Impact of Microbubble Degradation and Flow Velocity on Subharmonic-aided Pressure Estimation (SHAPE): An Experimental Investigation.

OBJECTIVE: This study aimed to investigate the impact of microbubble degradation and flow velocity on Sub-Harmonic Aided Pressure Estimation (SHAPE), and to explore the correlation between subharmonic amplitude and pressure as a single factor. METHODS: We develop an open-loop vascular phantom platform system and utilize a commercial ultrasound machine and microbubbles for subharmonic imaging. Subharmonic amplitude was measured continuously at constant pressure and flow velocity to assess the impact of microbubble degradation. Flow velocity was varied within a range of 4-14 cm/s at constant pressure to investigate its relationship to subharmonic amplitude. Furthermore, pressure was varied within a range of 10-110 mm Hg at constant flow velocity to assess its isolated effect on subharmonic amplitude. RESULTS: Under constant pressure and flow velocity, subharmonic amplitude exhibited a continuous decrease at an average rate of 0.221 dB/min, signifying ongoing microbubble degradation during the experimental procedures. Subharmonic amplitude demonstrated a positive correlation with flow velocity, with a variation ratio of 0.423 dB/(cm/s). Under controlled conditions of microbubble degradation and flow velocity, a strong negative linear correlation was observed between pressure and subharmonic amplitude across different Mechanical Index (MI) settings (all R2 > 0.90). The sensitivity of SHAPE was determined to be 0.025 dB/mmHg at an MI of 0.04. CONCLUSION: The assessment of SHAPE sensitivity is affected by microbubble degradation and flow velocity. Excluding the aforementioned influencing factors, a strong linear negative correlation between pressure and subharmonic amplitude was still evident, albeit with a sensitivity coefficient lower than previously reported values.

Pitfalls of ultrasound for deep vein thrombosis of the lower extremities.

Venous ultrasound is the primary, widely accepted diagnostic tool to assess deep vein thrombosis (DVT) in the lower extremities. However, other focal lesions in the lower extremities can be identified on ultrasound. The sonographic appearance of these abnormalities may overlap the thrombosis, which included vascular tumors, Baker's cyst, hematoma, cancer thrombosis, and peripheral nerve tumors. This essay derives from cases diagnosed in our centers and published literature, with images available for illustrations, which may help to improve the clinical management of these findings.

Sonographic characteristics of diffuse sclerosing variant of papillary thyroid carcinoma with histopathological correlation: a preliminary study.

BACKGROUND: Diffuse sclerosing variant of papillary thyroid carcinoma (DSVPTC) is a rare but high invasive subtype of papillary thyroid carcinoma, which mandates an aggressive clinical strategy. Few studies have focused on the sonographic characteristics of DSVPTC and the role of ultrasound in diagnosis and treatment of this variant remains unknown. This study aimed to identify and understand DSVPTC more accurately under ultrasound in correlation with pathology. METHODS: The ultrasound characteristics and histopathologic sections of 10 lesions in 10 DSVPTC patients who underwent thyroid surgery at our center between 2014 and 2020 were reviewed and compared with 184 lesions in 168 classic variant of papillary thyroid carcinoma (cPTC) patients. RESULTS: 6 DSVPTC cases (60%) showed the "snowstorm" pattern on sonogram and 4 cases (40%) presented hypoechoic solid nodules only. Vague borders (100.0% vs. 18.5%, P = 0.019) and abundant microcalcifications (66.7% vs. 10.9%, P = 0.037) were more common in DSVPTC nodules than in cPTC nodules, corresponding to the infiltrating boundaries and numerous psammoma bodies under the microscope respectively. Most of the DSVPTC cases had a heterogeneous background (80%) and suspicious metastatic cervical lymph nodes (80%) on sonograms. All DSVPTC cases had histopathological metastatic cervical lymph nodes. CONCLUSION: The sonographic "snowstorm" pattern indicated DSVPTC with whole-lobe occupation. Hypoechoic solid nodules with vague borders and abundant microcalcifications on sonogram suggested DSVPTC lesion with an ongoing invasion. Regardless of which of the two sonograms was shown, the corresponding DSVPTC lesions were aggressive and required the same attention from the surgeons.

