Ultrasonography Imaging Research Articles

Use of ultrasonography in the diagnosis of pneumonia in intensive care patients

Aims: A significant portion of respiratory system infections seen in intensive care units is ventilator-associated pneumonia, which has a high mortality rate and diagnosis may be delayed. Bedside lung ultrasonography offers the advantages of standard diagnostic methods as chest radiography or thorax tomography. We aim to evaluate the bedside lung ultrasonography correlation with the chest radiography or thorax tomography Methods: It was conducted prospectively on 60 patients between the ages of 18-85 who were admitted to intensive care with respiratory failure within an 8-month period. Anterioposterior chest radiographs were taken on the first day of mechanical ventilation for the patients included in the study. Ultrasonographic (USG) examination was performed separately for both hemithoraxes and recorded digitally. Simultaneously with the ultrasonographic examination, PEEP, FiO2, blood gas examination results and the highest body temperatures in the last 24 hours were recorded. The quality and quantity of tracheal secretions were evaluated, and sampling was performed for complete blood examination and tracheal aspirate culture-antibiogram. Lung injury score (LIS) and pulmonary infection score (CPIS) were calculated from the data obtained. In this process, independent of the research, the same day examination results of patients who required thorax tomography (CT) for diagnosis and treatment planning were evaluated together with USG and chest radiography. Tomographic examinations were evaluated by a radiologist. Results: In the examination of 120 hemithoraxes of 60 patients we included in the study, we were able to detect 74.17% of the pathological images obtained with chest radiography and ultrasonographic examination with bedside ultrasonography and 70.84% with chest radiography. Of the 20 patients who underwent tomographic evaluation, pathology was observed in 40 hemithoraxes in 92.5% by tomography, in 85% by ultrasonography, and in 75% by chest radiography. While isolated consolidation was detected equally on USG and CT imaging, there was a high false-positive rate of 48% on chest radiography. 58.8% of the patients with consolidation detected by CT, we observed that there was growth in the tracheal aspirate of 56.2% of the patients whose ultrasonographic examination revealed consolidation. Conclusion: Bedside lung ultrasonography is a fast, non-invasive, repeatable and reliable diagnostic method in patients followed in intensive care, and is compatible with traditionally used laboratory and clinical parameters. This reveals that USG, which is known to have other advantages such as cost, applicability, and not using radiopaque, will become an indispensable examination for ICU.

Compression neuropathy caused by a lipoma in the fourth finger: a case report

Lipomas are benign tumors originating from adipocytes, and less than 1% of lipomas occur in the fingers. While most lipomas can be managed with observation, those occurring in the fingers can cause symptoms and functional impairment due to the proximity of the subcutaneous fat layer to nerves and blood vessels. Herein, the authors report a case of a patient who experienced finger numbness due to a lipoma extending from the fourth proximal phalanx to the middle phalanx. In cases like this, where initial symptoms are present in the finger but the mass is not palpable, it is essential to actively pursue a diagnosis using ultrasonography or magnetic resonance imaging. Also, in cases of lipomas causing symptoms and functional impairment, early removal may be beneficial for alleviating the patient's symptoms.

Contrast-Enhanced Ultrasound Compared With Hepatobiliary Agent MRI for Differentiation of Focal Nodular Hyperplasia and Hepatic Adenoma: A Prospective Trial.

