To investigate the relationship between the specific ultrasonic manifestations of lower limb joints and impaired kidney function in gouty arthritis. In this cross-sectional study, 408 patients with gouty arthritis were divided into two groups based on the status of renal function: normal group (n = 240) and renal impairment (n = 168) group. All patients underwent ultrasound examination of the bilateral knee, ankle, and first metatarsophalangeal joints to detect ultrasound features of double-contour sign (DC) and tophus. Multiple logistic regression analysis was conducted to assess the association between kidney dysfunction and ultrasound features. A number of potential clinical confounders were adjusted in the model. Univariable conditional logistic regression produces several significant risk factors of impaired kidney function which were the highest and current lever of serum urate acid, course of disease, frequency of attack, hyperlipidemia, hypertension, diabetes, coronary heart disease, presence of multiple tophus, and DC (P < 0.05). After correcting the course of disease and other risk factors, tophus was still an independent risk factor of impaired kidney function and the multivariable adjusted odds ratios (95% CI) was 1.789 (1.005-3.185, P = 0.05), however, the association was not significant in DC (OR = 1.098, 95% CI: 0.668-1.803, P = 0.71). The ultrasound feature of tophus was associated with kidney dysfunction in patients with gout, independent of clinical risk factors, which may be helpful in guiding clinical practice.

Metastases are the most common neoplasm in the adult brain. In order to initiate the treatment, an extensive diagnostic workup is usually required. Radiomics is a discipline aimed at transforming visual data in radiological images into reliable diagnostic information. We aimed to examine the capability of deep learning methods to classify the origin of metastatic lesions in brain MRIs and compare the deep Convolutional Neural Network (CNN) methods with image texture based features. One hundred forty three patients with 157 metastatic brain tumors were included in the study. The statistical and texture based image features were extracted from metastatic tumors after manual segmentation process. Three powerful pre-trained CNN architectures and the texture-based features on both 2D and 3D tumor images were used to differentiate lung and breast metastases. Ten-fold cross-validation was used for evaluation. Accuracy, precision, recall, and area under curve (AUC) metrics were calculated to analyze the diagnostic performance. The texture-based image features on 3D volumes achieved better discrimination results than 2D image features. The overall performance of CNN architectures with 3D inputs was higher than the texture-based features. Xception architecture, with 3D volumes as input, yielded the highest accuracy (0.85) while the AUC value was 0.84. The AUC values of VGG19 and the InceptionV3 architectures were 0.82 and 0.81, respectively. CNNs achieved superior diagnostic performance in differentiating brain metastases from lung and breast malignancies than texture-based image features. Differentiation using 3D volumes as input exhibited a higher success rate than 2D sagittal images.

Data available on request from the authors.

Characteristic ultrasound features of large cell calcifying Sertoli cell tumor (LCCSCT), including hypoechoic masses with amorphous coarse calcifications can aid in differentiating this tumor from other entities. Bilateral multiple LCCSCTs almost always show a benign course; therefore, defining the diagnosis with sonographic findings is crucial to avoid unnecessary orchiectomy.

To explore the optimal peri-tumoral regions on ultrasound (US) images and investigate the performance of multimodal radiomics for predicting axillary lymph node metastasis (ALNM). This retrospective study included 326 patients (training cohort: n = 162, internal validation cohort: n = 74, external validation cohort: n = 90). Intra-tumoral region of interests (ROIs) were delineated on US and digital mammography (DM) images. Peri-tumoral ROI (PTR) on US images were gained by dilating actual 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 and 3.5 mm radius surrounding the tumor. Support vector machine (SVM) method was used to calculate the importance of radiomics features and to pick the 10 most important. Recursive feature elimination-SVM was used to evaluate the efficacy of models with different feature numbers used. The PTR0.5mm yielded a maximum AUC of 0.802 (95% confidence interval (CI): 0.676-0.901) within the validation cohort using SVM classifier. The multimodal radiomics (intra-tumoral US and DM and US-based PTR0.5mm radiomics model) achieved the highest predictive ability (AUC = 0.888/0.844/0.835 and 95% CI = 0.829-0.936/0.741-0.929/0.752-0.896 for training/internal validation/external validation cohort, respectively). The PTR0.5mm could be the optimal area for predicting ALNM. A favorable predictive accuracy for predicting ALNM was achieved using multimodal radiomics and its based nomogram.

We present a rare case of diffuse large B-cell lymphoma developed in subcutaneous fat layer of the breast with cardiac involvement. Radiologists should perform an image-guided biopsy for pathologic confirmation of breast lymphomas and avoidance of unnecessary invasive treatment.

The authors declare no conflict of interest. Not applicable.

This study aimed to evaluate fetal epicardial fat thickness (EFT) along with fetal myocardial performance index (MPI) and its effects on perinatal outcomes in non-severe idiopathic polyhydramnios (IP). This prospective study included 92 participants, 32 diagnosed with non-severe IP, and 60 healthy pregnant women. Amniotic fluid indices (AFI), umbilical and middle cerebral artery Doppler, EFT, and MPI measurements were performed for all patients. The fetal EFT and MPI values were statistically higher in the non-severe IP group than in the control group (p = 0.0001, p = 0.014, respectively). The optimal fetal EFT cutoff value for predicting non-severe IP disease was found as 1.3 mm with a specificity of 81.7% and sensitivity of 59.4%. The EFT cutoff for predicting cesarean section in non-severe IP cases was 1.25 mm (p = 0.038). Apgar scores, neonatal intensive care unit, respiratory distress syndrome, and stillbirth rates were not different between groups. In this study, EFT and MPI were found to be higher in non-severe IP cases compared to controls. It was observed that the increase in MPI and EFT was associated with the increase in cesarean rates, but not with adverse fetal outcomes.

To investigate the feasibility of high-frame-rate vector flow imaging (HiFR-VFI) compared to ultrasound color Doppler flow imaging (CDFI) for precisely evaluating flow characteristics in the carotid bifurcation (CB) of presumed healthy adults. Forty-three volunteers were assessed for flow characteristics and their extensions using HiFR-VFI and CDFI in CBs. The flow patterns were classified according to the streamlines in HiFR-VFI and quantitatively measured using an innovative turbulence index (Tur-value). Interobserver agreement was also assessed. HiFR-VFI was consistent with CDFI in detecting laminar and nonlaminar flow in 81.4% of the cases; however, in 18.6% of the cases, only HiFR-VFI identified the nonlaminar flow. HiFR-VFI showed a larger extension of complex flow (0.37 ± 0.26 cm2 ) compared to CDFI (0.22 ± 0.21 cm2 ; p < 0.05). The flow patterns were classified into four types: 3 type-I (laminar flow), 35 type-II (rotational flow), 27 type-III (reversed flow), and 5 type-IV (complex flow). The Tur-value of type-IV (50.03 ± 14.97)% is larger than type-III (44.57 ± 8.89)%, type-II (16.30 ± 8.16)%, and type-I (1.48 ± 1.43)% (p < 0.05). Two radiologists demonstrated almost perfect interobserver agreement on recognizing the change of streamlines (κ = 0.81, p < 0.001). The intraclass correlation coefficient of the Tur-value was 0.98. HiFR-VFI can reliably characterize complex hemodynamics with quantitative turbulence measurement and may be an auxiliary diagnostic tool for assessing atherosclerotic arterial disease.