Deep-learning-based renal artery stenosis diagnosis via multimodal fusion.

PURPOSE: Diagnosing Renal artery stenosis (RAS) presents challenges. This research aimed to develop a deep learning model for the computer-aided diagnosis of RAS, utilizing multimodal fusion technology based on ultrasound scanning images, spectral waveforms, and clinical information. METHODS: A total of 1485 patients received renal artery ultrasonography from Peking Union Medical College Hospital were included and their color doppler sonography (CDS) images were classified according to anatomical site and left-right orientation. The RAS diagnosis was modeled as a process involving feature extraction and multimodal fusion. Three deep learning (DL) models (ResNeSt, ResNet, and XCiT) were trained on a multimodal dataset consisted of CDS images, spectrum waveform images, and individual basic information. Predicted performance of different models were compared with senior physician and evaluated on a test dataset (N = 117 patients) with renal artery angiography results. RESULTS: Sample sizes of training and validation datasets were 3292 and 169 respectively. On test data (N = 676 samples), predicted accuracies of three DL models were more than 80% and the ResNeSt achieved the accuracy 83.49% ± 0.45%, precision 81.89% ± 3.00%, and recall 76.97% ± 3.7%. There was no significant difference between the accuracy of ResNeSt and ResNet (82.84% ± 1.52%), and the ResNeSt was higher than the XCiT (80.71% ± 2.23%, p < 0.05). Compared to the gold standard, renal artery angiography, the accuracy of ResNest model was 78.25% ± 1.62%, which was inferior to the senior physician (90.09%). Besides, compared to the multimodal fusion model, the performance of single-modal model on spectrum waveform images was relatively lower. CONCLUSION: The DL multimodal fusion model shows promising results in assisting RAS diagnosis.

How to identify juxtaglomerular cell tumor by ultrasound: a case series and review of the literature.

PURPOSE: To study the value of ultrasound in the diagnosis of juxtaglomerular cell tumor (JGCT). METHODS: From January 2005 to July 2020, fifteen patients diagnosed as JGCT by surgical pathology in Peking Union Medical College Hospital were collected. All patients underwent preoperative ultrasound examination. The clinical, laboratory, ultrasound, computed tomography (CT), surgical, and pathological features of the patients were analyzed retrospectively. RESULTS: The 15 patients were 5 males and 10 females with a median age of 29 years (10∼72 years). 14 of them had hypertension and one had normal blood pressure. The tumors were all solitary, with a median diameter of 1.5 cm (0.9-5.9 cm). Among the fifteen patients, eleven were correctly detected by preoperative ultrasound, and four were missed. There was a significant difference in tumor size (2.64 ± 1.48 cm vs. 1.23 ± 0.21 cm) and whether the tumor protruded outward (9/11 vs. 0/4) between the ultrasound-detected group and the ultrasound-missed group (p = 0.010, p = 0.011). Of the 11 tumors detected by ultrasound, four were extremely hypoechoic, two were hypoechoic, three were isoechoic, and two were hyperechoic. Color Doppler showed no blood flow in five tumors with the size range from 0.9 to 2.0 cm, and mild blood flow in six tumors with the size range from 2.8 to 5.9 cm. CONCLUSIONS: JGCT is rare, and has characteristic clinical manifestations. Diagnosis should be suspected in case of secondary hypertension, particularly in young women, if no renal vascular cause was found. Ultrasound, combined with clinical manifestations, was helpful for the diagnosis.

Ultrasound microflow patterns help in distinguishing malignant from benign thyroid nodules.