Magnetic resonance imaging (MRI) may be time-consuming, expensive, or poorly tolerated by patients with liver lesions. This is a prospective clinical trial designed to evaluate if contrast-enhanced ultrasound (CEUS) can be used to differentiate focal nodular hyperplasia (FNH) from hepatocellular adenoma (HCA) with similar accuracy compared with hepatobiliary agent MRI.Institutional review board approval was obtained (1805450097), and the trial was registered with ClinicalTrials.gov (NCT03652636). From 2018 through 2023, 40 patients who had lesions consistent with FNH or HCA on HBA-MRI underwent 1-time prospective CEUS of up to 2 hepatic lesions. Sonographic images obtained before and after intravenous administration of 2 mL sulfur hexafluoride lipid-type A microspheres (Lumason) per lesion totaling 59 lesions (27 FNHs/32 HCAs). Two blinded radiologists provided a diagnosis of FNH or HCA.Thirty-eight female and 2 male patients (age 36.7 ± 9.9) were scanned. Radiologists provided diagnosis of FNH or HCA with respective sensitivity (66.7/64.0%), specificity (71.9/90.6%), and accuracy (69.5/78.0%). For 38 lesions greater than or equal to 2 cm in size (17 FNHs/21 HCAs), readers had sensitivity (70.6/84.2%), specificity (70.6/84.2%), and accuracy (81.5/86.8%). Interobserver agreement for all lesions was fair (κ = 0.34), whereas agreement for lesions 2 cm or larger was substantial (κ = 0.67).Contrast-enhanced ultrasound can differentiate FNH from HCA with accuracy approaching that of hepatobiliary agent MRI for lesions 2 cm or greater. Interobserver agreement is improved with larger lesions. CEUS may have utility as an alternate diagnostic tool for FNH/HCA, especially in patients who cannot or do not desire to undergo MRI.

Contrast-enhanced ultrasonography guidance avoids US-CT/MR fusion error for percutaneous radiofrequency ablation of hepatocellular carcinoma

BackgroundThis study evaluated the impact of contrast-enhanced ultrasonography (CEUS) combined with CT or MRI fusion imaging on percutaneous radiofrequency ablation (RFA) outcomes for hepatocellular carcinoma (HCC) inconspicuous on conventional ultrasonography (US).MethodsPatients were categorized into US-inconspicuous (USI) and US-conspicuous (USC) groups based on US imaging. The parameters of viable HCCs ⎯ including diameter, location, and RFA efficacy ⎯ were compared between USI and USC groups. Moreover, the breathing fusion imaging errors were measured. The differences in technical success, technical efficacy, local tumor progression, new tumor occurrence, and overall survival rate between USI and USC groups were analyzed.ResultsSixty-five patients with 106 lesions were included. CEUS showed high consistency with CT/MRI but revealed larger diameters (p < 0.001) and more feeding arteries (p = 0.019) than CT/MRI. Breathing fusion imaging errors averaged 17 ± 4 mm, significantly affecting lesions in segments II, III, V, and VI (p < 0.001). The USI group had more lesions ablated per patient in a single RFA procedure (p = 0.001) than the USC group. No significant differences were observed in technical success rate, technical efficacy rate, local tumor progression rate, and overall survival rate between the two groups.ConclusionsCEUS combined with fusion imaging provides detailed information on viable HCCs and their feeding arteries. CEUS-guided RFA avoids fusion imaging errors and achieves comparable efficacy in both US-conspicuous and US-inconspicuous HCCs.

Image Biomarker Analysis of Ultrasonography Images of the Parotid Gland for Baseline Characteristic Establishment with Reduced Shape Effects

Background: This study aimed to analyze image biomarkers of the parotid glands in ultrasonography images with reduced shape effects, providing a reference for the radiomic diagnosis of parotid gland lesions. Methods: Ultrasound (US) and sialography images of the parotid glands, acquired from September 2019 to March 2024, were reviewed along with their clinical information. Parotid glands diagnosed as within the normal range were included. Overall, 91 US images depicting the largest portion of the parotid glands were selected for radiomic feature extraction. Regions of interest were drawn twice on 50 images using different shapes to assess the intraclass correlation coefficient (ICC). Feature dimensions were statistically reduced by selecting features with an ICC &gt; 0.8 and applying four statistical algorithms. The selected features were used to distinguish age and sex using the four classification models. Classification performance was evaluated using the area under the receiver operating characteristic curve (AUC), recall, and precision. Results: The combinations of the information gain ratio algorithm or stochastic gradient descent and the naïve Bayes model showed the highest AUC for both age and sex classification (AUC = 1.000). The features contributing to these classifications included the first-order and gray-level co-occurrence matrix (high-order) features, particularly discretized intensity skewness and kurtosis, intensity skewness, and GLCM angular second moment. These features also contributed to achieving one of the highest recall (0.889) and precision (0.926) values. Conclusions: The two features were the most significant factors in discriminating radiomic variations related to age and sex in US images with reduced shape effects. These radiomic findings should be assessed when diagnosing parotid gland pathology versus normal using US images and radiomics in a heterogeneous population.

