Purpose This study aimed to enhance the noninvasive identification of hepatocellular carcinoma (HCC) by modifying the Sonazoid-based contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) through integration of Kupffer-phase (KP) imaging and optimization of washout timing criteria. Methods This retrospective two-center study enrolled 558 patients with solitary liver nodules who underwent Sonazoid-based CEUS between August 2022 and September 2024. CEUS features were assessed according to LI-RADS v2017 using predefined washout time windows (2, 5, and 10 minutes). A modified LI-RADS algorithm incorporating KP hypoenhancement was developed. Interobserver agreement was evaluated using the Cohen kappa. Diagnostic performance metrics (area under the curve [AUC], sensitivity, specificity, and positive predictive value) were compared between the modified CEUS LI-RADS algorithms and the Japan Society of Hepatology (JSH) and Korean Liver Cancer Association (KLCA) guidelines using the McNemar and DeLong tests. Results For key CEUS features, interobserver agreement was moderate to almost perfect (κ=0.580–0.815). The modified CEUS LI-RADS-10min algorithm demonstrated superior performance over the other algorithms. By reclassifying LR-M nodules with early washout and mild KP hypoenhancement as LR-5, the modified CEUS LI-RADS-10min algorithm achieved the highest diagnostic performance for HCC (AUC, 0.782), improving sensitivity while maintaining specificity (73.7%). Its performance was comparable to that of the KLCA guideline. The JSH guidelines showed the highest sensitivity (91.7%) but the lowest specificity (53.9%). Conclusion Integrating KP hypoenhancement with a 10-minute washout window improves the sensitivity of Sonazoid-based CEUS LI-RADS for diagnosing HCC while preserving specificity, providing feasible refinement aligned with established guidelines and promoting standardization.

Abstract Background To evaluate the feasibility of contrast-enhanced ultrasound (CEUS) for early prediction of treatment response to chemotherapy combined with Cetuximab (Cet) in colorectal liver metastasis (CRLM). Methods From June 2023 and October 2024, 139 consecutive patients with CRLM who underwent chemotherapy with Cet were sequentially allocated into a training cohort (n = 105) and a validation cohort (n = 34). CEUS examinations were conducted pre-treatment at baseline (week 0) and at weeks 2, 4, 6 and 8 post-treatment, with one target lesion consistently monitored throughout the therapeutic course. Perfusion parameters were derived using SonoLiver software. The reductions (Δ) and ratios of these parameters from baseline to each subsequent follow-up time point were compared between responders and non-responders. Results According to mRECIST criteria, 83 patients (training cohort: n = 62, validation cohort: n = 21) and 56 patients (training cohort: n = 43, validation cohort: n = 13) were classified as responders and non-responders, respectively. In the training cohort, responders demonstrated significantly smaller tumor diameters compared to non-responders beginning at week 4 (4.6 ± 2.3 cm vs. 5.4 ± 2.8 cm, p = 0.027). From week 2 onward, the reductions and ratios of maximum intensity (IMAX) and area under curve (AUC) in responders were significantly higher compared to non-responders. At all time points (weeks 2, 4, and 6), the diagnostic performance of IMAX and AUC ratios was superior to that of ΔIMAX and ΔAUC. Furthermore, the AUROCs of IMAX and AUC ratios at week 4 were significantly higher than those at week 2 (Z = 3.531, p < 0.05; Z = 3.550, p < 0.05, respectively) and comparable to those at week 6 (Z = 1.596, p = 0.11; Z = 1.566, p = 0.12, respectively). In the validation cohort at week 4 post-treatment, the AUROCs of IMAX and AUC ratios were 0.896 and 0.905 (both p < 0.05), with corresponding accuracies of 85.3% and 85.3%. Conclusion The ratios of AUC and IMAX at week 4 post-treatment may serve as reliable early predictors of treatment response to chemotherapy combined with Cetuximab in patients with CRLM.