BACKGROUND: Vascular features are not commonly used to evaluate thyroid nodules by conventional ultrasound due to the low sensitivity. Superb Microvascular Imaging (SMI) is a new ultrasonic Doppler technology that specializes in depicting microvessels and low-speed flow. The objective of this study was to explore the value of microflow features on SMI in differentiating malignant from benign thyroid nodules. METHODS: One hundred and seventy-seven adult patients with thyroid nodules in our center from October 2021 to June 2022 with available histopathological results were recruited, including 125 malignant nodules and 123 benign nodules. Preoperative ultrasound was performed using greyscale, Color Doppler Flow Imaging (CDFI), monochrome SMI (mSMI) and color SMI (cSMI). Vascular features such as flow richness, microflow distribution and microflow patterns of malignant thyroid nodules were compared with those of benign nodules. RESULTS: Penetrating vessel ≥ 1 (82.4% in the malignant group vs. 30.9% in the benign group, P < 0.001), the crab claw-like pattern (64.0% vs. 10.6%, P < 0.001) and the root hair-like pattern (8.0% vs. 2.4%, P = 0.049) were common in malignant thyroid nodules, among which the crab claw-like pattern was an independent risk factor for malignant thyroid nodules. The wheel-like pattern (1.6% in the malignant group vs. 33.3% in the benign group, P < 0.001) and the arborescent pattern (0 vs. 19.5%, P < 0.001) were more likely to appear in benign nodules. The diagnostic specificities of the crab claw-like pattern and the root hair-like pattern for malignant thyroid nodules were 0.894, 0.976, and the positive predictive values were 0.860, 0.769. The diagnostic specificities of the wheel-like pattern and the arborescent pattern for benign thyroid nodules were 0.984, 1.000, and the positive predictive values were 0.953, 1.000. CONCLUSIONS: The crab claw-like pattern and the root hair-like pattern were microflow characteristics of malignant thyroid nodules. The wheel-like pattern and the arborescent pattern could help exclude the diagnosis of thyroid cancer.

Deep learning-based automatic scoring models for the disease activity of rheumatoid arthritis based on multimodal ultrasound images.

OBJECTIVES: We aimed to investigate the value of deep learning (DL) models based on multimodal ultrasonographic (US) images to quantify RA activity. METHODS: Static greyscale (SGS), dynamic greyscale (DGS), static power Doppler (SPD) and dynamic power Doppler (DPD) US images were collected and evaluated by two expert radiologists according to the EULAR-OMERACT Synovitis Scoring system. Four DL models were developed based on the ResNet-type structure, evaluated on two separate test cohorts, and finally compared with the performance of 12 radiologists with different levels of experience. RESULTS: In total, 1244 images were used for the model training, and 152 and 354 for testing (cohort 1 and 2, respectively). The best-performing models for the scores of 0/1/2/3 were the DPD, SGS, DGS and SPD models, respectively (Area Under the receiver operating characteristic Curve [AUC] = 0.87/0.95/0.74/0.95; no significant differences). All the DL models provided results comparable to the experienced radiologists on a per-image basis (intraclass correlation coefficient: 0.239-0.756, P < 0.05). The SPD model performed better than the SGS one on test cohort 1 (score of 0/2/3: AUC = 0.82/0.67/0.95 vs 0.66/0.66/0.75, respectively) and test cohort 2 (score of 0: AUC = 0.89 vs 0.81). The dynamic DL models performed better than the static ones in most of the scoring processes and were more accurate than the most of senior radiologists, especially the DPD model. CONCLUSION: DL models based on multimodal US images allow a quantitative and objective assessment of RA activity. Dynamic DL models in particular have potential value in assisting radiologists to improve the accuracy of RA US-based grading.

Localization of Ectopic Hyperparathyroidism: Ultrasound Versus 99mTc-sestamibi, 4-Dimensional Computed Tomography, and 11C-choline Positron Emission Tomography/Computed Tomography.

OBJECTIVE: To investigate the usefulness of ultrasound (US) for the localization of ectopic hyperparathyroidism and compare it with 99mTc-sestamibi (99mTc-MIBI), 4-dimensional computed tomography (4D-CT), and 11C-choline positron emission tomography/ computed tomography (PET/CT). METHODS: Of the 527 patients with surgically confirmed primary hyperparathyroidism, 79 patients with ectopic hyperparathyroidism were enrolled. The diagnostic performance of US, 99mTc-MIBI, US + MIBI, 4D-CT, and 11C-choline PET/CT was calculated, and the factors affecting the sensitivity of US and 99mTc-MIBI were analyzed. RESULTS: Eighty-three ectopic parathyroid lesions were found in 79 patients. The sensitivity was 75.9%, 81.7%, 95.1%, 83.3%, and 100% for US, 99mTc-MIBI, US + MIBI, 4D-CT, and 11C-choline PET/CT, respectively. The difference in sensitivity among these different modalities did not achieve statistical significance (P > .05). The US sensitivity was significantly higher for ectopic lesions in the neck region than for those in the anterior mediastinum/chest wall (85.9% vs. 42.1%, P < .001). The 99mTc-MIBI and 4D-CT sensitivity was not significantly different between these two groups (84.1% vs. 94.6%, P = .193 and 81.3% vs. 85.7%, P = 1). The 11C-choline PET/CT sensitivity was 100% in both groups. CONCLUSIONS: US is a valuable tool for the localization of ectopic hyperparathyroidism, especially for ectopic lesions in the neck region.

