Breast cancer metastasis significantly impacts women\'s health globally. This study aimed to construct predictive models using clinical blood markers and ultrasound data to predict distant metastasis in breast cancer patients, ensuring clinical applicability, cost-effectiveness, relative non-invasiveness, and accessibility of these models. Analysis was conducted on data from 416 patients across two centers, focusing on clinical blood markers (tumor markers, liver and kidney function indicators, blood lipid markers, cardiovascular biomarkers) and maximum lesion diameter from ultrasound. Feature reduction was performed using Spearman correlation and LASSO regression. Two models were built using LightGBM: a clinical model (using clinical blood markers) and a combined model (incorporating clinical blood markers and ultrasound features), validated in training, internal test, and external validation (test1) cohorts. Feature importance analysis was conducted for both models, followed by univariate and multivariate regression analyses of these features. The AUC values of the clinical model in the training, internal test, and external validation (test1) cohorts were 0.950, 0.795, and 0.883, respectively. The combined model showed AUC values of 0.955, 0.835, and 0.918 in the training, internal test, and external validation (test1) cohorts, respectively. Clinical utility curve analysis indicated the combined model\'s superior net benefit in identifying breast cancer with distant metastasis across all cohorts. This suggests the combined model\'s superior discriminatory ability and strong generalization performance. Creatine kinase isoenzyme (CK-MB), CEA, CA153, albumin, creatine kinase, and maximum lesion diameter from ultrasound played significant roles in model prediction. CA153, CK-MB, lipoprotein (a), and maximum lesion diameter from ultrasound positively correlated with breast cancer distant metastasis, while indirect bilirubin and magnesium ions showed negative correlations. This study successfully utilized clinical blood markers and ultrasound data to develop AI models for predicting distant metastasis in breast cancer. The combined model, incorporating clinical blood markers and ultrasound features, exhibited higher accuracy, suggesting its potential clinical utility in predicting and identifying breast cancer distant metastasis. These findings highlight the potential prospects of developing cost-effective and accessible predictive tools in clinical oncology.

OBJECTIVE: The association between Papillary Thyroid Carcinoma (PTC) and coexistent Hashimoto\'s Thyroiditis (HT) was controversial. The purpose of this study was to evaluate the presence of HT exerts any influence on the aggressiveness of PTC, and to establish a nomogram for predicting the possibility of aggressiveness in PTC.
METHODS: 373 consecutive PTC patients with/without coexistent HT from January 2017 to December 2020 were retrospective reviewed. Patients\' clinicopathologic and sonographic characteristics were collected for univariate and multivariate analyses. A nomogram was established based on the risk factors for aggressiveness in PTC.
RESULTS: Male (p = 0.001), tumor size >1.0 cm (p = 0.046) and lymph node metastasis (p = 0.018) were negatively associated with PTC coexisted with HT, while it was significantly positively associated with the frequence of multifocality (p = 0.010). Univariate and multivariate analyses suggested that age ≥55 years (p = 0.000), male (p = 0.027), HT (p = 0.017), tumor size >1.0 cm (p = 0.015), multifocality (p = 0.041), distance to capsular ≤0 cm (p = 0.050) and blood flow (Grade I: p = 0.044) were independent risk factors for predicting the aggressiveness in PTC. A nomogram according to these predictors was further developed and validated. The receiver operating characteristic curve (AUC = 0.734 and 0.809 for training and validation cohorts, respectively) and decision curve analyses indicated that the nomogram model was clinically useful. The calibration curve revealed that the nomogram exhibited an excellent consistency.
CONCLUSIONS: In this study, the coexistent HT might play a protective role in preventing the proliferation of PTC. Dispensable aggressive treatment may be reduced in PTC by pre-operative identification of sonographic and clinical characteristics and incorporating with the predicted nomogram model.

BACKGROUND: To investigate the accuracy of three model formulae for ultrasound quantification of pleural effusion (PE) volume in patients in supine position.
METHODS: A prospective study including 100 patients with thoracentesis and drainage of PE was conducted. Three model formulae (single section model, two section model and multi-section model) were used to calculate the PE volume. The correlation and consistency analyses between calculated volumes derived from three models and actual PE volume were performed.
RESULTS: PE volumes calculated by three models all showed significant linear correlations with actual PE volume in supine position (all p < 0.001). The reliability of multi-section model in predicting PE volume was significantly higher than that of single section model and slightly higher than that of two section model. When compared with actual drainage volume, the intra-class correlation coefficients (ICCs) of single section model, two section model and multi-section model were 0.72, 0.97 and 0.99, respectively. Significant consistency between calculated PE volumes by using two section model and multi-section model existed for full PE volume range (ICC 0.98).
CONCLUSIONS: Based on the convenience and accuracy of ultrasound quantification of PE volume, two section model is recommended for pleural effusion assessment in routine clinic, though different model formulae can be selected according to clinical needs.