An ultrasonography of thyroid nodules dataset with pathological diagnosis annotation for deep learning

Ultrasonography (US) of thyroid nodules is often time consuming and may be inconsistent between observers, with a low positivity rate for malignancy in biopsies. Even after determining the ultrasound Thyroid Imaging Reporting and Data System (TIRADS) stage, Fine needle aspiration biopsy (FNAB) is still required to obtain a definitive diagnosis. Although various deep learning methods were developed in medical field, they tend to be trained using TI-RADS reports as image labels. Here, we present a large US dataset with pathological diagnosis annotation for each case, designed for developing deep learning algorithms to directly infer histological status from thyroid ultrasound images. The dataset was collected from two retrospective cohorts, which consists of 8508 US images from 842 cases. Additionally, we explained three deep learning models used as validation examples using this dataset.

Exploratory study on the enhancement of O-RADS application effectiveness for novice ultrasonographers via deep learning.

The study aimed to create a deep convolutional neural network (DCNN) model based on ConvNeXt-Tiny to identify classic benign lesions (CBL) from other lesions (OL) within the Ovarian-Adnexal Reporting and Data System (O-RADS), enhancing the system's utility for novice ultrasonographers. Two sets of sonographic images of pathologically confirmed adnexal lesions were retrospectively collected [development dataset (DD) and independent test dataset (ITD)]. The ConvNeXt-Tiny model, optimized through transfer learning, was trained on the DD using the original images directly and after automatic lesion segmentation by a U-Net model. Models derived from both training paradigms were validated on the ITD for sensitivity, specificity, accuracy, and area under the curve (AUC). Two novice ultrasonographers were assessed in O-RADS with and without assistance from the model for Application Effectiveness. The ConvNeXt-Tiny model trained on original images scored AUCs of 0.978 for DD and 0.955 for ITD, while the U-Net segmented image model achieved 0.967 for DD and 0.923 for ITD; neither showed significant differences. When assessing the malignancy of lesions using O-RADS 4 and 5, the diagnostic performances of two novice ultrasonographers and senior ultrasonographer, as well as model-assisted classifications, showed no significant differences, except for one novice's low accuracy. This approach reduced classification time by 62 and 64 min. The kappa values with senior doctors' classifications rose from 0.776 and 0.761 to 0.914 and 0.903, respectively. The ConvNeXt-Tiny model demonstrated excellent and stable performance in distinguishing CBL from OL within O-RADS. The diagnostic performance of novice ultrasonographers using O-RADS is essentially equivalent to that of senior ultrasonographer, and the assistance of the model can enhance their classification efficiency and consistency with the results of senior ultrasonographer.

Relationship between nerve ultrasonography image and electrophysiology in diabetic polyneuropathy.

