UNASSIGNED: The objective of the study was to use a deep learning model to differentiate between benign and malignant sentinel lymph nodes (SLNs) in patients with breast cancer compared to radiologists\' assessments.Seventy-nine women with breast cancer were enrolled and underwent lymphosonography and contrast-enhanced ultrasound (CEUS) examination after subcutaneous injection of ultrasound contrast agent around their tumor to identify SLNs. Google AutoML was used to develop image classification model. Grayscale and CEUS images acquired during the ultrasound examination were uploaded with a data distribution of 80% for training/20% for testing. The performance metric used was area under precision/recall curve (AuPRC). In addition, 3 radiologists assessed SLNs as normal or abnormal based on a clinical established classification. Two-hundred seventeen SLNs were divided in 2 for model development; model 1 included all SLNs and model 2 had an equal number of benign and malignant SLNs. Validation results model 1 AuPRC 0.84 (grayscale)/0.91 (CEUS) and model 2 AuPRC 0.91 (grayscale)/0.87 (CEUS). The comparison between artificial intelligence (AI) and readers\' showed statistical significant differences between all models and ultrasound modes; model 1 grayscale AI versus readers, P = 0.047, and model 1 CEUS AI versus readers, P < 0.001. Model 2 r grayscale AI versus readers, P = 0.032, and model 2 CEUS AI versus readers, P = 0.041.The interreader agreement overall result showed κ values of 0.20 for grayscale and 0.17 for CEUS.In conclusion, AutoML showed improved diagnostic performance in balance volume datasets. Radiologist performance was not influenced by the dataset\'s distribution.

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: Gallbladder polyps are increasingly being identified due to the widespread use of abdominal ultrasound imaging. They are concerning lesions due to their potential malignant risk. It is hoped that managing them correctly will play a role in improving poor survival rates of gallbladder cancer. Awareness of these lesions is lacking. Management continues to be guided by expert opinion and observational studies and a number of consensus statements exist.
OBJECTIVE: This paper reviews and summarises the current literature and provides an approach for general practitioners based on the available guidance.
CONCLUSIONS: Although minor variation exists between consensus statements, the risk of malignancy for gallbladder polyps is still largely dictated by size, with those ≤5 mm generally considered to pose little risk and not requiring follow-up, whereas those ≥10 mm considered at greater risk and requiring referral for cholecystectomy.

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.

BACKGROUND: Point-of-Care Ultrasound (POCUS) consists of a range of increasingly important imaging modalities across a variety of specialties. Despite a variety of accreditation pathways available in the UK, lung POCUS training remains difficult to deliver and accreditation rates remain suboptimal. We describe a multidisciplinary, multi-centre, and multi-pronged approach to lung POCUS education within a region.
METHODS: A survey was conducted in a region. From these results, bottlenecks were identified for improvement. We utilised key stages in an established accreditation pathway, and the Action Learning process. Analysing participant feedback, consensus amongst the team, regional educational needs, and leveraging the expertise within the faculty, we implemented several solutions which were multidisciplinary, multi-centre, and multi-pronged. We also set up a database across several accreditation pathways to facilitate supervision and assessment of rotational trainees.
RESULTS: Utilising the Action Learning process, we implemented several improvements at elements of the lung ultrasound accreditation pathways. An initial regional survey identified key barriers to accreditation: lack of courses (52%), lack of mentors (93%), and difficulty arranging directly supervised scans (73%). A multidisciplinary team of trainers was assembled. Regular courses were organised and altered based on feedback and anecdotal educational needs within the region. Courses were set up to also facilitate continuing professional development and exchange of knowledge and ideas amongst trainers. The barrier of supervision was removed through the organisation of regular supervision sessions, facilitating up to fifty scans per half day per trainer. We collected feedback from courses and optimised them. Remote mentoring platforms were utilised to encourage asynchronous supervision. A database of trainers was collated to facilitate triggered assessments. These approaches promoted a conducive environment and a commitment to learning. Repeat survey results support this.
CONCLUSIONS: Lung ultrasound accreditation remains a complex educational training pathway. Utilising an education framework, recruiting a multidisciplinary team, ensuring a multi-pronged approach, and fostering a commitment to learning can improve accreditation success.

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.

The validity of the traditional nutritional assessment tools in intensive care settings might be compromised when the patient has conditions such as oedema and inflammation. Ultrasound (US) is considered a non-invasive, bedside tool that can be utilized to assess changes in muscle mass. Hence, US could guide healthcare practitioners in identifying the varying degrees of malnutrition and adjusting the nutritional prescription accordingly. This review discusses the currently available data regarding the feasibility and practicality of using US measurements in intensive care settings. Overall, the data suggest that using US as part of the standard anthropometric assessment for critically ill patients is a promising tool to track variations in muscle mass. This has the potential to enhance nutritional prescription and tailor the provision of protein and energy to critically ill patients based on their lean body mass measurements. Therefore, it is recommended to train dietitians on utilizing US for body composition measurements.

Presents corrections to the paper, Multi-View Feature Transformation Based SVM+ for Computer-Aided Diagnosis of Liver Cancers With Ultrasound Image.

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.

OBJECTIVE: Preoperative imaging is now recommended in patients with suspected acute appendicitis (AA) by the World Society of Emergency Surgery. Our aims were (i) to describe our local practice and (ii) to evaluate the efficiency of performing ultrasound (US) and/or computed tomography (CT) by assessing management failure, specificity and sensitivity, and length of stay in the emergency department (ED).
METHODS: This single-center retrospective study included all patients who underwent US or CT for the management of suspected AA. Patients were included if they were admitted to the ED in February or June between 2012 and 2021.
RESULTS: The study included 339 patients. US was performed in 278 patients (82%), of whom 91 also had a second-line CT (31.3%). There was a significant increase in the rate of CT over the inclusion period. Three percent (3%) of the patients had management failure and a higher age and CT or US + CT were significantly associated with the risk of management failure. Length of stay in the ED increased significantly when a second-line CT was performed. The sensitivity and specificity of US were 84.8% and 93.2%, respectively. Sensitivity was significantly different from CT (100%, p = 0.03) but not specificity (87.9%, p = 0.29). Both US and CT results were more likely to be considered for further management if positive. The vast majority of patients with negative or inconclusive results were admitted in surgical wards or underwent a second-line examination.
CONCLUSIONS: If available in the hospital together with CT, US should probably be performed systematically and as a first-line examination in patients with suspected acute appendicitis.