OBJECTIVES:To investigate the diagnostic performance of the quantitative morphological feature of solidity in preoperative prediction of proliferative hepatocellular carcinoma (HCC), and to compare the diagnostic performance between quantitative morphological features and machine learning (ML) models that incorporate Sonazoid contrast-enhanced ultrasound (CEUS) and clinical features. METHODS:This retrospective two-center study included 395 patients with histopathologically confirmed HCC. The morphological feature of solidity, along with clinical and CEUS features, was analyzed to predict the proliferative status of HCC. Eight ML models were trained and validated using area under the curve (AUC), calibration, and decision curve analysis (DCA). SHAP interpretability tools were used to elucidate feature contributions. RESULTS:Solidity emerged as the strongest independent predictor of proliferative HCC with AUC of 0.836 and 0.768 in the two-center cohorts, respectively. The LightGBM model, which integratedsolidity, AFP ≥ 400 ng/mL, ratio of neutrophils to lymphocytes (N/L), ten-min ratio, and standard deviation (StdDev) of lesion, achieved superior performance, with AUCs of 0.887 (95% CI: 0.803-0.971) and 0.871 (0.791-0.948) in internal and external validation, respectively, significantly surpassingsolidity(P = 0.0026 and 0.0067) and LightGBM-4F (P = 0.0004 and 0.033). Robustness was confirmed via 10-fold cross-validation (mean AUC = 0.897). Calibration curves and DCA confirmed the clinical utility across cohorts. SHAP analysis highlightedsolidity(mean impact = 1.38) as the dominant predictor, followed by AFP (mean = 0.7). CONCLUSION:Our interpretable ML model leverages quantitative CEUS features, spearheaded by the morphological biomarker solidity, to preoperatively predict HCC proliferative status. This enables noninvasive risk stratification, facilitating precision treatment planning.

BACKGROUND:We developed and validated an interpretable machine learning (ML) model integrating quantitative Sonazoid contrast-enhanced ultrasound (CEUS) and clinical features to predict microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS:We retrospectively analyzed 556 histopathologically confirmed HCCs from three Chinese hospitals. Ten ML models were constructed using significant Sonazoid CEUS and clinical features. Model performance was evaluated via the area under the receiver operating characteristic curve (AUC). The combined model (CEUS + clinical) was compared to the clinical model to assess the added predictive value. RESULTS:The tumor diameter, vitamin K Absence II (PIVKA-II) (≥40 ng/mL), alpha-fetoprotein (AFP) (≥400 ng/mL), ten-minute ratio, and standard deviation of peak intensity were identified as the significant variables for model development. XGBoost was selected as the optimal combined model for accurate MVI prediction in internal validation (AUC = 0.862) and external validation (AUC = 0.841) cohorts. Compared to the clinical model, the combined model resulted in higher AUC values in the internal validation (0.862 vs. 0.631, p = 0.012) and external validation (0.841 vs.0.653, p = 0.004) cohorts. CONCLUSIONS:The XGBoost combined model represents a promising approach for predicting MVI in HCC patients. The superior performance of the combined model to the clinical model highlighted the significant added value of quantitative Sonazoid CEUS features in enhancing MVI predicting accuracy.

OBJECTIVE:We aimed to develop and validate a prediction model to identify HCC in focal liver lesions (FLLs) ≤20 mm among patients at risk for HCC based on clinical and contrast-enhanced ultrasound (CEUS) features. METHODS:Between January 2022 and July 2023, 386 patients (mean age 58 ± 11 years; 277 male) at risk for HCC with FLLs ≤20 mm and clinical and preoperative CEUS data from three centers were retrospectively enrolled. Three prediction models based on clinical data (Cli-M), CEUS features (CEUS-M), and combined clinical and CEUS features (Com-M) were constructed using the training cohort (187 patients). Their predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) in the internal and external validation cohorts. All patients were reclassified using the American College of Radiology CEUS Liver Imaging Reporting and Data System (CEUS LI-RADS) and combined with the best-performing model (modified LI-RADS). RESULTS:The AUCs of Com-M were 0.873-0.951 in the training, internal, and external validation cohorts, which were higher than those of Cli-M (0.749-0.795, all P < 0.05) and CEUS-M (0.848-0.899, all P < 0.05). The sensitivity of LR-5 of modified LI-RADS was significantly improved from 83.1 % to 88.9 % (p＜0.001) in the training, internal and external validation cohort while there was no statistical different on its specificity (82.6 %-94.7 % vs 95.7 %-97.6 %., p = 0.162-0.650). CONCLUSIONS:The model based on clinical and CEUS features can help identify HCC in FLLs ≤ 20 mm in high-risk patients.