This study aims to investigate the association between cross-sectional area (CSA) imaging findings of nerve ultrasound and conventional nerve conduction studies (NCS) for patients with distal symmetric sensorimotor polyneuropathy (DSPN) due to type 2 diabetes mellitus. We enrolled 103 patients with type 2 diabetes mellitus and collected their demographic data, modified Michigan Neuropathy Screening Instrument (mMNSI) score, NCS, and ultrasonography images of peripheral nerves. The relationship of ultrasound variables for individual nerves and the ultrasound pattern sum score (UPSS) to conventional NCS findings was investigated. A higher grade of DSPN was associated with a notably higher CSA. Multivariate step-wise regression analysis revealed that the number of abnormal nerves was a positive independent variable for UPSS (β coefficient = 0.4205; P < 0.0001). Of the five nerves studied, abnormalities of the tibial nerve (P ≤ 0.0100) and ulnar nerve (P = 0.052) were the most significant variables. The tibial nerve exhibited the most substantial association with elevated UPSS. In addition, a strong correlation was observed between abnormal NCS findings and UPSS in patients with DSPN.

Imaging comparative analysis of familial and sporadic gout in Chinese men by multijoint ultrasonography.

This study aimed to compare the imaging features of bilateral knees, ankles, and the first metatarsophalangeal joint using high-frequency ultrasonography in male patients with familial and sporadic primary gout and sought to elucidate the relationship between the presence of tophi and various clinical indicators. Male patients with primary gouty arthritis (GA) in the acute phase presenting to the Department of Rheumatology and Immunology at the First Affiliated Hospital of Chengdu Medical College from November 2020 to June 2022 were enrolled and classified into familial and sporadic gout groups. Comparative analyses of their clinical data and ultrasonographic imaging findings of the knees, ankles, and first metatarsophalangeal joints were performed between the groups. Univariate and multivariate logistic regression analyses, as well as receiver operating characteristic (ROC) analysis, were conducted to determine the effectiveness of significant factors in the prediction of tophi. In comparison to male patients with sporadic gout, those with familial primary gout exhibited lower age, body mass index, disease duration, and serum uric acid (SUA) levels. However, they demonstrated higher incidences of tophi and bone erosion (54.6% in familial gout vs. 35.1% in sporadic gout, p < 0.05; 71.2% in familial gout vs. 48.1% in sporadic gout, p < 0.05, respectively), with a greater prevalence of tophi in the right first metatarsophalangeal joint (44.2% in familial gout vs. 32.3% in sporadic gout, p < 0.05). Independent risk factors for tophi included family history (OR = 6.712), age (OR = 1.049), disease duration (OR = 1.134), and SUA levels (OR = 1.006). ROC analysis yielded an area under the curve of 0.883 (p < 0.05) for predicting joint tophi using these factors. Male patients with familial primary GA in the acute phase experienced earlier onset, shorter disease duration compared to those with sporadic gout. They also had more affected joints, more frequent and a wider distribution of tophi, especially in the right first metatarsophalangeal joint. Family history, age, disease duration, and SUA levels are predictive of tophi formation.

Sonographic localisation of lymph nodes suspicious of metastatic breast cancer to surgical axillary levels

AbstractThe axillary lymph node (LN) burden of breast cancer patients guides multidisciplinary management and treatment regimes. Sonographic imaging is used to identify the presence, number and location of axillary LNs suspicious of malignancy and used to guide nodal fine needle aspirations and biopsies. Axillary LNs suspicious of harbouring breast cancer metastasis can be localised to three surgical axillary levels, numbered according to their location relative to the pectoralis minor muscle and lymph flow. To sonographically identify and localise suspicious axillary LNs, an understanding of the axillary anatomy, muscular sonographic landmarks, surgical axillary levels, and the sonographic technique to image and distinguish between benign and suspicious LNs is required.

Imaging diagnosis of acute urinary retention caused by a congenital imperforate hymen: A case report

ObjectiveCongenital hymen atresia is a rare congenital malformation of the female genital tract. Due to its physiologic and anatomic characteristics, congenital hymen atresia is often challenging to detect and is typically associated with complications that necessitate medical evaluation. Case presentationWe present the case of a 12-year-old patient who developed acute urinary retention secondary to a congenital imperforate hymen. Both ultrasonography and magnetic resonance imaging (MRI) revealed the presence of congenital imperforate hymen accompanied by blood accumulation in the uterus and vagina, which contributed to the urinary retention. The patient underwent hymenotomy under general anesthesia. By the second postoperative day, the bladder had emptied successfully, and at an 8-month follow-up, menstruation and urination were normal. ConclusionThe ultrasonographic and MRI findings of congenital imperforate hymen exhibit specific diagnostic characteristics. While ultrasonography is the preferred initial diagnostic modality, MRI can enhance the detection rate and provide further clarity.

