Role of muscle ultrasound in frailty assessment in older adults with type 2 diabetes mellitus.

BACKGROUND: Frailty is a geriatric syndrome that is characterized by increased vulnerability to intrinsic and extrinsic stressors due to decreased biologic reserves. Muscle ultrasound (US) is a valid and reliable method for assessing muscle quantity in older adults. The study aims to examine the relationship between frailty definitions and US-derived muscle parameters. METHODS: We conducted a cross-sectional study with type 2 diabetes mellitus outpatients in a tertiary hospital, and all participants underwent a comprehensive geriatric assessment. For frailty assessment, the Fried Frailty Phenotype (FFP), the Clinical Frailty Scale (CFS), and the Edmonton Frailty Scale (EFS) were performed. Muscle US measurements included Gastrocnemius Medialis (GM) muscle thickness, GM fascicle length, GM pennation angle, Rectus Femoris (RF) muscle thickness, Rectus Femoris cross-sectional area (RFCSA), Rectus Abdominis (RA) muscle thickness, External Oblique (EO) muscle thickness, Internal Oblique (IO) muscle thickness, and Transverse Abdominis (TA) muscle thickness. RESULTS: In all, 373 participants were included in the study. The median age of participants was 72.7 ± 5.9 years, and 64.6% of them were female. According to the FFP, 18.2% of the participants were living with frailty, 56% of them were pre-frail; 57.4% of them were living with frailty according to the CFS; 25.2% of them were living with frailty, and 20.6% of them were pre-frail according to the EFS. The FFP, CFS, and EFS scores were related to muscle thickness of GM, RF, and RA, fascicle length of GM, and pennation angle of GM and RFCSA. Particularly, GM pennation angle, RF muscle thickness, and RFCSA were associated with an increased risk of frailty. Besides muscle thickness of GM, RF, and RA, fascicle length of GM, pennation angle of GM, and RFCSA were significant for predicting the presence of frailty. CONCLUSIONS: US-derived regional muscle measurements are associated with frailty definitions (in both physical, cumulative deficit, and multidimensional models) in a diabetic geriatric population.

Explore the value of carotid ultrasound radiomics nomogram in predicting ischemic stroke risk in patients with type 2 diabetes mellitus.

Background and objective: Type 2 Diabetes Mellitus (T2DM) with insulin resistance (IR) is prone to damage the vascular endothelial, leading to the formation of vulnerable carotid plaques and increasing ischemic stroke (IS) risk. The purpose of this study is to develop a nomogram model based on carotid ultrasound radiomics for predicting IS risk in T2DM patients. Methods: 198 T2DM patients were enrolled and separated into study and control groups based on IS history. After manually delineating carotid plaque region of interest (ROI) from images, radiomics features were identified and selected using the least absolute shrinkage and selection operator (LASSO) regression to calculate the radiomics score (RS). A combinatorial logistic machine learning model and nomograms were created using RS and clinical features like the triglyceride-glucose index. The three models were assessed using area under curve (AUC) and decision curve analysis (DCA). Results: Patients were divided into the training set and the testing set by the ratio of 0.7. 4 radiomics features were selected. RS and clinical variables were all statically significant in the training set and were used to create a combination model and a prediction nomogram. The combination model (radiomics + clinical nomogram) had the largest AUC in both the training set and the testing set (0.898 and 0.857), and DCA analysis showed that it had a higher overall net benefit compared to the other models. Conclusions: This study created a carotid ultrasound radiomics machine-learning-based IS risk nomogram for T2DM patients with carotid plaques. Its diagnostic performance and clinical prediction capabilities enable accurate, convenient, and customized medical care.

[Expert consensus on the application of critical care ultrasonography in invasive procedures].

The evolution of critical care medicine is inextricably linked to the development of critical care procedures. These procedures not only facilitate diagnosis and treatment of critically ill patients, but also provide valuable insights into disease pathophysiology. While critical care interventions offer undeniable benefits, the potential for iatrogenic complications necessitates careful consideration. The recent surge in critical care ultrasound (US) utilization is a testament to its unique advantages: non-invasiveness, real-time bedside availability, direct visualization of internal structures, elimination of ionizing radiation exposure, repeatability, and relative ease of learning. Recognizing the need to optimize procedures and minimize complications, critical care utrasound study group of Beijing critical care ultrasound research assocition convened a panel of critical care experts to generate this consensus statement. This document serves as a guide for healthcare providers, aiming to ensure patient safety and best practices in critical care.