To investigate the value of dynamic contrast-enhanced ultrasound (DCE-US) analysis using the Liver Imaging Reporting and Data System (LI-RADS) to improve the diagnosis of hepatocellular carcinoma (HCC). This multicenter study retrospectively enrolled consecutive high-risk patients for HCC who underwent contrast-enhanced ultrasound (CEUS) between December 2022 and June 2023. Quantitative CEUS analysis was performed using VueBox® to obtain diagnostic parameters for HCC. These parameters were used as auxiliary indicators to reassign the LI-RADS categories. The reference standard was pathologic confirmation or composite criteria. The diagnostic performance of LI-RADS with and without quantitative DCE-US parameters was assessed. 269 patients (median age, 61 years [interquartile range, 52–69]; 206 men, 63 women) with 269 focal liver lesions (FLLs) (median size, 40 mm [interquartile range, 25–62 mm]) were included. Among the 269 FLLs, 227 were HCC, 31 non-HCC malignancies, and 11 benign lesions. DCE-US analysis showed HCC had higher rise time (RT) and fall time (FT) at the lesion margin than non-HCC malignancies (both P < 0.05) but lower RT and FT than benign lesions (both P < 0.05). RT at the lesion margin (range 17.48 s–21.16 s) serves as an auxiliary indicator for HCC diagnosis. Compared to CEUS LI-RADS, the revised LR-5 improved sensitivity (61.7 vs. 52.8

To evaluate the potential of dynamic contrast-enhanced ultrasound (CEUS) quantitative parameters in preoperative prediction of macrotrabecular-massive (MTM) subtype and high Ki-67 pattern in hepatocellular carcinoma (HCC) patients. This study included a retrospective primary cohort and a multicenter prospective validation cohort comprising HCC patients who underwent surgical resection and preoperative CEUS between January 2023 and April 2024. The Clinic-CEUS model was established by combining clinical data and CEUS features, while the Clinic-Q-CEUS model was constructed by combining clinical data and CEUS features with matched quantitative parameters. Model performance was tested with the area under the receiver operating characteristic curve (AUC) in the validation cohort. A total of 170 patients (mean age, 61 years ± 11 [SD]; 130 men; primary cohort, n = 118; validation cohort, n = 52) were included. The Clinic-Q-CEUS model better predicted MTM subtype and high Ki-67 pattern than the Clinic-CEUS model (AUC, 0.860 vs. 0.753, p = 0.027 and AUC, 0.836 vs. 0.738, p = 0.036) in the primary cohort, with similar performance in the validation cohort (AUC, 0.868 vs. 0.693, p = 0.046 and AUC, 0.787 vs. 0.610, p = 0.018). Dynamic CEUS quantification analysis could be used as an effective adjunct tool for preoperative identification of MTM subtype and high Ki-67 pattern in HCC patients. Dynamic contrast-enhanced ultrasound (CEUS) quantitative parameters can help radiologists more accurately identify aggressive macrotrabecular-massive (MTM) subtype and high Ki-67 pattern in HCC patients preoperatively, which provides useful information for subsequent treatment planning.

Immunogenic cell death (ICD) significantly boosts anti-tumor immunotherapy effectiveness; however, the recruitment of polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs), driven by factors such as adenosine accumulation and oxygen depletion during ICD, impairs the overall therapeutic outcome as well as facilitates tumor development and metastasis. Recent studies identify CD300ld as a key regulator of PMN-MDSCs recruitment, making it a promising immunotherapeutic target. Here, an innovative strategy is developed to eminently amplify ICD while alleviating PMN-MDSC infiltration upon ultrasound (US) stimulation. A modified generation 5 (G5) poly(amidoamine) dendrimer (G5PBA) was employed to encapsulate hematoporphyrin (GH)-a widely used organic sonosensitizer-and modified the complex with pardaxin peptides (Par) to achieve precise endoplasmic reticulum (ER) targeting, and adsorbed the negatively charged siCD300ld (PGH@siRNA). Under US irradiation, PGH@siRNA precisely accumulates at ER to induce localized reactive oxygen species (ROS) bursts, and effectively silence CD300ld, thereby amplifying endoplasmic reticulum stress (ERS)-mediated ICD and reducing PMN-MDSCs infiltration to reshape the tumor microenvironment. This systematic preclinical evaluations demonstrated enhanced immune activation, suppressed metastasis, and improved therapeutic outcomes. This study introduces a sono-responsive synergistic strategy integrating ER-targeted sonodynamic therapy with gene silencing, offering a novel paradigm for targeting PMN-MDSCs and enhancing triple-negative breast cancer (TNBC) immunotherapy.