Identification of novel serum protein biomarkers in the context of 3P medicine for intravenous leiomyomatosis: a data-independent acquisition mass spectrometry-based proteomics study.

Background: Intravenous leiomyomatosis (IVL) is a rare endocrine-associated tumor with unique characteristics of intravascular invasion. This study aimed to identify reliable biomarkers to supervise the development or recurrence of IVL in the context of predictive, preventive, and personalized medicine (PPPM/3PM). Methods: A total of 60 cases were recruited to detect differentially expressed proteins (DEPs) in serum samples from IVL patients. These cases included those with recurrent IVL, non-recurrent IVL, uterine myoma, and healthy individuals without uterine myoma, with 15 cases in each category. Then, weighted gene co-expression network analysis (WGCNA), lasso-penalized Cox regression analysis (Lasso), trend clustering, and a generalized linear regression model (GLM) were utilized to screen the hub proteins involved in IVL progression. Results: First, 93 differentially expressed proteins (DEPs) were determined from 2582 recognizable proteins, with 54 proteins augmented in the IVL group, and the remaining proteins declined. These proteins were enriched in the modulation of the immune environment, mainly by activating the function of B cells. After the integrated analyses mentioned above, a model based on four proteins (A0A5C2FUE5, A0A5C2GPQ1, A0A5C2GNC7, and A0A5C2GBR3) was developed to efficiently determine the potential of IVL lesions to progress. Among these featured proteins, our results demonstrated that the risk factor A0A5C2FUE5 was associated with IVL progression (OR = 2.64). Conversely, A0A5C2GPQ1, A0A5C2GNC7, and A0A5C2GBR3 might act in a protective manner and prevent disease development (OR = 0.32, 0.60, 0.53, respectively), which was further supported by the multi-class receiver operator characteristic curve analysis. Conclusion: Four hub proteins were eventually identified based on the integrated bioinformatics analyses. This study potentiates the promising application of these novel biomarkers to predict the prognosis or progression of IVL by a 3PM approach. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00338-0.

Development of Quality Indicators for the Ultrasound Department through a Modified Delphi Method.

This study aims to establish precise quality indicators for evaluating and enhancing ultrasound performance, employing a methodology based on a comprehensive review of the literature, expert insights, and practical application experiences. We conducted a thorough review of both the domestic and international literature on ultrasound quality control to identify potential indicators. A dedicated team was formed to oversee the complete indicator development process. Utilizing a three-round modified Delphi method, we sought expert opinions through personalized email correspondence. Subsequently, data from diverse hospital indicators were collected to validate and assess feasibility. A novel set of seven indicators was compiled initially, followed by the convening of a 36-member nationally representative expert panel. After three rounds of meticulous revisions, consensus was reached on 13 indicators across three domains. These finalized indicators underwent application in various hospital settings, demonstrating their initial validity and feasibility. The development of thirteen ultrasound quality indicators represents a significant milestone in evaluating ultrasound performance. These indicators empower hospitals to monitor changes in quality effectively, fostering efficient quality management practices.

Real-time carotid plaque recognition from dynamic ultrasound videos based on artificial neural network.

