity. The presence of heterogeneity in the study was found to have a significant association with factors such as the study design, the number of image reviewers, the proportion of cirrhosis, the proportion of other non-HCC malignancies (OM) cases, and the type of reference standard (P≤0.05). The modified CEUS LI-RADS LR-5 categorization demonstrates a reasonable level of sensitivity 0.77, but an insufficient level of specificity 0.88 when diagnosing HCC. KP defects cannot be used as a primary feature in the diagnosis of HCC by CEUS LI-RADS, perhaps as an ancillary feature.

Compared with SonoVue® LR-5, Sonazoid® modified LR-5 has better diagnostic sensitivity for hepatocellular carcinoma: a systematic review and meta-analysis.

The contrast-enhanced ultrasound (CEUS) liver imaging reporting and data system (LI-RADS) is a standardized system for reporting liver nodules in patients at risk of developing hepatocellular carcinoma (HCC) and is only recommended for pure blood pool agents such as SonoVue®. A modified LI-RADS was proposed for Sonazoid®, a Kupffer cell-specific contrast agent. This meta-analysis was conducted to compare the diagnostic efficiency of the CEUS LI-RADS for SonoVue® and the modified LI-RADS for Sonazoid®. The PubMed, Medline, Web of Science, Embase, and Cochrane Library databases were systematically searched to retrieve studies on the diagnostic efficiency of the CEUS LI-RADS algorithms in diagnosing HCC using SonoVue® and/or Sonazoid® from January 2016 to June 2023. Histopathology or imaging follow-up served as the reference standards. Only articles published in English on retrospective or prospective studies with full reports were included in the meta-analysis. A bivariate random-effects model was used. Data pooling, meta-regression, and sensitivity analysis were performed for the meta-analysis. Deeks' funnel plot asymmetry test was used to evaluate publication bias, and the QUADAS-2 tool was used to assess the methodological quality of eligible studies. In total, 26 studies comprising 8,495 patients with 9,244 lesions were included in the meta-analysis. The pooled data results for SonoVue® LI-RADS category 5 (LR-5) and Sonazoid® modified LR-5 were as follows: pooled sensitivity: 0.68 [95%confidence interval (CI): 0.64-0.73, I2=89.20%; P<0.01] and 0.82 (95% CI: 0.74-0.87, I2=85.39%; P<0.01) (P<0.05); pooled specificity: 0.93 (95% CI: 0.90-0.96, I2=86.52%; P<0.01) and 0.86 (95% CI: 0.79-0.91, I2=59.91%; P=0.01) (P<0.05); pooled area under the curve (AUC): 0.86 (95% CI: 0.82-0.89) and 0.91 (95% CI: 0.88-0.93) (P<0.05), respectively. The meta-regression analysis revealed that the study design, subject enrollment method, and reference standard contributed to the heterogeneity of SonoVue® LR-5, and the number of lesions was a source of heterogeneity for Sonazoid® modified LR-5. The diagnostic performance of the LI-RADS category M (LR-M) algorithms of SonoVue® and Sonazoid® was comparable. The Sonazoid® modified LR-5 algorithm had a higher diagnostic sensitivity, lower specificity, and higher AUC than SonoVue® LR-5.

Comments and illustrations of the WFUMB CEUS liver guidelines: Rare benign focal liver lesion, part I.

Improved detection and characterization of common focal liver lesions (FLL) are the main topics of the World Federation for Ultrasound in Medicine and Biology (WFUMB) guidelines on the use of contrast-enhanced ultrasound (CEUS). On stateof-the-art CEUS imaging, to create a library of rare FLL, especially concerning their atypical imaging characteristics, might be helpful for improving clinical diagnostic efficiency. In this review, we aim to summarize the ultrasound and CEUS features of rare benign FLL. Currently there are limited reports and images published.

Deep learning using contrast-enhanced ultrasound images to predict the nuclear grade of clear cell renal cell carcinoma.