Early thrombus detection in the extracorporeal membrane oxygenation circuit by noninvasive real-time ultrasonic sensors.

Thrombus formation in extracorporeal membrane oxygenation (ECMO) remains a major concern as it can lead to fatal outcomes. To the best of our knowledge, there is no standard non-invasive method for quantitatively measuring thrombi. This study's purpose was to verify thrombus detection in an ECMO circuit using novel, non-invasive ultrasonic sensors in real-time, utilizing the fact that the ultrasonic velocity in a thrombus is known to be higher than that in the blood. Ultrasonic sensors with a customized chamber, an ultrasonic pulse-receiver, and a digital storage oscilloscope (DSO) were used to set up the measuring unit. The customized chamber was connected to an ECMO circuit primed with porcine blood. Thrombi formed from static porcine blood were placed in the circuit and ultrasonic signals were extracted from the oscilloscope at various ECMO flow rates of 1-4 L/min. The ultrasonic signal changes were successfully detected at each flow rate on the DSO. The ultrasonic pulse signal shifted leftward when a thrombus passed between the two ultrasonic sensors and was easily detected on the DSO screen. This novel real-time non-invasive thrombus detection method may enable the early detection of floating thrombi in the ECMO system and early management of ECMO thrombi.

Ultrasonography of the normal donkey tarsus (equus asinus).

Tarsal joint illness is a frequent source of hind limb lameness due to the complex anatomy of the region and the presence of numerous bony and soft tissue structures. Proper lameness diagnosis aims to discover the structure provoking lameness. Ultrasonography documents valuable information of soft tissues and characterizes soft tissue injuries that have heretofore been difficult to obtain either noninvasively or via radiography. The objectives of the current study were to develop and describe a standardized ultrasonographic protocol for investigation of the tarsal region in donkeys. The donkey tarsal anatomy was investigated in 5 cadavers and the tarsi of 11 healthy lameness free adult donkeys were echographically investigated. The dorsal, plantar, lateral and medial aspects of the tarsal region were substantially evaluated at four anatomical landmarks in both the longitudinal and horizontal planes using a multi-frequency 5-12 MHz linear transducer. Sonoanatomy of the extensor and flexor tarsal tendons, collateral and plantar ligaments, and synovial pouches was delineated and described. Systematic echography of the tarsal region allowed accurate localization and thorough exploration of various soft tissues of clinical interest in the donkey tarsus. Sonograms provided in this study should serve as a reference database for tarsal ultrasonography in clinical circumstances.

Prospective measurement of the width of cerebrospinal fluid spaces by cranial ultrasound in neurologically healthy children aged 0-19 months.

BACKGROUND: Ultrasound (US) is often the first method used to look for brain or cerebrospinal fluid (CSF) space pathologies. Knowledge of normal CSF width values is essential. Most of the available US normative values were established over 20 years ago, were obtained with older equipment, and cover only part of the age spectrum that can be examined by cranial US. This prospective study aimed to determine the normative values of the widths of the subarachnoid and internal CSF spaces (craniocortical, minimal and maximal interhemispheric, interventricular, and frontal horn) for high-resolution linear US probes in neurologically healthy infants and children aged 0-19 months and assess whether subdural fluid collections can be delineated. METHODS: Two radiologists measured the width of the CSF spaces with a conventional linear probe and an ultralight hockey-stick probe in neurologically healthy children not referred for cranial or spinal US. RESULTS: This study included 359 neurologically healthy children (nboys = 178, 49.6%; ngirls = 181, 50.4%) with a median age of 46.0 days and a range of 1-599 days. We constructed prediction plots, including the 5th, 50th, and 95th percentiles, and an interactive spreadsheet to calculate normative values for individual patients. The measurements of the two probes and the left and right sides did not differ, eliminating the need for separate normative values. No subdural fluid collection was detected. CONCLUSION: Normative values for the widths of the subarachnoid space and the internal CSF spaces are useful for evaluating intracranial pathology, especially when determining whether an increase in the subarachnoid space width is abnormal.

Ultra high frequency ultrasound enables real-time visualization of blood supply from chorioallantoic membrane to human autosomal dominant polycystic kidney tissue.