PURPOSE: Carotid ultrasound allows noninvasive assessment of vascular anatomy and function with real-time display. Based on the transfer learning method, a series of research results have been obtained on the optimal image recognition and analysis of static images. However, for carotid plaque recognition, there are high requirements for self-developed algorithms in real-time ultrasound detection. This study aims to establish an automatic recognition system, Be Easy to Use (BETU), for the real-time and synchronous diagnosis of carotid plaque from ultrasound videos based on an artificial neural network. MATERIALS AND METHODS: 445 participants (mean age, 54.6±7.8 years; 227 men) were evaluated. Radiologists labeled a total of 3259 segmented ultrasound images from 445 videos with the diagnosis of carotid plaque, 2725 images were collected as a training dataset, and 554 images as a testing dataset. The automatic plaque recognition system BETU was established based on an artificial neural network, and remote application on a 5G environment was performed to test its diagnostic performance. RESULTS: The diagnostic accuracy of BETU (98.5%) was consistent with the radiologist's (Kappa = 0.967, P < 0.001). Remote diagnostic feedback based on BETU-processed ultrasound videos could be obtained in 150ms across a distance of 1023 km between the ultrasound/BETU station and the consultation workstation. CONCLUSION: Based on the good performance of BETU in real-time plaque recognition from ultrasound videos, 5G plus Artificial intelligence (AI)-assisted ultrasound real-time carotid plaque screening was achieved, and the diagnosis was made.

Validation of the diagnostic efficacy of O-RADS in adnexal masses.

The aim of this study was to validate the performance of the Ovarian-Adnexal Reporting and Data Systems (O-RADS) series models proposed by the American College of Radiology (ACR) in the preoperative diagnosis of adnexal masses (AMs). Two experienced sonologists examined 218 patients with AMs and gave the assessment results after the examination. Pathological findings were used as a reference standard. Of the 218 lesions, 166 were benign and 52 were malignant. Based on the receiver operating characteristic (ROC) curve, we defined a malignant lesion as O-RADS > 3 (i.e., lesions in O-RADS categories 4 and 5 were malignant). The area under the curve (AUC) of O-RADS (v2022) was 0.970 (95% CI 0.938-0.988), which wasn't statistically significantly different from the O-RADS (v1) combined Simple Rules Risk (SRR) assessment model with the largest AUC of 0.976 (95% CI 0.946-0.992) (p = 0.1534), but was significantly higher than the O-RADS (v1) (AUC = 0.959, p = 0.0133) and subjective assessment (AUC = 0.918, p = 0.0255). The O-RADS series models have good diagnostic performance for AMs. Where, O-RADS (v2022) has higher accuracy and specificity than O-RADS (v1). The accuracy and specificity of O-RADS (v1), however, can be further improved when combined with SRR assessment.

The performance of duplex ultrasonography for the assessment of renal artery stenosis in Takayasu's arteritis patients.

BACKGROUND: This study intends to analyze the hemodynamic parameters of the renal artery in patients with Takayasu's arteritis (TAK) to explore the diagnostic efficacy of duplex ultrasonography in assessing the involved renal artery in TAK patients. METHODS: One hundred fifteen TAK patients with 314 renal arteries were retrospectively analyzed, who were admitted to Peking Union Medical College Hospital between 2017 and 2022. These patients underwent both renal artery ultrasonography and angiography within a 4-week period. Specifically, the study compared seven ultrasonic parameters across groups categorized by the severity of renal artery stenosis (RAS), including noninvolvement, < 50% stenosis, 50-69% stenosis, and 70-99% stenosis. Receiver operating characteristic (ROC) curves were employed to determine the optimal threshold values for renal artery peak systolic velocity (RPSV), renal-aortic PSV ratio (RAR), and renal-interlobar PSV ratio (RIR) in order to diagnose various degrees of RAS in TAK patients. RESULTS: Statistically significant differences were observed in RAR and RIR among the four groups (all P < 0.05). However, no statistically significant differences were found in RPSV and AT between the moderate stenosis group (50-69% stenosis) and the severe stenosis group (70-99% stenosis). The discrimination of interlobar PSV (IPSV) and interlobar RI (IRI) was not significant, and IEDV did not show statistical significance among the four groups. For TAK patients, the recommended thresholds of RPSV for the diagnosis of renal artery involvement (RAI), ≥ 50% RAS, and ≥ 70% RAS were determined to be 143 cm/s, 152 cm/s, and 183 cm/s, respectively. The sensitivities, specificities, and accuracies of these thresholds were all found to be greater than 80%. Additionally, the optimal thresholds of RIR for detecting RAI, ≥ 50% RAS, and ≥ 70% RAS were determined to be 4.6, 5.6, and 6.4, respectively, with satisfactory diagnostic efficiencies. The areas under the curve (AUCs) for RPSV and RIR were calculated to be 0.908 and 0.910, respectively, for the diagnosis of ≥ 50% RAS, and 0.876 and 0.882 for the diagnosis of ≥ 70% RAS. When the aortic PSV is greater than or equal to 140 cm/s, the RAR exhibits inadequate diagnostic efficacy. Conversely, when the aortic PSV is less than 140 cm/s, a RAR value of 2.2 or higher can be employed as the diagnostic threshold for identifying RAS of 70% or greater, with a sensitivity of 84.00%, specificity of 89.93%, and an overall accuracy of 89.08%. CONCLUSION: In the present study, it has been demonstrated that RPSV and RIR possess substantial diagnostic value as ultrasonic parameters for diagnosing RAS in TAK patients. Furthermore, when assessing the diagnostic efficacy of RAR, it is crucial to consider the severity of aortic stenosis as a determining factor.