UNASSIGNED: This study aimed to investigate the efficacy and safety of ultrasound-guided percutaneous radiofrequency ablation (RFA) for the treatment of synovial hyperplasia in the knee joints of antigen-induced arthritis (AIA) model rabbits.
UNASSIGNED: Forty Japanese large-eared white rabbits were divided into AIA and control groups. After successful induction of the AIA model, the knee joints were randomly assigned to RFA and non-RFA groups. The RFA group underwent ultrasound-guided RFA to treat synovial hyperplasia in the knee joint. Dynamic observation of various detection indices was conducted to evaluate the safety and effectiveness of the RFA procedure.
UNASSIGNED: Successful synovial ablation was achieved in the RFA group, with no intraoperative or perioperative mortality. Postoperative the circumference of the knee joint reached a peak before decreasing in the third week after surgery. The incidence and diameter of postoperative skin ulcers were not significantly different compared to the non-RFA group (p > .05). Anatomical examination revealed an intact intermuscular fascia around the ablated area in the RFA group. The ablated synovial tissue initially presented as a white mass, which subsequently liquefied into a milky white viscous fluid. Gross articular cartilage was observed, along with liquefied necrosis of the synovium on pathological histology and infiltration of inflammatory cells in the surrounding soft tissue.
UNASSIGNED: The experimental results demonstrated that ultrasound-guided RFA of the knee in the treatment of synovial hyperplasia in AIA model animals was both effective and safe.

BACKGROUND: Significant progress has been made in kidney xenotransplantation in the past few years, and this field is accelerating towards clinical translation. Therefore, surveillance of the xenograft with appropriate tools is of great importance. Ultrasonography has been widely used in kidney allotransplantation and served as an economical and non-invasive method to monitor the allograft. However, questions remain whether the ultrasonographic criteria established for human kidney allograft could also be applied in xenotransplantation.
METHODS: In the current study, we established a porcine-rhesus life sustaining kidney xenotransplantation model. The xenograft underwent intensive surveillance using gray-scale, colorful Doppler ultrasound as well as 2D shear wave elastography. The kidney growth, blood perfusion, and cortical stiffness were measured twice a day. These parameters were compared with the clinical data including urine output, chemistry, and pathological findings.
RESULTS: The observation continued for 16 days after transplantation. Decline of urine output and elevated serum creatinine were observed on POD9 and biopsy proven antibody-mediated rejection was seen on the same day. The xenograft underwent substantial growth, with the long axis length increased by 32% and the volume increased by threefold at the end of observation. The resistive index of the xenograft arteries elevated in response to rejection, together with impaired cortical perfusion, while the peak systolic velocity (PSV) was not compromised. The cortical stiffness also increased along with rejection.
CONCLUSIONS: In summary, the ultrasound findings of kidney xenograft shared similarities with those in allograft but possessed some unique features. A modified criteria needs to be established for further application of ultrasound in kidney xenotransplantation.

This study aimed to construct a non-invasive diagnostic nomogram based on high-frequency ultrasound and magnetic resonance imaging results for early liver cirrhosis patients with chronic hepatitis B (CHB) which cannot be detected by conventional non-invasive examination methods but can only be diagnosed through invasive liver puncture for pathological examination. 72 patients with CHB were enrolled in this prospective study, and divided into S4 stage of liver cirrhosis and S0-S3 stage of non-liver cirrhosis according to pathological findings. Binary logistic regression analysis was performed to identify independent predictors, and a diagnostic nomogram was constructed for CHB-related early cirrhosis. It was validated and calibrated by bootstrap self-extraction. Binary logistic regression analysis showed that age (OR 1.14, 95% CI (1.04-1.27)), right hepatic vein diameter (OR 0.43, 95% CI 0.23-0.82), presence or absence of nodules (OR 31.98, 95% CI 3.84-266.08), and hepatic parenchymal echogenicity grading (OR 12.82, 95% CI 2.12-77.51) were identified as independent predictive indicators. The nomogram based on the 4 factors above showed good performance, with a sensitivity and specificity of 90.70% and 89.66%, respectively. The area under the curve (AUC) of the prediction model was 0.96, and the predictive model showed better predictive performance than APRI score (AUC 0.57), FIB-4 score (AUC 0.64), INPR score (AUC 0.63), and LSM score (AUC 0.67). The calibration curve of the prediction model fit well with the ideal curve, and the decision curve analysis showed that the net benefit of the model was significant. The nomogram in this study can detect liver cirrhosis in most CHB patients without liver biopsy, providing a direct, fast, and accurate practical diagnostic tool for clinical doctors.

UNASSIGNED: This study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the benign or malignant nature of pancreatic tumors.
UNASSIGNED: 242 pancreatic tumor patients who were hospitalized at the First Affiliated Hospital of Guangxi Medical University between January 2020 and June 2023 were included in this retrospective study. The patients were randomly divided into a training cohort (n=169) and a test cohort (n=73). We collected 28 clinical features from the patients. Concurrently, 306 radiomics features were extracted from the ultrasound images of the patients\' tumors. Initially, a clinical model was constructed using the logistic regression algorithm. Subsequently, radiomics models were built using SVM, random forest, XGBoost, and KNN algorithms. Finally, we combined clinical features with a new feature RAD prob calculated by applying radiomics model to construct a fusion model, and developed a nomogram based on the fusion model.
UNASSIGNED: The performance of the fusion model surpassed that of both the clinical and radiomics models. In the training cohort, the fusion model achieved an AUC of 0.978 (95% CI: 0.96-0.99) during 5-fold cross-validation and an AUC of 0.925 (95% CI: 0.86-0.98) in the test cohort. Calibration curve and decision curve analyses demonstrated that the nomogram constructed from the fusion model has high accuracy and clinical utility.
UNASSIGNED: The fusion model containing clinical and ultrasound radiomics features showed excellent performance in predicting the benign or malignant nature of pancreatic tumors.