Ultra high frequency (UHF) ultrasound enables the visualization of very small structures that cannot be detected by conventional ultrasound. The utilization of UHF imaging as a new imaging technique for the 3D-in-vivo chorioallantoic membrane (CAM) model can facilitate new insights into tissue perfusion and survival. Therefore, human renal cystic tissue was grafted onto the CAM and examined using UHF ultrasound imaging. Due to the unprecedented resolution of UHF ultrasound, it was possible to visualize microvessels, their development, and the formation of anastomoses. This enabled the observation of anastomoses between human and chicken vessels only 12 h after transplantation. These observations were validated by 3D reconstructions from a light sheet microscopy image stack, indocyanine green angiography, and histological analysis. Contrary to the assumption that the nutrient supply of the human cystic tissue and the gas exchange happens through diffusion from CAM vessels, this study shows that the vasculature of the human cystic tissue is directly connected to the blood vessels of the CAM and perfusion is established within a short period. Therefore, this in-vivo model combined with UHF imaging appears to be the ideal platform for studying the effects of intravenously applied therapeutics to inhibit renal cyst growth.

Differential diagnosis of thyroid nodules using heterogeneity quantification software on ultrasound images: correlation with the Bethesda system and surgical pathology.

Ultrasonography (US)-guided fine-needle aspiration cytology (FNAC) is the primary modality for evaluating thyroid nodules. However, in cases of atypia of undetermined significance (AUS) or follicular lesion of undetermined significance (FLUS), supplemental tests are necessary for a definitive diagnosis. Accordingly, we aimed to develop a non-invasive quantification software using the heterogeneity scores of thyroid nodules. This cross-sectional study retrospectively enrolled 188 patients who were categorized into four groups according to their diagnostic classification in the Bethesda system and surgical pathology [II-benign (B) (n = 24); III-B (n = 52); III-malignant (M) (n = 54); V/VI-M (n = 58)]. Heterogeneity scores were derived using an image pixel-based heterogeneity index, utilized as a coefficient of variation (CV) value, and analyzed across all US images. Differences in heterogeneity scores were compared using one-way analysis of variance with Tukey's test. Diagnostic accuracy was determined by calculating the area under the receiver operating characteristic (AUROC) curve. The results of this study indicated significant differences in mean heterogeneity scores between benign and malignant thyroid nodules, except in the comparison between III-M and V/VI-M nodules. Among malignant nodules, the Bethesda classification was not observed to be associated with mean heterogeneity scores. Moreover, there was a positive correlation between heterogeneity scores and the combined diagnostic category, which was based on the Bethesda system and surgical cytology grades (R = 0.639, p < 0.001). AUROC for heterogeneity scores showed the highest diagnostic performance (0.818; cut-off: 30.22% CV value) for differentiating the benign group (normal/II-B/III-B) from the malignant group (III-M/V&VI-M), with a diagnostic accuracy of 72.5% (161/122). Quantitative heterogeneity measurement of US images is a valuable non-invasive diagnostic tool for predicting the likelihood of malignancy in thyroid nodules, including AUS or FLUS.

A non-invasive preoperative prediction model for predicting axillary lymph node metastasis in breast cancer based on a machine learning approach: combining ultrasonographic parameters and breast gamma specific imaging features.

BACKGROUND: The most common route of breast cancer metastasis is through the mammary lymphatic network. An accurate assessment of the axillary lymph node (ALN) burden before surgery can avoid unnecessary axillary surgery, consequently preventing surgical complications. In this study, we aimed to develop a non-invasive prediction model incorporating breast specific gamma image (BSGI) features and ultrasonographic parameters to assess axillary lymph node status. MATERIALS AND METHODS: Cohorts of breast cancer patients who underwent surgery between 2012 and 2021 were created (The training set included 1104 ultrasound images and 940 BSGI images from 235 patients, the test set included 568 ultrasound images and 296 BSGI images from 99 patients) for the development of the prediction model. six machine learning (ML) methods and recursive feature elimination were trained in the training set to create a strong prediction model. Based on the best-performing model, we created an online calculator that can make a linear predictor in patients easily accessible to clinicians. The receiver operating characteristic (ROC) and calibration curve are used to verify the model performance respectively and evaluate the clinical effectiveness of the model. RESULTS: Six ultrasonographic parameters (transverse diameter of tumour, longitudinal diameter of tumour, lymphatic echogenicity, transverse diameter of lymph nodes, longitudinal diameter of lymph nodes, lymphatic color Doppler flow imaging grade) and one BSGI features (axillary mass status) were selected based on the best-performing model. In the test set, the support vector machines' model showed the best predictive ability (AUC = 0.794, sensitivity = 0.641, specificity = 0.8, PPV = 0.676, NPV = 0.774 and accuracy = 0.737). An online calculator was established for clinicians to predict patients' risk of ALN metastasis ( https://wuqian.shinyapps.io/shinybsgi/ ). The result in ROC showed the model could benefit from incorporating BSGI feature. CONCLUSION: This study developed a non-invasive prediction model that incorporates variables using ML method and serves to clinically predict ALN metastasis and help in selection of the appropriate treatment option.