Diagnostic experience of intravenous leiomyomatosis with emphasis on conventional ultrasonography imaging: a single-center study.

Objective: Intravenous leiomyomatosis (IVL) is a rare and aggressive tumor type that has the potential to extend into the inferior vena cava (IVC) and is susceptible to be misdiagnosed and neglected. Despite its clinical significance, there is a paucity of research that has focused on the specific manifestations of IVL on ultrasonography. Therefore, this study aims to systematically analyze the specific ultrasound features of IVL and augment its diagnostic accuracy. Materials and method: Prospective inclusion was granted to patients admitted to our hospital between December 2016 and March 2021 for an IVC-occupying lesion. Multi-modal ultrasonography, encompassing gray-scale and color Doppler, was conducted. Lesions were categorized as IVL or non-IVL based on pathological or follow-up data. Two ultrasound sonographers with over 5 years of experience read and recorded ultrasound data for all lesions, which were subsequently comparatively analyzed to identify specific signs of IVL. Results: A total of 284 patients diagnosed with IVC-occupying lesions were included in the study. The lesion types comprised of IVL (n=67, 23.6%), IVC thrombus (n=135, 47.5%), tumor thrombus of renal carcinoma involving the IVC (n=35, 12.4%), tumor thrombus of liver carcinoma involving the IVC (n=24, 8.5%), leiomyosarcoma of the IVC (n=14, 4.9%), and tumor thrombus of adrenocortical adenocarcinoma (n=9, 4.1%). The presence of "sieve hole" and "multi-track" signs was observed in 20 IVL lesions under the grey-scale modality, while both signs were absent in the non-IVL group (P<0.01). The study found no statistically significant differences in the presentation of "sieve hole" and "multi-track" signs under the grey-scale and color Doppler modalities in cases of intravascular lithotripsy (IVL) (P>0.05). Using these two signs as diagnostic criteria for IVL, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), miss rate, misdiagnosis rate, and accuracy were determined to be 29.9%, 100%, 100%, 82.2%, 70.1%, 0, and 83.5%, respectively (AUC ROC=0.649; 95%CI: 0.537-0.761). Conclusion: IVL exhibits distinct ultrasound presentations, including "sieve hole" and "multi-track" signs, which demonstrate high specificity and accuracy as diagnostic indicators. Furthermore, these signs are corroborated by pathological evidence and effectively distinguish IVL from other lesions occupying the IVC.

Effect of differences in O-RADS lexicon interpretation between senior and junior sonologists on O-RADS classification and diagnostic performance.