To assess the effectiveness of a deep learning model using contrastenhanced ultrasound (CEUS) images in distinguishing between low-grade (grade I and II) and high-grade (grade III and IV) clear cell renal cell carcinoma (ccRCC). A retrospective study was conducted using CEUS images of 177 Fuhrmangraded ccRCCs (93 low-grade and 84 high-grade) from May 2017 to December 2020. A total of 6412 CEUS images were captured from the videos and normalized for subsequent analysis. A deep learning model using the RepVGG architecture was proposed to differentiate between low-grade and high-grade ccRCC. The model's performance was evaluated based on sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating characteristic curve (AUC). Class activation mapping (CAM) was used to visualize the specific areas that contribute to the model's predictions. For discriminating high-grade ccRCC from low-grade, the deep learning model achieved a sensitivity of 74.8%, specificity of 79.1%, accuracy of 77.0%, and an AUC of 0.852 in the test set. The deep learning model based on CEUS images can accurately differentiate between low-grade and high-grade ccRCC in a non-invasive manner.

Development and validation of a multi-modal ultrasomics model to predict response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

To assess the performance of multi-modal ultrasomics model to predict efficacy to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) and compare with the clinical model. This study retrospectively included 106 patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 at our hospital, randomly divided into a training set of 74 and a validation set of 32 in a 7: 3 ratios. Ultrasomics features were extracted from the tumors' region of interest of B-mode ultrasound (BUS) and contrast-enhanced ultrasound (CEUS) images based on PyRadiomics. Mann-Whitney U test, spearman, and least absolute shrinkage and selection operator algorithms were utilized to reduce features dimension. Five models were built with ultrasomics and clinical analysis using multilayer perceptron neural network classifier based on python. Including BUS, CEUS, Combined_1, Combined_2 and Clinical models. The diagnostic performance of models was assessed with the area under the curve (AUC) of the receiver operating characteristic. The DeLong testing algorithm was utilized to compare the models' overall performance. The AUC (95% confidence interval [CI]) of the five models in the validation cohort were as follows: BUS 0.675 (95%CI: 0.481-0.868), CEUS 0.821 (95%CI: 0.660-0.983), Combined_1 0.829 (95%CI: 0.673-0.985), Combined_2 0.893 (95%CI: 0.780-1.000), and Clinical 0.690 (95%CI: 0.509-0.872). The Combined_2 model was the best in the overall prediction performance, showed significantly better compared to the Clinical model after DeLong testing (P < 0.01). Both univariate and multivariate logistic regression analyses showed that age (P < 0.01) and clinical stage (P < 0.01) could be an independent predictor of efficacy after nCRT in patients with LARC. The ultrasomics model had better diagnostic performance to predict efficacy to nCRT in patients with LARC than the Clinical model.

Interaction Between Microbubbles and Microwave Ablation: A Phantom and Rabbit Model.

This study aimed to explore the interactions between microbubbles and microwave ablation (MWA). The study employed custom-made phantoms (in vitro) and white New Zealand rabbits (in vivo). MWA was performed with or without microbubbles in the phantoms (2 × 105 particles mL-1 ) and rabbit livers (intravenous injection of 0.05 mL/kg SonoVue). During the MWA, K-type thermocouple probes were used to monitor the MWA-induced temperature increase. Contrast-enhanced ultrasound imaging (CEUS) was used to monitor and analyze the microbubbles signal intensity. After MWA, the ablation-zone volumes were evaluated and compared between the groups with and without microbubbles. In both the phantom models and rabbits, microbubbles showed no significant influence on MWA, including the ablation range and MWA-induced temperature increase. In phantoms and rabbit livers filled with microbubbles, MWA caused the formation of a gradually expanding microbubble-defect region over the ablation time. An increase in the temperature caused microbubble destruction. Microbubbles had no significant influence on MWA. However, MWA induced the destruction of microbubbles in a temperature-dependent manner. Thus, the poor thermotolerance of microbubbles is a non-negligible barrier when using CEUS to monitor the ablation range during MWA in real-time.

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.

Motion correction of 3D dynamic contrast-enhanced ultrasound imaging without anatomical B-Mode images: Pilot evaluation in eight patients.