Liver Imaging Reporting and Data System Contrast-Enhanced US Nonradiation Treatment Response Assessment Version 2024.

The American College of Radiology Liver Imaging Reporting and Data System (LI-RADS) standardizes the imaging technique, reporting lexicon, disease categorization, and management for patients with or at risk for hepatocellular carcinoma (HCC). LI-RADS encompasses HCC surveillance with US; HCC diagnosis with CT, MRI, or contrast-enhanced US (CEUS); and treatment response assessment (TRA) with CT or MRI. LI-RADS was recently expanded to include CEUS TRA after nonradiation locoregional therapy or surgical resection. This report provides an overview of LI-RADS CEUS Nonradiation TRA v2024, including a lexicon of imaging findings, techniques, and imaging criteria for posttreatment tumor viability assessment. LI-RADS CEUS Nonradiation TRA v2024 takes into consideration differences in the CEUS appearance of viable tumor and posttreatment changes within and in close proximity to a treated lesion. Due to the high sensitivity of CEUS to vascular flow, posttreatment reactive changes commonly manifest as areas of abnormal perilesional enhancement without washout, especially in the first 3 months after treatment. To improve the accuracy of CEUS for nonradiation TRA, different diagnostic criteria are used to evaluate tumor viability within and outside of the treated lesion margin. Broader criteria for intralesional enhancement increase sensitivity for tumor viability detection. Stricter criteria for perilesional enhancement limit miscategorization of posttreatment reactive changes as viable tumor. Finally, the TRA algorithm reconciles intralesional and perilesional tumor viability assessment and assigns a single LI-RADS treatment response (LR-TR) category: LR-TR nonviable, LR-TR equivocal, or LR-TR viable.

Multimode ultrasound imaging of an anal canal gastrointestinal stromal tumour (GIST).

Gastrointestinal stromal tumours (GISTs) can develop throughout the entire gastrointestinal tract, but these tumours are usually found in the stomach and small intestine. In this case, a rare GIST arising from the anal canal was investigated using high-frequency endoanal ultrasound and external three-dimensional ultrasound with tomographic ultrasound imaging. The endoanal approach revealed the inner structure of the tumour. External ultrasound was used to determine the relationship between the lesion and surrounding tissues. In the limited reports of anal canal GISTs, no other lesions have been correctly diagnosed preoperatively or displayed in detail on imaging. The multilayer structure of the anal sphincter and these lesions can be clearly displayed by a variety of ultrasound imaging methods, which are nonradiative, low-cost and easily accessible. Modern ultrasound has the potential for broad application in anal canal tumour diagnosis and surveillance.

Contrast-enhanced US in Renal Transplant Complications: Overview and Imaging Features.

Renal transplant is the first-line treatment of end-stage renal disease. The increasing number of transplants performed every year has led to a larger population of transplant patients. Complications may arise during the perioperative and postoperative periods, and imaging plays a key role in this scenario. Contrast-enhanced US (CEUS) is a safe tool that adds additional value to US. Contrast agents are usually administered intravenously, but urinary tract anatomy and complications such as stenosis or leak can be studied using intracavitary administration of contrast agents. Assessment of the graft and iliac vessels with CEUS is particularly helpful in identifying vascular and parenchymal complications, such as arterial or venous thrombosis and stenosis, acute tubular injury, or cortical necrosis, which can lead to graft loss. Furthermore, infectious and malignant graft involvement can be accurately studied with CEUS, which can help in detection of renal abscesses and in the differentiation between benign and malignant disease. CEUS is also useful in interventional procedures, helping to guide percutaneous aspiration of collections with better delimitation of the graft boundaries and to guide renal graft biopsies by avoiding avascular areas. Potential postprocedural vascular complications, such as pseudoaneurysm, arteriovenous fistula, or active bleeding, are identified with CEUS. In addition, newer quantification tools such as CEUS perfusion are promising, but further studies are needed to approve its use for clinical purposes. ©RSNA, 2024 Supplemental material is available for this article.

Hot-pack therapy increased gliding function of the iliotibial band during passive knee motion: An exploratory study.