OBJECTIVE:To explore the prognostic impact of contrast-enhanced ultrasound (CEUS) features for initially unresectable colorectal liver metastases (CLMs) in a clinical setting of conversion therapy.METHODS:Between March 2015 and November 2020, consecutive patients with CLMs who received conversion treatment were prospectively enrolled. All participants underwent liver CEUS at baseline. The primary endpoint was conversion resection rate (R0 and overall resection). Secondary endpoints were objective response rate (ORR), overall survival (OS), and progression-free survival (PFS).RESULTS:104 participants who completed conversion treatment were included. CEUS enhancement pattern was correlated with index lesion (size and echogenicity), primary (site, differentiation, perineural invasion, and RAS genotype) and serum (CA19-9 level) characteristics (P = <0.001-0.016). CEUS enhancement pattern was significantly associated with R0 resection rate, ORR, PFS, and OS (P = 0.001-0.049), whereas enhancement degree was associated with PFS and OS (P = 0.043 and 0.045). Multivariate analysis showed that heterogeneous enhancement independently predicted R0 and overall resection (P = 0.028 and 0.024) while rim-like enhancement independently predicted ORR and OS (P = 0.009 and 0.026).CONCLUSION:CEUS enhancement pattern was significantly associated with tumor characteristics and clinical outcomes following conversion therapy, and thus might be of prognosis impact for initially unresectable CLMs.

Objective:To explore the MRI characteristics of the hepatic epithelioid hemangioendothelioma (HEHE) classification according to morphology and size.Methods:The clinical, pathological, and MRI imaging data of 40 cases with HEHE confirmed pathologically from December 2009 to September 2021 were retrospectively analyzed. A paired sample t-test was used for comparison between the two groups. Results:There were 40 cases (5 solitary, 24 multifocal, 9 local fusion, and 2 diffuse fusion) and 214 lesions (163 nodules, 31 masses, and 20 fusion foci). The most common features of lesions were subcapsular growth and capsular depression. The signal intensity of lesions ≤1cm was usually uniform with whole or ring enhancement. Nodules and mass-like lesions ≥1cm on a T1-weighted image had slightly reduced signal intensity or manifested as a halo sign. Target signs on a T2-weighted image were characterized by: target or centripetal enhancement; fusion-type lesions; irregular growth and hepatic capsular retraction, with ring or target-like enhancement in the early stage of fusion and patchy irregular enhancement in the late stage; blood vessels traversing or accompanied by malformed blood vessels; focal bleeding; an increasing proportion of extrahepatic metastases and abnormal liver function with the type of classified manifestation; primarily portal vein branches traversing; and reduced overall intralesional bleeding rate (17%). Lollipop signs were presented in 19 cases, with a high expression rate in mass-type lesions (42%). The fusion lesions were expressed, but the morphological manifestation was atypical. The diffusion-weighted imaging mostly showed high signal or target-like high signal. An average apparent diffusion coefficient of lesions was (1.56±0.36) ×10 -3mm 2/s, which was statistically significantly different compared with that of adjacent normal liver parenchyma ( t=8.28, P<0.001). Conclusion:The MRI manifestations for the HEHE classification are closely related to the morphology and size of the lesions and have certain differences and characteristics that are helpful for the diagnosis of the disease when combined with clinical and laboratory examinations.