Dynamic contrast-enhanced ultrasound (DCE-US) is highly susceptible to motion artifacts arising from patient movement, respiration, and operator handling and experience. Motion artifacts can be especially problematic in the context of perfusion quantification. In conventional 2D DCE-US, motion correction (MC) algorithms take advantage of accompanying side-by-side anatomical B-Mode images that contain time-stable features. However, current commercial models of 3D DCE-US do not provide side-by-side B-Mode images, which makes MC challenging. This work introduces a novel MC algorithm for 3D DCE-US and assesses its efficacy when handling clinical data sets. In brief, the algorithm uses a pyramidal approach whereby short temporal windows consisting of three consecutive frames are created to perform local registrations, which are then registered to a master reference derived from a weighted average of all frames. We applied the algorithm to imaging studies from eight patients with metastatic lesions in the liver and assessed improvements in original versus motion corrected 3D DCE-US cine using: (i) frame-to-frame volumetric overlap of segmented lesions, (ii) normalized correlation coefficient (NCC) between frames (similarity analysis), and (iii) sum of squared errors (SSE), root-mean-squared error (RMSE), and r-squared (R2 ) quality-of-fit from fitted time-intensity curves (TIC) extracted from a segmented lesion. We noted improvements in frame-to-frame lesion overlap across all patients, from 68%±13% without correction to 83%±3% with MC (p=0.023). Frame-to-frame similarity as assessed by NCC also improved on two different sets of time points from 0.694±0.057 (original cine) to 0.862±0.049 (corresponding MC cine) and 0.723±0.066 to 0.886±0.036 (p ≤ 0.001 for both). TIC analysis displayed a significant decrease in RMSE (p=0.018) and a significant increase in R2 goodness-of-fit (p=0.029) for the patient cohort. Overall, results suggest decreases in 3D DCE-US motion after applying the proposed algorithm.

Three-dimensional ultrasound fusion imaging in precise needle placement for thermal ablation of hepatocellular carcinoma

Purpose To investigate the value of three-dimensional ultrasound fusion imaging (3DUS FI) technique for guiding needle placement in hepatocellular carcinoma (HCC) thermal ablation. Methods A total of 57 patients with 60 HCCs with 3DUS FI-guided thermal ablation were retrospectively included in the study. 3DUS volume data of liver were acquired preoperatively by freehand scanning with the tumor and predetermined 5 mm ablative margin automatically segmented. Plan of needle placement was made through a predetermined simulated ablation zone to ensure a 5 mm ablative margin with the coverage rate toward tumor and ablative margin. With real-time ultrasound and 3DUS fusion imaging, ablation needles were placed according to the plan. After ablation, the ablative margin was immediately evaluated by contrast-enhanced ultrasound and 3DUS fusion imaging. The rate of adequate ablative margin, complete response (CR), local tumor progression (LTP), disease-free survival (DFS), and overall survival (OS) was evaluated. Results According to postoperative contrast-enhanced CT or MR imaging, the complete response rate was 100% (60/60), and 83% of tumors (30/36) achieved adequate ablative margin (>5 mm) three-dimensionally. During the follow-up period of 6.0-42.6 months, LTP occurred in 5 lesions, with 1- and 2-year LTP rates being 7.0% and 9.4%. The 1- and 2-year DFS rates were 76.1% and 65.6%, and 1- and 2-year OS rates were 98.1% and 94.0%. No major complications or ablation-related deaths were observed in any patients. Conclusions Three-dimensional ultrasound fusion imaging technique may improve the needle placement of thermal ablation for HCC and reduce the rate of LTP.

Ultrasound Imaging of Tumor Vascular CD93 with MMRN2 Modified Microbubbles for Immune Microenvironment Prediction.

Vascular microenvironment is found to be closely related to immunotherapy efficacy. Identification and ultrasound imaging of the unique vascular characteristics, able to predict immune microenvironment, is important for immunotherapy decision-making. Herein, it is proved that high CD93 expression in the tumor vessels is closely related to the poor immune response of prostate cancer. For ultrasound molecular imaging of CD93, CD93-targeted microbubbles (MBs) consist a gaseous core and the MMRN2 (Multimerin-2) containing cell membrane (CM) /lipid hybrid membrane is then synthesized. In vitro and in vivo assays demonstrate that these MBs can recognize CD93 efficiently and then accumulate within tumor regions highly expressing CD93. Contrast-enhanced ultrasound (CEUS) imaging with CD93-targeted MBs demonstrates that targeted ultrasound intensity is negatively related to inflammatory tumor immune microenvironment (TIME) and cytotoxic T cell infiltration. Together, endothelial expression of CD93 in tumor is a unique predictor of immunosuppressive microenvironment and CD93-targeted MBs have a great potential to evaluate tumor immune status.