PURPOSE: To assess the consistency of Ovarian-Adnexal Reporting and Data System (O-RADS) lexicon interpretation between senior and junior sonologists and to investigate its impact on O-RADS classification and diagnostic performance. METHODS: We prospectively studied 620 patients with adnexal lesions, all of whom underwent transvaginal or transrectal ultrasound performed by a senior sonologist (R1) who selected the O-RADS lexicon description and O-RADS category for the lesion after the examination. Meanwhile, the junior sonologist (R2) analyzed the images retained by R1 and divided the lesion in the same way. Pathological findings were used as a reference standard. kappa (к) statistics were used to assess the interobserver agreement. RESULTS: Of the 620 adnexal lesions, 532 were benign and 88 were malignant. When using the O-RADS lexicon, R1 and R2 had almost perfect agreement regarding lesion category, external contour of solid lesions, presence of papillary inside cystic lesions, and fluid echogenicity (к: 0.81-1.00). Substantial agreement in solid components, acoustic shadow, vascularity and O-RADS categories (к: 0.61-0.80). Consistency in classifying classic benign lesions in the O-RADS category was only moderate (к = 0.535). No significant difference in diagnostic performance between them using O-RADS (P = 0.1211). CONCLUSION: There was good agreement between senior and junior sonologists in the interpretation of the O-RADS lexicon and in the classification of O-RADS, except for a moderate agreement in the interpretation and classification of classic benign lesions. Differences in O-RADS category delineation between sonologists had no significant effect on the diagnostic performance of O-RADS.

Pancreatic ductal adenocarcinoma with synchronous and metachronous hepatic metastasis predicted by contrast-enhanced ultrasound.

Background: Contrast-enhanced ultrasound (CEUS) has proven valuable in diagnosing benign and malignant pancreatic diseases, but its value in evaluating hepatic metastasis remains to be further explored. This study investigated the relationship between CEUS features of pancreatic ductal adenocarcinoma (PDAC) and concomitant or recurrent liver metastases after treatment. Methods: This retrospective study included 133 participants with PDAC who were diagnosed with pancreatic lesions with CEUS at Peking Union Medical College Hospital from January 2017 to November 2020. According to the CEUS classification methods in our center, all the pancreatic lesions were classified as either with rich or poor blood supply. Additionally, quantitative ultrasonographic parameters were measured in the center and periphery of all pancreatic lesions. CEUS modes and parameters of the different hepatic metastasis groups were compared. The diagnostic performance of CEUS was calculated for diagnosing synchronous and metachronous hepatic metastasis. Results: The proportions of rich blood supply and poor blood supply were 46% (32/69) and 54% (37/69), respectively, in the no hepatic metastasis group; 42% (14/33) and 58% (19/33), respectively, in the metachronous hepatic metastasis (MHM) group; and 19% (6/31) and 81% (25/31), respectively, in the synchronous hepatic metastasis (SHM) group. The wash-in slope ratio (WIS ratio) between the center of the lesion and around the lesion and peak intensity ratio (PI ratio) between the center of the lesion and around the lesion had higher values in the negative hepatic metastasis group (P<0.05). In predicting synchronous and metachronous hepatic metastasis, the WIS ratio had the best diagnostic performance. The sensitivity (SEN), specificity (SPE), accuracy (ACC), positive predictive value (PPV), and negative predictive value (NPV) were 81.8%, 95.7%, 91.2%, 90.0%, and 91.7%, respectively, for MHM; and 87.1%, 95.7%, 93.0%, 90.0%, and 94.3%, respectively, for SHM. Conclusions: CEUS would be helpful in image surveillance for synchronous or metachronous hepatic metastasis of PDAC.

Predicting multigland disease in primary hyperparathyroidism using ultrasound and clinical features.

Background: The identification of multigland disease (MGD) in primary hyperparathyroidism (PHPT) patients is essential for minimally invasive surgical decision-making. Objective: To develop a nomogram based on ultrasound (US) findings and clinical factors to predict MGD in PHPT patients. Materials and methods: Patients with PHPT who had surgery between March 2021 and January 2022 were consecutively enrolled to this study. Biochemical and clinicopathological data were recorded. US images were analyzed to extract US features for prediction. Logistic regression analyses were used to identify MGD risk factors. A nomogram was constructed based on these factors and its performance evaluated by area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer-Lemeshow tests, and decision curve analysis (DCA). Results: A total of 102 PHPT patients were included; 82 (80.4%) had single-gland disease (SGD) and 20 (19.6%) had MGD. Using multivariate analyses, MGD was positively correlated with age (odds ratio (OR) = 1.033, 95% confidence interval (CI): 0.190-4.047), PTH levels (OR = 1.001, 95% CI: 1.000-1.002), multiple endocrine neoplasia type 1 (MEN1) (OR = 29.730, 95% CI: 3.089-836.785), US size (OR = 1.198, 95% CI: 0.647-2.088), and US texture (cystic-solid) (OR = 5.357, 95% CI: 0.499-62.912). MGD was negatively correlated with gender (OR = 0.985, 95% CI: 0.190-4.047), calcium levels (OR = 0.453, 95% CI: 0.070-2.448), and symptoms (yes) (OR = 0.935, 95% CI: 0.257-13.365). The nomogram showed good discrimination with an AUC = 0.77 (0.68-0.85) and good agreement in predicting MGD in PHPT patients. Also, 65 points was recommended as a cut-off value, with specificity = 0.94 and sensitivity = 0.50. Conclusion: US was useful in evaluating MGD. Combining US and clinical features in a nomogram showed good diagnostic performance for predicting MGD.