A comprehensive benchmarking of a U-Net based model for midbrain auto-segmentation on transcranial sonography

Background and objectiveTranscranial sonography-based grading of Parkinson's Disease has gained increasing attention in recent years, and it is currently used for assistive differential diagnosis in some specialized centers. To this end, accurate midbrain segmentation is considered an important initial step. However, current practice is manual, time-consuming, and bias-prone due to the subjective nature. Relevant studies in the literature are scarce and lacks comprehensive model evaluations from application perspectives. Herein, we aimed to benchmark the best-performing U-Net model for objective, stable and robust midbrain auto-segmentation using transcranial sonography images. MethodsA total of 584 patients who were suspected of Parkinson's Disease were retrospectively enrolled from Beijing Tiantan Hospital. The dataset was divided into training (n = 416), validation (n = 104), and testing (n = 64) sets. Three state-of-the-art deep-learning networks (U-Net, U-Net+++, and nnU-Net) were utilized to develop segmentation models, under 5-fold cross-validation and three randomization seeds for safeguarding model validity and stability. Model evaluation was conducted in testing set in three key aspects: (i) segmentation agreement using DICE coefficients (DICE), Intersection over Union (IoU), and Hausdorff Distance (HD); (ii) model stability using standard deviations of segmentation agreement metrics; (iii) prediction time efficiency, and (iv) model robustness against various degrees of ultrasound imaging noise produced by the salt-and-pepper noise and Gaussian noise. ResultsThe nnU-Net achieved the best segmentation agreement (averaged DICE: 0.910, IoU: 0.836, HD: 2.793-mm) and time efficiency (1.456-s). Under mild noise corruption, the nnU-Net outperformed others with averaged scores of DICE (0.904), IoU (0.827), HD (2.941 mm) in the salt-and-pepper noise (signal-to-noise ratio, SNR = 0.95), and DICE (0.906), IoU (0.830), HD (2.967 mm) in the Gaussian noise (sigma value, σ = 0.1); by contrast, intriguingly, performance of the U-Net and U-Net+++ models were remarkably degraded. Under increasing levels of simulated noise corruption (SNR decreased from 0.95 to 0.75; σ increased from 0.1 to 0.5), the nnU-Net network exhibited marginal decline in segmentation agreement meanwhile yielding decent performance as if there were absence of noise corruption. ConclusionsThe nnU-Net model was the best-performing midbrain segmentation model in terms of segmentation agreement, stability, time efficiency and robustness, providing the community with an objective, effective and automated alternative. Moving forward, a multi-center multi-vendor study is warranted when it comes to clinical implementation.

A Machine Learning Model Based on Thyroid US Radiomics to Discriminate Between Benign and Malignant Nodules.

