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Abstract Purpose A retrospective study was conducted to determine the value of shear wave elastography (SWE) and red blood cell distribution width (RDW) in the diagnosis of various forms of erectile dysfunction (ED). Methods With the method of Nocturnal Penile Tumescence and Rigidity (NPTR) and the screening method of Color Duplex Doppler Ultrasound (CDDU), hematological data were collected from 131 individuals, among whom 24 are with psychogenic ED, 48 are with non-arterial ED(NAED) and 59 are with arterial ED(AED) with erectile dysfunction. SWE value of penile corpus cavernosum(CCP) and cavernous arterial flow velocity were measured before (flaccid state) and after (erect state) intracavernous injection (ICI) in all patients. Results Among the AED patients and other types of ED patients, there were statistically significant differences in the abridged five-item International Index of Erectile Function (IIEF-5), red blood cell distribution width-coefficient of variation (RDW-CV), red blood cell distribution width-standard deviation (RDW-SD), and SWE values (all P

We developed a multimodal ultrasound (US) deep learning (DL) fusion model to automatically classify early fibrosis in patients with chronic kidney disease (CKD). This prospective study included patients with CKD who underwent continuous gray-scale US, superb microvascular imaging, and strain elastography from May to November 2022. According to the pathological tubular atrophy and interstitial fibrosis score, patients were divided into minimal and mild groups (affected area ≤10% and 11 − 25% of the total cortical volume, respectively). The dataset was divided into training (70%) and test (30%) sets. A DL model combining the features of the three US modes was developed to predict early fibrosis in patients with CKD. We compared these findings with the area under the receiver operating characteristic curve (AUC) of the clinical model by analyzing the receiver operating characteristic curve in the test set. The AUC of single-mode DL based on gray-scale US, superb microvascular imaging, and strain elastography was 0.682, 0.745, and 0.648, respectively, while that of the multimodal US DL model was 0.86. The accuracy, specificity, and sensitivity of the multimodal US DL model were 0.779, 0.767, and 0.796, respectively, and the negative and positive predictive values were 0.842 and 0.706, respectively. The AUC of the multimodal US DL model was significantly better than that of the single-mode DL and clinical models. The DL algorithm developed using multimodal US images can effectively predict early fibrosis in patients with CKD with significantly greater accuracy than single-mode DL or clinical models.

ObjectiveThe purpose of this retrospective study was to establish a combined model based on ultrasound (US)-radiomics and clinical factors to predict patients with stage I cervical cancer (CC) before surgery.Materials and methodsA total of 209 CC patients who had cervical lesions found by transvaginal sonography (TVS) from the First Affiliated Hospital of Anhui Medical University were retrospectively reviewed, patients were divided into the training set (n = 146) and internal validation set (n = 63), and 52 CC patients from Anhui Provincial Maternity and Child Health Hospital and Nanchong Central Hospital were taken as the external validation set. The clinical independent predictors were selected by univariate and multivariate logistic regression analyses. US-radiomics features were extracted from US images. After selecting the most significant features by univariate analysis, Spearman’s correlation analysis, and the least absolute shrinkage and selection operator (LASSO) algorithm, six machine learning (ML) algorithms were used to build the radiomics model. Next, the ability of the clinical, US-radiomics, and clinical US-radiomics combined model was compared to diagnose stage I CC. Finally, the Shapley additive explanations (SHAP) method was used to explain the contribution of each feature.ResultsLong diameter of the cervical lesion (L) and squamous cell carcinoma-associated antigen (SCCa) were independent clinical predictors of stage I CC. The eXtreme Gradient Boosting (Xgboost) model performed the best among the six ML radiomics models, with area under the curve (AUC) values in the training, internal validation, and external validation sets being 0.778, 0.751, and 0.751, respectively. In the final three models, the combined model based on clinical features and rad-score showed good discriminative power, with AUC values in the training, internal validation, and external validation sets being 0.837, 0.828, and 0.839, respectively. The decision curve analysis validated the clinical utility of the combined nomogram. The SHAP algorithm illustrates the contribution of each feature in the combined model.ConclusionWe established an interpretable combined model to predict stage I CC. This non-invasive prediction method may be used for the preoperative identification of patients with stage I CC.

