Dual concentric echo sign of ultrasound in primary hyperparathyroidism: The clinical and histopathologic features and differentiation from lymph nodes.

Context: Ultrasound (US) is the most economical and widely used method for detecting lesions in parathyroid regions. Identifying typically parathyroid adenomas as hypoechoic nodules with clear margins. However, 10 % of lesions exhibit atypical features, such as the dual concentric sign, and the cognition of them still needs to be improved. Objective: To promote understanding of clinical and histopathological features for parathyroid lesions with the dual concentric echo sign and to investigate its pathogenesis and methods for distinguishing from cervical lymph nodes to improve US diagnostic accuracy. Methods: Retrospectively, patients were categorized into three groups: Group 1, with 36 patients showing parathyroid lesions with dual concentric echo signs; Group 2, with 40 patients displaying classic hypoechoic parathyroid lesions; and Group 3, comprising 36 patients with identified lymph nodes, which were all examined from January 2018 to December 2019. The clinical data on demographics, clinical symptoms, serum levels, histopathologic findings, and US image characteristics were thoroughly reviewed. Results: According to the clinical data, no significant differences in demographics or lesion sizes were observed in Group 1 and Group 2 (p > 0.05). No significant variances were noted in biochemical markers, including PTH, T-25OHD, and ALP. However, a notable difference was identified in adjusted serum calcium levels, which were significantly lower in Group 1 compared to Group 2 (p < 0.05). Additionally, the proportion of asymptomatic patients was significantly higher in Group 1 compared to Group 2 (p < 0.05). Pathological examination revealed that all lesions with dual concentric echo signs were parathyroid adenomas. The isoechoic central region predominantly corresponded to areas of loose edema, while the hypoechoic peripheral layer was primarily associated with chief and/or oncocytic cells. By comparing the ultrasonography of Groups 1 and 3, the parathyroid lesions with dual concentric echo signs exhibited significant distinctions from lymph nodes in size, blood flow classification, vascular distribution, and anatomical location (p < 0.05). Conclusion: The parathyroid lesions with dual concentric echo signs in US corresponded to specific histopathological manifestations and relatively mild clinical features in the patients, this finding may increase the likelihood of incidental detection of parathyroid lesions by US. Attention to the details of size, location, and blood flow, especially, may aid US physicians in differentiating parathyroid adenomas from cervical lymph nodes.

Imaging findings of Budd-Chiari syndrome caused by intravenous leiomyomatosis: a case report.

Intravenous leiomyomatosis (IVL) is a rare gynecological-related tumor. It can invade and extend in the blood vessel and eventually involve the cardiac cavity or even the pulmonary artery. IVL generally does not adhere to the vein wall and infrequently leads to the manifestation of Budd-Chiari syndrome (BCS). In this case report, the presence of a sizable tumor obstructed the second hepatic portal, impeding the return flow of the hepatic veins, thereby precipitating the development of BCS. The presence of collateral veins and dilation of the accessory hepatic vein were identified through computed tomography venography and ultrasonography, thus supporting the diagnosis of BCS. The patient underwent a comprehensive surgical resection and was found to have a favorable prognosis.

Artificial intelligence assisted ultrasound for the non-invasive prediction of axillary lymph node metastasis in breast cancer.

PURPOSE: A practical noninvasive method is needed to identify lymph node (LN) status in breast cancer patients diagnosed with a suspicious axillary lymph node (ALN) at ultrasound but a negative clinical physical examination. To predict ALN metastasis effectively and noninvasively, we developed an artificial intelligence-assisted ultrasound system and validated it in a retrospective study. METHODS: A total of 266 patients treated with sentinel LN biopsy and ALN dissection at Peking Union Medical College & Hospital(PUMCH) between the year 2017 and 2019 were assigned to training, validation and test sets (8:1:1). A deep learning model architecture named DeepLabV3 + was used together with ResNet-101 as the backbone network to create an ultrasound image segmentation diagnosis model. Subsequently, the segmented images are classified by a Convolutional Neural Network to predict ALN metastasis. RESULTS: The area under the receiver operating characteristic curve of the model for identifying metastasis was 0.799 (95% CI: 0.514-1.000), with good end-to-end classification accuracy of 0.889 (95% CI: 0.741-1.000). Moreover, the specificity and positive predictive value of this model was 100%, providing high accuracy for clinical diagnosis. CONCLUSION: This model can be a direct and reliable tool for the evaluation of individual LN status. Our study focuses on predicting ALN metastasis by radiomic analysis, which can be used to guide further treatment planning in breast cancer.