Background: Though contrast-enhanced ultrasound (CEUS) perfusion parameters have been approved to be potential indicators for response to chemotherapy in solid tumors, their ability in assessment of colorectal liver metastasis (CRLM) to chemotherapy with bevacizumab (Bev) has rarely been investigated. Methods: From March 2021 to May 2022, 115 consecutive CRLM patients with CEUS pre- and post-2 months' chemotherapy with Bev were prospectively enrolled. One target lesion per patient underwent CEUS quantitative analysis with SonoLiver software. Rise time, time-to-peak, mean transit time, maximal intensity (IMAX), and area under the time-intensity curve (AUC) were assessed with region of interest (ROI) selected on whole lesion, lesion periphery, and internal lesion, respectively. The reduction and ratio of postto pre-treatment in parameters were investigated in development cohort (n=89) and validated in internal validation cohort (n=26) according to the chronological order. Results: With modified Response Evaluation Criteria in Solid Tumor as reference, 48, 14 responders and 41, 12 non-responders were included in development and validation cohort, respectively. Significantly smaller values of IMAX and AUC on ROIwhole, ROIperipheral, and ROIinternal, were observed posttreatment in development cohort (all P<0.05). In predicting treatment response, the influence of ROI selection was observed when using increment IMAX and increment AUC, while no influence was observed using ratios. Areas under the receiver operating characteristic curve (AUROCs) for increment IMAX and increment AUC on ROIperipheral were 0.939 (0.867-0.979), 0.951 (0.883-0.985), and 0.917 (0.740-0.988), 0.923 (0.748-0.990) in development and validation cohort, respectively. For ratios of IMAX and AUC, AUROCs were 0.976 (0.919-0.997), 0.938 (0.865-0.978), and 0.899 (0.717-0.982), 0.982 (0.836-1.000) in development and validation cohort, respectively. Conclusions: IMAX and AUC showed significant reductions in responders, and different analyses ROIs influence the performance of increment IMAX and increment AUC in response assessment. Parameters derived from ROI peripheral exhibited the most promising results in predicting treatment response.

Abstract Objective To retrospectively evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) LI-RADS in liver nodules < 20 mm at high risk of hepatocellular carcinoma (HCC) and their correlation with clinic-pathological features. Methods A total of 432 pathologically proved liver nodules < 20 mm were included from January 2019 to June 2022. Each nodule was categorized as LI-RADS grade (LR)-1 to LR-5 through LR-M according to CEUS LI-RADS. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) of CEUS LI-RADS were evaluated using pathological reference standard. Correlations between clinic-pathological features and CEUS LI-RADS categorization, together with major CEUS features, were further explored. Results With LR-5 to diagnose HCC, the sensitivity, specificity, PPV, NPV, and AUC were 50.3%, 70.0%, 91.2%, 18.5%, and 0.601, respectively. The proportion of LR-5 in primary HCCs was significantly higher than that in recurrent ones (p = 0.014). HCC 10–19 mm showed significantly more frequent arterial phase hyper-enhancement (APHE) and late washout (p < 0.05) and less no-washout (p = 0.003) compared with those in HCC < 10 mm. Well-differentiated HCCs showed more frequent non-APHE and no-washout than moderate- and poor-differentiated HCCs (p < 0.05). Upgrading “APHE without washout” LR-4 nodules 10–19 mm with HCC history and “APHE with late mild washout” LR-4 nodules < 10 mm to LR-5 could improve the diagnostic performance of LR-5. The corresponding sensitivity, specificity, PPV, NPV, and AUC are 60.2%, 70.0%, 92.6%, 22.1%, and 0.651, respectively. Conclusions CEUS LI-RADS is valuable in the diagnosis of HCC < 20 mm and performance can be improved with the combination of clinic-pathological features. Critical relevance statement CEUS LI-RADS was valuable in the diagnosis of HCC < 20 mm and its diagnostic performance can be improved by combining clinic-pathological features. Further research is needed to define its value in this set of lesions. Key Points Contrast-enhanced ultrasound can detect small liver lesions where LI-RADS accuracy is uncertain. Many LI-RADS Grade-4 nodules were upgraded to Grade-5 by combining imaging with clinic-pathological factors. The reclassification of LI-RADS Grade-5 can improve sensitivity without decreasing positive predictive value. Graphical Abstract

PURPOSE: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).METHODS: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.RESULT: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.CONCLUSION: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.

This study aimed to develop and validate an ultrasound (US)-based nomogram for the preoperative differentiation of renal urothelial carcinoma (rUC) from central renal cell carcinoma (c-RCC). Clinical data and US images of 655 patients with 655 histologically confirmed malignant renal tumors (521 c-RCCs and 134 rUCs) were collected and divided into training (n = 455) and validation (n = 200) cohorts according to examination dates. Conventional US and contrast-enhanced US (CEUS) tumor features were analyzed to determine those that could discriminate rUC from c-RCC. Least absolute shrinkage and selection operator regression was applied to screen clinical and US features for the differentiation of rUC from c-RCC. Using multivariate logistic regression analysis, a diagnostic model of rUC was constructed and visualized as a nomogram. The diagnostic model’s performance was assessed in the training and validation cohorts by calculating the area under the receiver operating characteristic curve (AUC) and calibration plot. Decision curve analysis (DCA) was used to assess the clinical usefulness of the US-based nomogram. Seven features of both clinical features and ultrasound imaging were selected to build the diagnostic model. The nomogram achieved favorable discrimination in the training (AUC = 0.996, 95