A unifying Rayleigh-Plesset-type equation for bubbles in viscoelastic media.

Understanding the ultrasound pressure-driven dynamics of microbubbles confined in viscoelastic materials is relevant for multiple biomedical applications, ranging from contrast-enhanced ultrasound imaging to ultrasound-assisted drug delivery. The volumetric oscillations of spherical bubbles are analyzed using the Rayleigh-Plesset equation, which describes the conservation of mass and momentum in the surrounding medium. Several studies have considered an extension of the Rayleigh-Plesset equation for bubbles embedded into viscoelastic media, but these are restricted to a particular choice of constitutive model and/or to small deformations. Here, we derive a unifying equation applicable to bubbles in viscoelastic media with arbitrary complex moduli and that can account for large bubble deformations. To derive this equation, we borrow concepts from finite-strain theory. We validate our approach by comparing the result of our model to previously published results and extend it to show how microbubbles behave in arbitrary viscoelastic materials. In particular, we use our viscoelastic Rayleigh-Plesset model to compute the bubble dynamics in benchmarked viscoelastic liquids and solids.

A combination of PD-L1-targeted IL-15 mRNA nanotherapy and ultrasound-targeted microbubble destruction for tumor immunotherapy

PD-1/PD-L1-based immune checkpoint blockade therapy has shown limited benefits in tumor patients, partially attributed to the inadequate infiltration of immune effector cells within tumors. Here, we established a nanoplatform named DPPA/IL-15 NPs to target PD-L1 for the tumor delivery of IL-15 messenger RNA (mRNA). DPPA/IL-15 NPs were endowed with ultrasound responsiveness and contrast-enhanced ultrasound (CEUS) imaging performance. They effectively protected IL-15 mRNA from degradation and specifically transfected it into tumor cells through the utilization of ultrasound-targeted microbubble destruction (UTMD). This resulted in the activation of IL-15-related immune effector cells while blocking the PD-1/PD-L1 pathway. In addition, UTMD could generate reactive oxygen species (ROS) that induce endoplasmic reticulum (ER) stress-driven immunogenic cell death (ICD), initiating anti-tumor immunity. In vitro and in vivo studies revealed that this combination therapy could induce a robust systemic immune response and enhance anti-tumor efficacy. Thus, this combination therapy has the potential for clinical translation through enhanced immunotherapy and provides real-time ultrasound imaging guidance.

Dual-modal radiomics nomogram based on contrast-enhanced ultrasound to improve differential diagnostic accuracy and reduce unnecessary biopsy rate in ACR TI-RADS 4–5 thyroid nodules