This study aimed to develop and validate a combined nomogram model based on superb microvascular imaging (SMI)-based deep learning (DL), radiomics characteristics, and clinical factors for noninvasive differentiation between immunoglobulin A nephropathy (IgAN) and non-IgAN. We prospectively enrolled patients with chronic kidney disease who underwent renal biopsy from May 2022 to December 2022 and performed an ultrasound and SMI the day before renal biopsy. The selected patients were randomly divided into training and testing cohorts in a 7:3 ratio. We extracted DL and radiometric features from the two-dimensional ultrasound and SMI images. A combined nomograph model was developed by combining the predictive probability of DL with clinical factors using multivariate logistic regression analysis. The proposed model’s utility was evaluated using receiver operating characteristics, calibration, and decision curve analysis. In this study, 120 patients with primary glomerular disease were included, including 84 in the training and 36 in the test cohorts. In the testing cohort, the ROC of the radiomics model was 0.816 (95% CI:0.663–0.968), and the ROC of the DL model was 0.844 (95% CI:0.717–0.971). The nomogram model combined with independent clinical risk factors (IgA and hematuria) showed strong discrimination, with an ROC of 0.884 (95% CI:0.773–0.996) in the testing cohort. Decision curve analysis verified the clinical practicability of the combined nomogram. The combined nomogram model based on SMI can accurately and noninvasively distinguish IgAN from non-IgAN and help physicians make clearer patient treatment plans.

In this study, we aimed to explore the clinical value of routine color ultrasound parameters in the evaluation of tubular atrophy and interstitial fibrosis (TA/IF) in IgA nephropathy (IgAN). We enrolled 725 patients with IgAN who underwent renal biopsy at the First Affiliated Hospital of Anhui Medical University between January 2019 and May 2022. Examinations were performed to measure the routine ultrasound renal parameters and renal biopsy was done within next three days. Univariate and multivariate analyses were used to determine the correlates and the independent predictors of TA/IF. Simultaneously, a nomogram based on risk indicators was created to predict TA/IF. Univariate and multivariate analyses showed that sex (p

ObjectiveWe used machine-learning (ML) models based on ultrasound radiomics to construct a nomogram for noninvasive evaluation of the crescent status in immunoglobulin A (IgA) nephropathy.MethodsPatients with IgA nephropathy diagnosed by renal biopsy (n=567) were divided into training (n=398) and test cohorts (n=169). Ultrasound radiomic features were extracted from ultrasound images. After selecting the most significant features using univariate analysis and the least absolute shrinkage and selection operator algorithm, three ML algorithms were assessed for final radiomic model establishment. Next, clinical, ultrasound radiomic, and combined clinical−radiomic models were compared for their ability to detect IgA crescents. The diagnostic performance of the three models was evaluated using receiver operating characteristic curve analysis.ResultsThe average area under the curve (AUC) of the three ML radiomic models was 0.762. The logistic regression model performed best, with AUC values in the training and test cohorts of 0.838 and 0.81, respectively. Among the final models, the combined model based on clinical characteristics and the Rad score showed good discrimination, with AUC values in the training and test cohorts of 0.883 and 0.862, respectively. The decision curve analysis verified the clinical practicability of the combined nomogram.ConclusionML classifier based on ultrasound radiomics has a potential value for noninvasive diagnosis of IgA nephropathy with or without crescents. The nomogram constructed by combining ultrasound radiomic and clinical features can provide clinicians with more comprehensive and personalized image information, which is of great significance for selecting treatment strategies.

Objectives The purpose of the current study was to determine the performance of contrast-enhanced ultrasound (CEUS) in the assessment of radiofrequency ablation (RFA) of hyperplastic parathyroid glands due to secondary hyperparathyroidism (SHPT). Methods Thirty-two patients, each with ≥4 hyperplastic parathyroid glands due to SHPT, underwent RFA via hydro-dissection. CEUS was performed in each patient before and during RFA. The patients in whom the intact parathyroid hormone (iPTH) level did not decrease to 300 pg/ml were examined by CEUS. The iPTH, serum calcium, and serum phosphorus levels before and after RFA were compared. Results Ablation was achieved in all patients (131 ablated glands). The volume of the glands was 479.88 ± 549.3mm3. The pre-operative and day 1 post-operative iPTH levels were 2355 ± 1062 and 292.7 ± 306.8 pg/ml, respectively. Three nodules in three patients showed little enhancement on CEUS on post-operative day 1. The iPTH level was 0.05). Conclusions CEUS-guided RFA is effective and feasible for the treatment of ≥4 hyperplastic parathyroid glands. CEUS was shown to assist the surgeon before, during, and after RFA. CEUS on post-operative day 2, but not immediately post-operatively, was shown to accurately reflect gland perfusion.