[Performance of Contrast-enhanced Ultrasound Liver Imaging Reporting and Data System LR-5 in the Diagnosis of Hepatocellular Carcinoma:A Meta-analysis].

Objective To evaluate the performance of contrast-enhanced ultrasound (CEUS) liver imaging reporting and data system (LI-RADS) LR-5 in the diagnosis of hepatocellular carcinoma (HCC). Methods The clinical research reports with the application of CEUS LI-RADS in the diagnosis of HCC were collected from PubMed,Embase,Cochrane Library,CNKI,and Wanfang Data from inception to November 14,2021.Two researchers respectively screened the literature and extracted relevant information.The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) was used to evaluate the quality of all the included articles.RevMan 5.4,Meta disc 1.4,and Stata 16.0 were employed to analyze the diagnostic performance of LR-5 for HCC in high-risk patients. Results Twenty original studies were included,involving a total of 6131 lesions,of which 5142 were HCC.The results of meta-analysis showed that the LR-5 in CEUS LI-RADS for diagnosing HCC in the high-risk population had the overall sensitivity of 0.72 (95%CI=0.66-0.77),the overall specificity of 0.93 (95%CI=0.87-0.96),the overall positive likelihood ratio of 9.89 (95%CI=5.31-18.41),the overall negative likelihood ratio of 0.30 (95%CI=0.25-0.37),and the area under the summary receiver operating characteristic curve of 0.88 (95%CI=0.85-0.91).There was heterogeneity among the included studies (I2=95.31,P<0.001).The funnel plot indicated the existence of publication bias (P=0.04). Conclusion The CEUS LI-RADS can effectively diagnose HCC in high-risk patients based on the LR-5 criteria.

Intraoperative shear-wave elastography and superb microvascular imaging contribute to the glioma grading.

BACKGROUND: To explore the diagnostic value and feasibility of shear wave elastography and superb microvascular imaging in the grading diagnosis of glioma intraoperatively. MATERIALS AND METHODS: Forty-nine patients with glioma were included in this study. B-mode ultrasonography, Young's modulus in shear-wave elastography (SWE) and vascular architecture in superb microvascular imaging(SMI) of tumor tissue and peritumoral tissue were analyzed. Receiver operating characteristic(ROC) curve analysis was used to evaluate the diagnostic effect of SWE. Logistic regression model was used to calculate the prediction probability of HGG diagnosis. RESULTS: Compared with LGG, HGG was often characterized by peritumoral edema in B mode (P < 0.05). There was a significant difference in Young's modulus between HGG and LGG; the diagnostic threshold of HGG and LGG was 13.05 kPa, the sensitivity was 78.3%, and the specificity was 76.9%. The vascular architectures of the tumor tissue and peritumoral tissues of HGG and LGG were significantly different (P < 0.05). The vascular architectures of peritumoral tissue in HGG often characterized by distorted blood flow signals surrounding the tumor (14/26,53.8%); in the tumor tissue, HGG often presents as dilated and bent vessels(19/26,73.1%). The elasticity value of SWE and the tumor vascular architectures of SMI were correlated with the diagnosis of HGG. CONCLUSION: Intraoperative ultrasound (ioUS), especially SWE, and SMI are beneficial for the differentiation of HGG and LGG and may help optimize clinical surgical procedures.