ABSTRACT:This study aimed to evaluate the clinical value of automated breast volume scanner (ABVS) compared with hand-held ultrasound (HHUS). From January 2015 to May 2019, a total of 912 breast lesions in 725 consecutive patients were included in this study. κ statistics were calculated to identify interobserver agreement of ABVS and HHUS. The diagnostic performance for ABVS and HHUS was expressed as the area under the receiver operating characteristic curve, as well as the corresponding 95% confidence interval, sensitivity, and specificity. The sensitivities of ABVS and HHUS were 95.95% and 93.69%, and the specificities were 85.47% and 81.20%, respectively. A difference that nearly reached statistical significance was observed in sensitivities between ABVS and HHUS (P = 0.0525). The specificity of ABVS was significantly higher than that of HHUS (P = 0.006). When lesions were classified according to their maximum diameter, the sensitivity and specificity of ABVS were significantly higher than HHUS for lesions ≤20 mm, while they made no statistical significance between ABVS and HHUS for lesions >20 mm. The interobserver agreement for ABVS was better than that of HHUS. Automated breast volume scanner was more valuable than HHUS in diagnosing breast cancer, especially for lesions ≤20 mm, and it could be a valuable diagnostic tool for breast cancer.

The diagnostic value of Bosniak classification based on contrast enhanced ultrasound (CEUS) on complex cystic renal mass (CRM) was compared among different observers. From 2012 to 2022, 212 complex CRMs, confirmed by pathology or at least 1-year imaging follow-up, were enrolled. The CEUS images of CRM were retrospectively analyzed by two reviewers with different experience in kidney CEUS examination, and each lesion was given the categorizations according the Bosniak ver. 2015 and ver. 2019, respectively. There was good inter-observer agreement (k = 0.689; p < 0.05) for Bosniak ver. 2019. With Bosniak ver. 2019, senior and junior reviewer correctly classified 44 (83.02%), 40 (75.47%) benign lesions as low grade (Ⅰ ∼ ⅡF), and 94 (94.95%), 80 (80.81%) malignant tumors as high grade (Ⅲ or Ⅳ), respectively. Meanwhile, the combination of Bosniak 2019 and senior reviewer demonstrated the highest area under the receiver operating characteristic curve (Az: 0.875, all p < 0.05) than other groups, with the highest specificity (81.10%) and sensitivity (93.90%) among all the groups (95% CI, 0.807–0.943; p < 0.05), respectively. Overall, the senior reviewer combined with Bosniak ver. 2019 was the best choice for the diagnosis of complex CRM.

The 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. Seventy-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. Enhanced 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. Enhanced intensity and high-enhancing ring may be helpful in the differential diagnosis of thyroid nodules with halo sign on B-mode ultrasound.

In recent years, the implication of sphingomyelin in lipid raft formation has intensified the long sustained interest in this membrane lipid. Accumulating evidences show that cholesterol preferentially interacts with sphingomyelin, conferring specific physicochemical properties to the bilayer membrane. The molecular packing created by cholesterol and sphingomyelin, which presumably is one of the driving forces for lipid raft formation, is known in general to differ from that of cholesterol and phosphatidylcholine membranes. However, in many studies, saturated phosphatidylcholines are still considered as a model for sphingolipids. Here, we investigate the effect of cholesterol on mixtures of dioleoyl-phosphatidylcholine (DOPC) and dipalmitoyl-phosphatidylcholine (DPPC) or distearoyl-phosphatidylcholine (DSPC) and compare it to that on mixtures of DOPC and sphingomyelin analyzed in previous studies. Giant unilamellar vesicles prepared from ternary mixtures of various lipid compositions were imaged by confocal fluorescence microscopy and, within a certain range of sterol content, domain formation was observed. The assignment of distinct lipid phases and the molecular mobility in the membrane bilayer was investigated by fluorescence correlation spectroscopy. Cholesterol was shown to affect lipid dynamics in a similar way for DPPC and DSPC when the two phospholipids were combined with cholesterol in binary mixtures. However, the corresponding ternary mixtures exhibited different spatial lipid organization and dynamics. Finally, evidences of a weaker interaction of cholesterol with saturated phosphatidylcholines than with sphingomyelin (with matched chain length) are discussed.