INTRODUCTION: Quantifying soft tissue dynamics during joint motion is important for the valid assessment and development of effective therapeutic interventions for the soft tissues. This study aimed to examine the immediate effect of thermotherapy on gliding of the iliotibial band (ITB), including the subcutaneous tissue, and vastus lateralis (VL) muscle during passive knee joint motion. METHODS: Ten participants (age, 20.4 ± 0.7 years; height, 172.0 ± 8.9 cm; weight, 64.1 ± 9.7 kg; BMI, 21.6 ± 1.7 kg/m2) with no history of lower extremity surgery or neuromuscular disease participated in the study. An electrothermal hot pack with an internal temperature of 65 °C was applied to one of the lateral thighs, followed by measuring its stiffness using a durometer. Movements of both the ITB and VL were recorded using ultrasound imaging during isokinetic knee motion. The Farneback method and optical flow algorithm analysis software were adapted to create the movement velocity from ultrasound imaging. Gliding coefficient was calculated using the coefficient of correlation for each velocity in the proximal-distal direction during knee motion. The mean velocity during knee motion was calculated using absolute values. The differences between the pre-intervention values and between the pre- and post-intervention values were examined. RESULTS: After applying the hot pack, the stiffness significantly decreased (p = 0.01), and the mean velocity of the ITB significantly increased (p = 0.03). The gliding coefficient and VL mean velocity did not significant differ (p = 0.65 and p = 0.80, respectively) between pre- and post-hot-pack applications. CONCLUSIONS: Hot-pack therapy might increase gliding function of the ITB during passive knee motion.

A dose response analysis of exercise prescription variables for lateral abdominal muscle thickness and activation: A systematic review.

BACKGROUND: Various exercise programs are used to treat lateral abdominal muscle (LAM) impairments in people with low back pain. Factors comprising these programs include exercise type, session time, frequency, and program duration. However, specific clinical guidance about optimal exercise prescription is lacking. OBJECTIVES: To perform a dose-response analysis on exercise prescription variables for LAM thickness and activation as measured by ultrasound imaging. DESIGN: Systematic review METHOD: Databases were searched from their inception for studies examining the association between exercise interventions and LAM thickness/activation measured by ultrasound imaging in healthy individuals. Risk of bias was assessed using the Joanna Brigg's Institute critical appraisal tools. For each muscle, subgroup analyses were performed to determine the dose response of exercise prescription variables for LAM thickness and activation. Where there was insufficient data for subgroup analyses, data was narratively synthesised. RESULTS: Fourteen studies comprising 395 participants were included. Statistical and narrative synthesis revealed specific local abdominal exercises, programs from four weeks duration, three sessions per week and sessions of ≥30 min were associated with greatest improvements to LAM thickness. Only the variables exercise type, program duration and session frequency showed a significant between groups difference for the subgroup analysis. The main limitation was inability to perform subgroup analyses for all variables across all muscles measured at rest and during contraction, due to non-reporting of data. CONCLUSION: This review provides preliminary guidance to practitioners on how the LAM respond to different exercise dosages. Future research should trial these findings.

The difference in the upper trapezius deep fascia slides between individuals with and without myofascial pain syndrome: A case-control study.

INTRODUCTION: MPS is a chronic disorder caused by myofascial trigger points, leading to pain and limited neck movements due to impacted fascia. Studies have reported reduced fascia slides in chronic low back pain, but limited fascia slides in MPS patients are still unreported. AIM: We determined differences in upper trapezius' deep fascia slides between MPS and non-MPS participants. METHODS: Between January-August 2019, participants from diverse work sectors were recruited in Manila. An expert physiotherapist diagnosed MPS, while non-MPS participants performed full painless cervical movements. Participants underwent upper trapezius deep fascia scans on both shoulders while performing six cervical movements. An HS1 Konica Minolta ultrasound recorded the data. Two blinded physiotherapists used Tracker 5.0 © 2018 to analyze videos and quantify deep fascia slides by measuring the distance between two x-axis points. The Multivariate analysis of variance (MANOVA) assessed deep fascia slide differences in six active cervical movements. Pillai's Trace, with a range of 0-1 and a p-value of <0.05, was set. Effect sizes in individuals with and without MPS were calculated using Hedges' g and Cohen's d. RESULTS: Of the 327 participants (136 non-MPS, 191 MPS), 101 MPS participants had shoulder pain for <1 year and 103 experienced unilateral pain. The study examined 3800 ultrasound videos but found no significant difference in deep fascia slides across cervical movements between MPS and non-MPS groups (Pillai's Trace = 0.004, p = 0.94). Minor differences in deep fascia displacement were observed, with small effect sizes (g = 0.02-0.08). CONCLUSION: A limited deep fascia slide does not characterize MPS participants from non-MPS participants.