Background/Objectives: Thyroid nodules are a very common finding, mostly benign but sometimes malignant, and thus require accurate diagnosis. Ultrasound and fine needle biopsy are the most widely used and reliable diagnostic methods to date, but they are sometimes limited in addressing benign from malignant nodules, mainly with regard to ultrasound, by the operator's experience. Radiomics, quantitative feature extraction from medical images and machine learning offer promising avenues to improve diagnosis. The aim of this work was to develop a machine learning model based on thyroid ultrasound images to classify nodules into benign and malignant classes. Methods: For this purpose, images of ultrasonography from 142 subjects were collected. Among these subjects, 40 patients (28.2%) belonged to the class "malignant" and 102 patients (71.8%) belonged to the class "benign", according to histological diagnosis from fine-needle aspiration. This image set was used for the training, cross-validation and internal testing of three different machine learning models. A robust radiomic approach was applied, under the hypothesis that the radiomic feature could capture the disease heterogeneity among the two groups. Three models consisting of four ensembles of machine learning classifiers (random forests, support vector machines and k-nearest neighbor classifiers) were developed for the binary classification task of interest. The best performing model was then externally tested on a cohort of 21 new patients. Results: The best model (ensemble of random forest) showed Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) (%) of 85 (majority vote), 83.7 ** (mean) [80.2-87.2], accuracy (%) of 83, 81.2 ** [77.1-85.2], sensitivity (%) of 70, 67.5 ** [64.3-70.7], specificity (%) of 88, 86.5 ** [82-91], positive predictive value (PPV) (%) of 70, 66.5 ** [57.9-75.1] and negative predictive value (NPV) (%) of 88, 87.1 ** [85.5-88.8] (* p < 0.05, ** p < 0.005) in the internal test cohort. It achieved an accuracy of 90.5%, a sensitivity of 100%, a specificity of 86.7%, a PPV of 75% and an NPV of 100% in the external testing cohort. Conclusions: The model constituted of four ensembles of random forest classifiers could identify all the malignant nodes and the consistent majority of benign in the external testing cohort.

A Deep Learning Model to Predict Breast Implant Texture Types Using Ultrasonography Images: Feasibility Development Study.

Breast implants, including textured variants, have been widely used in aesthetic and reconstructive mammoplasty. However, the textured type, which is one of the shell texture types of breast implants, has been identified as a possible etiologic factor for lymphoma, specifically breast implant-associated anaplastic large cell lymphoma (BIA-ALCL). Identifying the shell texture type of the implant is critical to diagnosing BIA-ALCL. However, distinguishing the shell texture type can be difficult due to the loss of human memory and medical history. An alternative approach is to use ultrasonography, but this method also has limitations in quantitative assessment. This study aims to determine the feasibility of using a deep learning model to classify the shell texture type of breast implants and make robust predictions from ultrasonography images from heterogeneous sources. A total of 19,502 breast implant images were retrospectively collected from heterogeneous sources, including images captured from both Canon and GE devices, images of ruptured implants, and images without implants, as well as publicly available images. The Canon images were trained using ResNet-50. The model's performance on the Canon dataset was evaluated using stratified 5-fold cross-validation. Additionally, external validation was conducted using the GE and publicly available datasets. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (PRAUC) were calculated based on the contribution of the pixels with Gradient-weighted Class Activation Mapping (Grad-CAM). To identify the significant pixels for classification, we masked the pixels that contributed less than 10%, up to a maximum of 100%. To assess the model's robustness to uncertainty, Shannon entropy was calculated for 4 image groups: Canon, GE, ruptured implants, and without implants. The deep learning model achieved an average AUROC of 0.98 and a PRAUC of 0.88 in the Canon dataset. The model achieved an AUROC of 0.985 and a PRAUC of 0.748 for images captured with GE devices. Additionally, the model predicted an AUROC of 0.909 and a PRAUC of 0.958 for the publicly available dataset. This model maintained the PRAUC values for quantitative validation when masking up to 90% of the least-contributing pixels and the remnant pixels in breast shell layers. Furthermore, the prediction uncertainty increased in the following order: Canon (0.066), GE (0072), ruptured implants (0.371), and no implants (0.777). We have demonstrated the feasibility of using deep learning to predict the shell texture type of breast implants. This approach quantifies the shell texture types of breast implants, supporting the first step in the diagnosis of BIA-ALCL.