BackgroundAmerican College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS, TR) 4 and 5 thyroid nodules (TNs) demonstrate much more complicated and overlapping risk characteristics than TR1-3 and have a rather wide range of malignancy possibilities (> 5%), which may cause overdiagnosis or misdiagnosis. This study was designed to establish and validate a dual-modal ultrasound (US) radiomics nomogram integrating B-mode ultrasound (BMUS) and contrast-enhanced ultrasound (CEUS) imaging to improve differential diagnostic accuracy and reduce unnecessary fine needle aspiration biopsy (FNAB) rates in TR 4–5 TNs.MethodsA retrospective dataset of 312 pathologically confirmed TR4-5 TNs from 269 patients was collected for our study. Data were randomly divided into a training dataset of 219 TNs and a validation dataset of 93 TNs. Radiomics characteristics were derived from the BMUS and CEUS images. After feature reduction, the BMUS and CEUS radiomics scores (Rad-score) were built. A multivariate logistic regression analysis was conducted incorporating both Rad-scores and clinical/US data, and a radiomics nomogram was subsequently developed. The performance of the radiomics nomogram was evaluated using calibration, discrimination, and clinical usefulness, and the unnecessary FNAB rate was also calculated.ResultsBMUS Rad-score, CEUS Rad-score, age, shape, margin, and enhancement direction were significant independent predictors associated with malignant TR4-5 TNs. The radiomics nomogram involving the six variables exhibited excellent calibration and discrimination in the training and validation cohorts, with an AUC of 0.873 (95% CI, 0.821–0.925) and 0.851 (95% CI, 0.764–0.938), respectively. The marked improvements in the net reclassification index and integrated discriminatory improvement suggested that the BMUS and CEUS Rad-scores could be valuable indicators for distinguishing benign from malignant TR4-5 TNs. Decision curve analysis demonstrated that our developed radiomics nomogram was an instrumental tool for clinical decision-making. Using the radiomics nomogram, the unnecessary FNAB rate decreased from 35.3 to 14.5% in the training cohort and from 41.5 to 17.7% in the validation cohorts compared with ACR TI-RADS.ConclusionThe dual-modal US radiomics nomogram revealed superior discrimination accuracy and considerably decreased unnecessary FNAB rates in benign and malignant TR4-5 TNs. It could guide further examination or treatment options.

Evaluation of the Time Difference Method in Identifying Hepatocellular Carcinoma in Current CEUS LR-M Category Nodules

ObjectiveTo explore a potential method for improving the diagnostic detection hepatocellular carcinoma (HCC) based on the contrast-enhanced ultrasound (CEUS) liver imaging reporting and data system (LI-RADS) version 2017. MethodsWe retrospectively evaluated 585 liver nodules in 427 patients with HCC risk from December 2020 to March 2023. The nodules were categorized as LR-1 to LR-M based on CEUS LI-RADS version 2017 and were randomly subclassified into a developmental cohort (DC) and a validation cohort (VC) at 3:1. In the DC, the cutoff value of the time difference (∆T) for differentiating HCC from other malignancies in LR-M was calculated, which was used to reclassify nodules in the VC. The diagnostic effect on HCC detection before and after reclassification was further assessed. ResultsAccording to the current CEUS LI-RADS, 140 of 426 (32.9%) confirmed HCC nodules were misclassified as LR-M. In the DC (439 nodules), the receiver operating characteristic (ROC) curve showed that the cutoff value of ∆T (washout onset time minus contrast arrival time) recommended for HCC diagnosis was greater than 21 s. In the VC (146 nodules), 34 HCCs were correctly categorized as LR-5 according to the cutoff value and after reclassification LR-5 had higher accuracy (67.1% vs. 89.0%, p < 0.001) and sensitivity (56.0% vs. 87.2%, p < 0.001) for HCC diagnosis with high specificity (100% vs. 94.6%, p = 0.500) remained. ConclusionUsing the time difference method could identify HCC nodules misdiagnosed as LR-M and improve the diagnostic performance for current CEUS LI-RADS.

Applications of the Photocatalytic and Photoacoustic Properties of Gold Nanorods in Contrast-Enhanced Ultrasound and Photoacoustic Imaging.

The applications of ultrasound imaging are often limited due to low contrast, which arises from the comparable acoustic impedance of normal tissues and disease sites. To improve the low contrast, we propose a contrast agent called gas-generating laser-activatable nanorods for contrast enhancement (GLANCE), which enhances ultrasound imaging contrast in two ways. First, GLANCE absorbs near-infrared lasers and generates nitrogen gas bubbles through the photocatalytic function of gold nanorods and photolysis of azide compounds. These gas bubbles decrease the acoustic impedance and highlight the injection site from the surrounding tissues. Second, GLANCE exhibits photoacoustic properties owing to the gold nanorods that emit photoacoustic signals upon laser irradiation. Additionally, GLANCE offers several benefits for biomedical applications such as nanometer-scale size, adjustable optical absorption, and biocompatibility. These distinctive features of GLANCE would overcome the limitations of conventional ultrasound imaging and facilitate the accurate diagnosis of various diseases.

Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics.

Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50-61% of all renal tumors are found incidentally. Methods: The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions: The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context.