BACKGROUND: The aim of this study is to assess the efficacy of a multiparametric ultrasound imaging omics model in predicting the risk of postoperative recurrence and molecular typing of breast cancer.
METHODS: A retrospective analysis was conducted on 534 female patients diagnosed with breast cancer through preoperative ultrasonography and pathology, from January 2018 to June 2023 at the Affiliated Cancer Hospital of Xinjiang Medical University. Univariate analysis and multifactorial logistic regression modeling were used to identify independent risk factors associated with clinical characteristics. The PyRadiomics package was used to delineate the region of interest in selected ultrasound images and extract radiomic features. Subsequently, radiomic scores were established through Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine (SVM) methods. The predictive performance of the model was assessed using the receiver operating characteristic (ROC) curve, and the area under the curve (AUC) was calculated. Evaluation of diagnostic efficacy and clinical practicability was conducted through calibration curves and decision curves.
RESULTS: In the training set, the AUC values for the postoperative recurrence risk prediction model were 0.9489, and for the validation set, they were 0.8491. Regarding the molecular typing prediction model, the AUC values in the training set and validation set were 0.93 and 0.92 for the HER-2 overexpression phenotype, 0.94 and 0.74 for the TNBC phenotype, 1.00 and 0.97 for the luminal A phenotype, and 1.00 and 0.89 for the luminal B phenotype, respectively. Based on a comprehensive analysis of calibration and decision curves, it was established that the model exhibits strong predictive performance and clinical practicability.
CONCLUSIONS: The use of multiparametric ultrasound imaging omics proves to be of significant value in predicting both the risk of postoperative recurrence and molecular typing in breast cancer. This non-invasive approach offers crucial guidance for the diagnosis and treatment of the condition.

Using ultrasound findings and clinical characteristics, we constructed and validated a new nomogram for distinguishing epididymal tuberculosis from nontuberculous epididymitis, both of which share similar symptoms. We retrospectively examined data of patients with epididymal tuberculosis and nontuberculous epididymitis hospitalized between January 1, 2013, and March 31, 2023. Eligible patients were randomly assigned to derivation and validation cohorts (ratio, 7:3). We drew a nomogram to construct a diagnostic model through multivariate logistic regression and visualize the model. We used concordance index, calibration plots, and decision curve analysis to assess the discrimination, calibration, and clinical usefulness of the nomogram, respectively. In this study, 136 participants had epididymal tuberculosis and 79 had nontuberculous epididymitis. Five variables-C-reactive protein level, elevated scrotal skin temperature, nodular lesion, chronic infection, and scrotal skin ulceration-were significant and used to construct the nomogram. Concordance indices of the derivation and validation cohorts were 0.95 and 0.96, respectively (95% confidence intervals, 0.91-0.98 and 0.92-1.00, respectively). Decision curve analysis of this nomogram revealed that it helped differentiate epididymal tuberculosis from nontuberculous epididymitis. This nomogram may help clinicians distinguish between epididymal tuberculosis and nontuberculous epididymitis, thereby increasing diagnosis accuracy.

OBJECTIVE: This study aims to examine the clinical characteristics and surgical management of pediatric testicular epidermoid cysts, thereby contributing to the existing body of knowledge pertinent to the diagnosis and therapeutic intervention s for this condition.
METHODS: A retrospective analysis was conducted on the clinical records of 23 pediatric patients diagnosed with testicular epidermoid cysts, who were admitted to our institution between April 2013 and February 2024. Concurrently, a comprehensive review and analysis of pertinent literature were undertaken to augment the findings.
RESULTS: The mean age at which the onset of epidermoid cysts was observed was 6.0 years. All cases were singular and unilateral. B-ultrasound diagnosis categorized 6 cases as epidermoid cysts, 11 as teratomas, and 6 as indeterminate, yielding a diagnostic sensitivity of 26.1%. All patients underwent testicle-sparing mass resection, and nine patients underwent rapid intraoperative frozen section analysis, revealing eight cases of testicular epidermoid cysts and one teratoma, with a diagnostic sensitivity of 88.89%. Postoperative histopathological examination confirmed the diagnosis of testicular epidermoid cyst.
CONCLUSIONS: Pediatric testicular epidermoid cysts are an uncommon occurrence, primarily presenting as a painless scrotal mass, which can mimic the clinical features of malignant testicular tumors. Imaging modalities and histopathological assessment are pivotal in the diagnostic process for pediatric testicular epidermoid cysts. For cases where B-ultrasound is inconclusive, rapid intraoperative pathological examination should be considered.