CT and MR Imaging of Hepatocellular Carcinoma and Liver Cirrhosis

Liver masses are routinely evaluated using ultrasonography (US), computed tomography (CT), and magnetic resonance imaging (MRI). MRI may be used for further investigation in cases with atypical findings and difficult diagnoses. Hepatocellular carcinoma (HCC) is a common malignancy, and it is important to know the exact spread and number of HCCs, as there are numerous treatment options. In addition, it is important to know how the differentiations of HCCs are reflected on the images, and what the subtypes of HCCs look like on the images. Elastography with US and MRI is increasingly used to measure liver stiffness, and non-invasive assessment of liver fibrosis is also possible. This review describes the diagnosis of HCC on commonly used CT and MRI, and also touches on the frontiers of imaging diagnosis of liver parenchymal changes such as liver cirrhosis.

Gonadal changes in children and adolescents with congenital adrenal hyperplasia.

Testicular adrenal rest tumours (TARTs) are a common cause of infertility in males with congenital adrenal hyperplasia (CAH). Ovarian adrenal rest tumours (OARTs) and polycystic ovaries (PCO) can impair ovarian function in female patients with CAH. We aim to detect gonadal changes in children and adolescents with CAH. This study was conducted on 50 CAH patients (30 females and 20 males) with 21-hydroxylase deficiency (21-OHD), with a mean age of 10.35 ± 2.36 years. Testicular ultrasonography and pelvic magnetic resonance imaging (MRI) were done in males and females respectively. Glucocorticoid doses and biochemical data were obtained from the patients' medical records. TARTs were detected in 10/20 male patients (50 %). There was a significant relation between presence of TARTs, body mass index (BMI) standard deviation score (SDS), and bone age (p=0.017 and 0.023; respectively). There was no significant relation between presence of TARTs, laboratory parameters, or treatment received (p>0.05). Of those subjected to genetic analysis, 48 % had I2 splice (c.290-13A/C>G) followed by P30L (c.89C>T) (40.7 %). P30L (c.89C>T) was the most common allele among the patients with TARTs (42.9 %). There was no significant relation between presence of TARTs, the genotype, alleles, or the genotype groups (p>0.05). Only one female patient had radiological evidence of bilateral polycystic ovaries and none had OARTs. The prevalence of TARTs in our study was high (50 %). Screening for TARTs in males with CAH is crucial; however, routine ovarian imaging in CAH females is not indicated unless ovarian dysfunction is present.

Clinical course of psoriatic arthritis treated with biologics delineated with ultrasonography.

Psoriatic arthritis (PsA) is a chronic, inflammatory articular disease regarded as a specific subtype of psoriasis. Long-term assessment for PsA using ultrasonography has not yet been investigated. The present study was conducted to delineate the changes in articular lesions after the initiation of biologics using ultrasonography, and to provide the evidence of the utility of ultrasonography in long-term follow-up of PsA patients. We retrospectively recruited 17 Japanese PsA patients treated with biologics who met the classification criteria for psoriatic arthritis. Ultrasonographic images were recorded using a high-frequency linear 18 MHz probe through Doppler- and B-modes. Before the treatment with biologics, all examined patients (100%) had enthesitis and extensor tendinitis, while only six patients (35.3%) had loss of the fibrillar pattern of the tendon (LFP). There were significant changes over time in the numerical rating scale score for pain, and in the degree of ultrasonographic findings, including enthesitis, extensor tendinitis, and LFP. Also, there were significant changes over time between these ultrasonographic findings. The study identified the improvement course for a specific PsA lesion after the initiation of biologics. The improvement courses in enthesitis, extensor tendinitis, and LFP were found to differ from each other. These results may contribute to deeper understanding of the pathogenesis of PsA.

Minimally invasive interventional guided imaging therapies of musculoskeletal tumors.