Super-resolution based Nodule Localization in Thyroid Ultrasound Images through Deep Learning.

BACKGROUND: Currently, it is difficult to find a solution to the inverse inappropriate problem, which involves restoring a high-resolution image from a lowresolution image contained within a single image. In nature photography, one can capture a wide variety of objects and textures, each with its own characteristics, most notably the high-frequency component. These qualities can be distinguished from each other by looking at the pictures. OBJECTIVE: The goal is to develop an automated approach to identify thyroid nodules on ultrasound images. The aim of this research is to accurately differentiate thyroid nodules using Deep Learning Technique and to evaluate the effectiveness of different localization techniques. METHODS: The method used in this research is to reconstruct a single super-resolution image based on segmentation and classification. The poor-quality ultrasound image is divided into several parts, and the best applicable classification is chosen for each component. Pairs of high- and lowresolution images belonging to the same class are found and used to figure out which image is high-resolution for each segment. Deep learning technology, specifically the Adam classifier, is used to identify carcinoid tumors within thyroid nodules. Measures, such as localization accuracy, sensitivity, specificity, dice loss, ROC, and area under the curve (AUC), are used to evaluate the effectiveness of the techniques. RESULTS: The results of the proposed method are superior, both statistically and qualitatively, compared to other methods that are considered one of the latest and best technologies. The developed automated approach shows promising results in accurately identifying thyroid nodules on ultrasound images. CONCLUSION: The research demonstrates the development of an automated approach to identify thyroid nodules within ultrasound images using super-resolution single-image reconstruction and deep learning technology. The results indicate that the proposed method is superior to the latest and best techniques in terms of accuracy and quality. This research contributes to the advancement of medical imaging and holds the potential to improve the diagnosis and treatment of thyroid nodules.

Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography.

Introduction: Ultrasound is instrumental in the early detection of thyroid nodules, which is crucial for appropriate management and favorable outcomes. However, there is a lack of clinical guidelines for the judicious use of thyroid ultrasonography in routine screening. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to leverage the ML approach in assessing the risk of thyroid nodules based on common clinical features. Methods: Data were sourced from a Chinese cohort undergoing routine physical examinations including thyroid ultrasonography between 2013 and 2023. Models were established to predict the 3-year risk of thyroid nodules based on patients' baseline characteristics and laboratory tests. Four ML algorithms, including logistic regression, random forest, extreme gradient boosting, and light gradient boosting machine, were trained and tested using fivefold cross-validation. The importance of each feature was measured by the permutation score. A nomogram was established to facilitate risk assessment in the clinical settings. Results: The final dataset comprised 4,386 eligible subjects. Thyroid nodules were detected in 54.8% (n=2,404) individuals within the 3-year observation period. All ML models significantly outperformed the baseline regression model, successfully predicting the occurrence of thyroid nodules in approximately two-thirds of individuals. Age, high-density lipoprotein, fasting blood glucose and creatinine levels exhibited the highest impact on the outcome in these models. The nomogram showed consistency and validity, providing greater net benefits for clinical decision-making than other strategies. Conclusion: This study demonstrates the viability of an ML-based approach in predicting the occurrence of thyroid nodules. The findings highlight the potential of ML models in identifying high-risk individuals for personalized screening, thereby guiding the judicious use of ultrasound in this context.

Ultrasound features of high-risk basal cell carcinoma: a systematic review.

Basal Cell Carcinoma (BCC) is the most prevalent skin cancer and continues to witness a surge in incidence rates. The categorization of BCC subtypes into low or high risk, guided by recurrence and invasiveness metrics, underscores the need for precise differentiation. While the punch biopsy remains the gold standard for diagnosis, its invasiveness prompts a need for non-invasive alternatives. Ultrasound (US) has emerged as a noteworthy candidate, gaining momentum in its potential to offer a less intrusive diagnostic approach. We conducted a systematic review regarding features of the high-risk subtypes of BCC on US. A thorough literature search of PubMed Medline, Embase, and CINAHL databases was conducted according to PRISMA guidelines and a total of nine studies meeting our inclusion criteria were included in this review. Evidence is still nascent but US features such as lesional shape, depth, hyperechoic spots, and color doppler may be helpful in differentiating high-risk BCC subtypes. However, further prospective studies with standardized interventions and outcome measures are required.