Radiomics-Based Machine Learning Classification Strategy for Characterization of Hepatocellular Carcinoma on Contrast-Enhanced Ultrasound in High-Risk Patients with LI-RADS Category M Nodules

Abstract Objective Accurate differentiation within the LI-RADS category M (LR-M) between hepatocellular carcinoma (HCC) and non-HCC malignancies (mainly intrahepatic cholangiocarcinoma [CCA] and combined hepatocellular and cholangiocarcinoma [cHCC-CCA]) is an area of active investigation. We aimed to use radiomics-based machine learning classification strategy for differentiating HCC from CCA and cHCC-CCA on contrast-enhanced ultrasound (CEUS) images in high-risk patients with LR-M nodules. Methods A total of 159 high-risk patients with LR-M nodules (69 HCC and 90 CCA/cHCC-CCA) who underwent CEUS within 1 month before pathologic confirmation from January 2006 to December 2019 were retrospectively included (111 patients for training set and 48 for test set). The training set was used to build models, while the test set was used to compare models. For each observation, six CEUS images captured at predetermined time points (T1, peak enhancement after contrast injection; T2, 30 seconds; T3, 45 seconds; T4, 60 seconds; T5, 1–2 minutes; and T6, 2–3 minutes) were collected for tumor segmentation and selection of radiomics features, which included seven types of features: first-order statistics, shape (2D), gray-level co-occurrence matrix, gray-level size zone matrix, gray-level run length matrix, neighboring gray tone difference matrix, and gray-level dependence matrix. Clinical data and key radiomics features were employed to develop the clinical model, radiomics signature (RS), and combined RS-clinical (RS-C) model. The RS and RS-C model were built using the machine learning framework. The diagnostic performance of these three models was calculated and compared. Results Alpha-fetoprotein (AFP), CA19-9, enhancement pattern, and time of washout were included as independent factors for clinical model (all p &lt; 0.05). Both the RS and RS-C model performed better than the clinical model in the test set (area under the curve [AUC] of 0.698 [0.571–0.812] for clinical model, 0.903 [0.830–0.970] for RS, and 0.912 [0.838–0.977] for the RS-C model; both p &lt; 0.05). Conclusions Radiomics-based machine learning classifiers may be competent for differentiating HCC from CCA and cHCC-CCA in high-risk patients with LR-M nodules.

A multi-parameter intrahepatic cholangiocarcinoma scoring system based on modified contrast-enhanced ultrasound LI-RADS M criteria for differentiating intrahepatic cholangiocarcinoma from hepatocellular carcinoma.

To develop a multi-parameter intrahepatic cholangiocarcinoma (ICC) scoring system and compare its diagnostic performance with contrast-enhanced ultrasound (CEUS) liver imaging reporting and data system M (LR-M) criteria for differentiating ICC from hepatocellular carcinoma (HCC). This retrospective study enrolled 62 high-risk patients with ICCs and 62 high-risk patients with matched HCCs between January 2022 and December 2022 from two institutions. The CEUS LR-M criteria was modified by adjusting the early wash-out onset (within 45 s) and the marked wash-out (within 3 min). Then, a multi-parameter ICC scoring system was established based on clinical features, B-mode ultrasound features, and modified LR-M criteria. We found that elevated CA 19-9 (OR=12.647), lesion boundary (OR=11.601), peripheral rim-like arterial phase hyperenhancement (OR=23.654), early wash-out onset (OR=7.211), and marked wash-out (OR=19.605) were positive predictors of ICC, whereas elevated alpha-fetoprotein (OR=0.078) was a negative predictor. Based on these findings, an ICC scoring system was established. Compared with the modified LR-M and LR-M criteria, the ICC scoring system showed the highest area under the curve (0.911 vs. 0.831 and 0.750, both p<0.05) and specificity (0.935 vs. 0.774 and 0.565, both p<0.05). Moreover, the numbers of HCCs categorized as LR-M decreased from 27 (43.5%) to 14 (22.6%) and 4 (6.5%) using the modified LR-M criteria and ICC scoring system, respectively. The modified LR-M criteria-based multi-parameter ICC scoring system had the highest specificity for diagnosing ICC and reduced the number of HCC cases diagnosed as LR-M category.