Plasma metabolomics signatures of developmental dysplasia of the hip in Tibet plateau.

BACKGROUND: Developmental dysplasia of the hip (DDH) is a common childhood health complaint, whose etiology is multifactorial. The incidence of DDH is variable and higher in Tibet plateau. Here, we collected plasma samples and studied the metabolomics signatures of DDH. METHODS: Fifty babies were enrolled: 25 with DDH and 25 age-matched non-DDH healthy controls (HC group). We collected plasma samples, laboratory parameters and conducted untargeted metabolomics profiling. RESULTS: There are many differential metabolites among patients with DDH, including 4-ß-hydroxymethyl-4-α-methyl-5-α-cholest-7-en-3-beta-ol, ß-cryptoxanthin, α-tocopherol, taurocholic acid, glycocholic acid, 2-(3,4-dihydroxybenzoyloxy)-4,6-dihydroxybenzoate, arabinosylhypoxanthine, leucyl-hydroxyproline, hypoxanthine. The main differential metabolic pathways focused on primary bile acid biosynthesis, arginine and proline metabolism, phenylalanine metabolism, histidine metabolism, purine metabolism. CONCLUSIONS: To our knowledge, this is the first report of metabolomics profile in babies with DHH. By combining the α-tocopherol and taurocholic acid, we could achieve the differential diagnosis of DDH.

Clinical application of intraoperative ultrasound superb microvascular imaging in brain tumors resections: contributing to the achievement of total tumoral resection.

BACKGROUND: To investigate whether the intraoperative superb microvascular imaging(SMI) technique helps evaluate lesion boundaries compared with conventional grayscale ultrasound in brain tumor surgery and to explore factors that may be associated with complete radiographic resection. METHODS: This study enrolled 57 consecutive brain tumor patients undergoing surgery. During the operation, B-mode and SMI ultrasound evaluated the boundaries of brain tumors. MRI before and within 48h after surgery was used as the gold standard to evaluate gross-total resection(GTR). The ultrasound findings and GTR results were analyzed to determine the imaging factors related to GTR. RESULTS: A total of 57 patients were enrolled in the study, including 32 males and 25 females, with an average age of 53.4 ± 14.1 years old(range 19 ~ 80). According to the assessment criteria of MRI, before and within 48 h after the operation, 37(63.9%) cases were classified as GTR, and 20(35.1%) cases were classified as GTR. In comparing tumor interface definition between B-mode and SMI mode, SMI improved HGG boundary recognition in 5 cases(P = 0.033). The results showed that the tumor size ≥ 5 cm and unclear ultrasonic boundary were independent risk factors for nGTR (OR>1, P<0.05). CONCLUSIONS: As an innovative intraoperative doppler technique in neurosurgery, SMI can effectively demarcate the tumor's boundary and help achieve GTR as much as possible.

Deep-learning-based sampling position selection on color Doppler sonography images during renal artery ultrasound scanning.

Accurate selection of sampling positions is critical in renal artery ultrasound examinations, and the potential of utilizing deep learning (DL) for assisting in this selection has not been previously evaluated. This study aimed to evaluate the effectiveness of DL object detection technology applied to color Doppler sonography (CDS) images in assisting sampling position selection. A total of 2004 patients who underwent renal artery ultrasound examinations were included in the study. CDS images from these patients were categorized into four groups based on the scanning position: abdominal aorta (AO), normal renal artery (NRA), renal artery stenosis (RAS), and intrarenal interlobular artery (IRA). Seven object detection models, including three two-stage models (Faster R-CNN, Cascade R-CNN, and Double Head R-CNN) and four one-stage models (RetinaNet, YOLOv3, FoveaBox, and Deformable DETR), were trained to predict the sampling position, and their predictive accuracies were compared. The Double Head R-CNN model exhibited significantly higher average accuracies on both parameter optimization and validation datasets (89.3 ± 0.6% and 88.5 ± 0.3%, respectively) compared to other methods. On clinical validation data, the predictive accuracies of the Double Head R-CNN model for all four types of images were significantly higher than those of the other methods. The DL object detection model shows promise in assisting inexperienced physicians in improving the accuracy of sampling position selection during renal artery ultrasound examinations.

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.

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.

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.

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.