Historically, musculoskeletal (MSK) tumors, which include both bone and soft tissue tumors, have been managed as distinct entities. The incidence of metastases, particularly bone metastasis, in patients with MSK tumors can result in the emergence of significant complications such as pain, impairment of vital anatomical structures, or pathological fractures. Given these issues, a diverse team of experts is typically engaged in intricate treatment decision-making concerning the necessity of surgery, radiation, chemotherapy, or a mix of these methodologies. Nevertheless, percutaneous image-guided minimally invasive interventional therapy for MSK tumors represent a promising approach for treating such tumors. Over the past decade, significant progress has been made in this technique, leading to its growing acceptance in ordinary clinical practice. MSK tumors can be effectively treated by the use of ablation techniques, either as standalone procedures or in conjunction with other percutaneous treatments. Various image-guided techniques have been employed to observe the ablation zone and nearby structures, such as fluoroscopy, ultrasonography (US), computed tomography (CT), and magnetic resonance imaging (MRI). However, CT is the favored method due to its widespread availability and ability to visualize the tumor and its environs. The procedures employed include ethanol injection, radiofrequency ablation, microwave ablation, cryoablation, and magnetic resonance (MR)-guided high-intensity focused ultrasound (HIFU). The technique can be performed in combination with cementation, with or without additional metallic stabilizing devices, depending on the location of the lesion. Improved local tumor control can be attained by combining ablation with bland embolization or transarterial chemoembolization. This article provides an overview of the fundamental elements of minimally invasive interventional guided imaging therapy for MSK malignancies.

Mobile Phone Radiation Effects on Brain with Reference to the Protective Role of Moringa oleifera: Sonographic and Histological Study

Abstract Background: Concerns regarding potential negative impacts on human health are growing as cell phone use dramatically expands worldwide. Due to the anatomical position of the brain, cell phones emit nonionizing radiofrequency waves that may have an impact on it. Objectives: The study aims to examine the effect of mobile phone radiation in male rats on the histological structure, and sonographic imaging of the brain of rats after mobile phone radiation exposure and investigate the ameliorating role of moringa. Materials and Methods: In the study, 44 male Albino rats were used. The rats were randomly divided into six groups and exposed to cell phone radiation (in the data or calling modes) for 10 weeks. The 200 mg/kg of body weight of moringa extract was administered to evaluate the protective role of the rats for 10 weeks. Results: A sonographic and histological analysis of the rat brain showed that rats exposed to mobile phone radiation suffered serious damage to their neurons in several parts of the brain. Meanwhile, the rats given moringa extract suffered less harm. Conclusion: Rats given moringa extract had less damage to neuronal architecture in their brains.

Neck triangle nerve enlargement in hereditary transthyretin amyloidosis correlates with changes in the autonomic, cardiac, and gastrointestinal systems.

Hereditary transthyretin amyloidosis (ATTRv) is a hereditary disease that affects multiple bodily systems. Although sonography generally reveals enlargement of nerves in the limbs, the brachial plexus, and vagus nerve, the clinical significance of these findings remains unclear. We performed sonographic measurements of the median nerve, cervical spinal nerves at the C5-C7 level, and the vagus nerve in patients with ATTRv and healthy controls. Clinical profiles and cardiac and gastrointestinal examination results were also collected for linear regression analysis. We recruited 47 patients with ATTRv (males/females: 34/13, age: 65.6±5.3 years). The sampled segments were all significantly larger than those of the controls. In the clinical profiles, the sum of the Z scores of the neck triangle nerves (cervical spinal nerves and vagus nerve) and of all nerves (cervical spinal nerves, vagus nerve, and median nerve at the wrist) significantly correlated with the familial amyloid polyneuropathy stage, onset of autonomic nervous system (ANS) symptoms, and autonomic symptom scores. On cardiac examinations, several ultrasonography and magnetic resonance imaging parameters (primarily those that reflect heart volume) were found to be significantly correlated with the sum of the Z scores of the cervical spinal nerves but not with the Z score of the vagus nerve. In gastrointestinal evaluation, the cross-sectional area of the vagus nerve was correlated with gastric emptying time parameters on scintigraphy. Neck triangle nerve enlargement on sonography correlated with parameters related to ANS dysfunction, indicating that nerve enlargement observed on ultrasonography may serve as a potential surrogate biomarker of ATTRv.