Ultrasound Quarterly最新文献

Abstract: This study aimed to explore the feasibility of using the ultrasound quantitative scoring system to guide the selection of surgical methods for a cesarean scar ectopic pregnancy (CSEP). A retrospective analysis was conducted of the medical records of 117 cases of patients with a CSEP. All patients underwent transvaginal ultrasound examination before treatment, and the ultrasound results were compared with the results of surgical exploration. The treatment methods that were delivered for the 2 patient groups with CSEP were analyzed, and agreement between the predicted and actual treatment protocols was tested using the kappa consistency test. Residual myometrial thickness at the scar site was mainly concentrated above 3 mm in the low-risk group, with 70 cases accounting for 76.9%, while in the high-risk group, this was primarily in the range of 1-3 mm with 18 cases accounting for 69.2%. The grading of nourishing blood supply to the gestational sac showed that 96.7% of patients in the low-risk group had Grade I and Grade II blood flow, while 84.6% of the high-risk group had Grade I and Grade II blood flow; Grade II blood flow was predominant in the high-risk group. The majority of patients in both the low- and high-risk groups experienced intraoperative blood loss of ≤50 mL (93.9% vs 80.6%, respectively). The consistency test between the actual and predicted treatment methods yielded a kappa coefficient of 0.644, indicating consistency between the two. For patients with CSEP in the low-risk group (score <4), the ultrasound quantitative scoring system could provide individualized assessment and offer clinically valuable treatment protocols.

Abstract: The non-mass breast lesions on ultrasound (US) are a group of challenging pathology. We aimed to standardize these grayscale findings and investigate the effectiveness of superb microvascular imaging (SMI) and shear wave elastography (SWE). A total of 195 lesions were evaluated by B-mode US, SWE, and SMI in the same session. A "NON-MASS model" was built on grayscale US to group the lesions only as areas and those with associated features: microcalcifications, architectural distortion, ductal changes, and microcysts. The mean stiffness parameters Emean, Eratio, and mean vascular index (VI) were recorded following consecutive measurements. Besides, the microvascularity was graded based on Adler's classification (grades 0 to 3). Lesions were divided into 3 groups: benign, category B3, and malignant. One hundred twelve (57.4%) lesions were benign, 23 (11.8%) were B3, and 60 were (30.8%) in the malignant category. Thirty-eight (19.5%) lesions were observed only as an area, whereas associated features were present in 157 lesions (80.5%). Distortion was the only associated feature predicting malignancy among the grayscale findings (P < 0.001). There was a significant difference between malignant and nonmalignant (benign and B3) groups in terms of Adler's grade, Emean, Eratio, and VI values (P < 0.001). Sensitivity, specificity, and accuracy increased when advanced imaging parameters were added to grayscale findings (P < 0.001). In the presence of microcalcifications, architectural distortion, high elasticity, and hypervascularity in the "NON-MASS" imaging model, the suspicion of malignancy increases. The non-mass findings and advanced imaging techniques have the potential to find greater coverage in the following versions of BI-RADS atlas.

Abstract: The risk of malignancy in nonvisualized ovaries on pelvic ultrasound is presumed to be close to zero per imaging correlation; the goal of this manuscript is to define the risk of malignancy in nonvisualized ovaries on pelvic ultrasound as defined by surgical pathology. Records for patients with pelvic ultrasound and surgical pathology containing the word "ovary" or "ovaries" performed at our institution between 10/1/2015 and 9/30/2021 were reviewed. Data for ovarian visualization were extracted from the radiology report and correlated with surgical pathology results within each ovary. Eighty-seven ovaries in 71 patients out of 422 ovaries (20.6%) in 215 eligible patients were not visualized on ultrasound. Twenty ovaries were excluded because imaging showed large pelvic mass, and 19 ovaries were excluded because surgical pathology for the ovary of interest was not available. A total of 48 ovaries in 37 patients were nonvisualized and had available surgical pathology. Out of 48 nonvisualized ovaries, 31 were normal on surgical pathology and 17 had abnormalities, with 15 benign lesions (12 of which were ≤1 cm in size). Two ovaries in 1 patient contained malignant lesions; although the ovaries were not visualized on ultrasound, the scan demonstrated peritoneal carcinomatosis. In conclusion, a high proportion of ovaries (20.6%, 87/422) are not visualized on pelvic ultrasound, and surgical pathology reveals ovarian lesions in 35.4% (17/48) of nonvisualized ovaries on pelvic ultrasound, with the majority being subcentimeter benign lesions. In the absence of peritoneal carcinomatosis, nonvisualized ovaries had no malignant lesions.

Abstract: Reliability of muscle thickness assessed in B-mode ultrasound has been confirmed in adults but remains a less direct assessment of muscle mass than muscle volume (MV). The aim of this prospective monocentric study was to assess the interday reliability of MV determined by the 2D-panoramic ultrasound in older hospitalized patients.Ten participants hospitalized in geriatric ward (mean age = 84) underwent 2 sessions of panoramic ultrasound scanning 1 week apart for MV assessment and DXA for leg lean mass (LLM) assessment when it was possible. The interday reliability of MV assessed using the intraclass correlation coefficient (ICC) was excellent, whatever was the muscle analyzed: ICC = 0.99 for tibialis anterior; ICC = 0.99 for vastus lateralis; ICC = 1 for rectus femoris. The pilot analysis of its association with leg lean mass revealed a good association between the vastus lateralis MV and LLM, but this needs to be confirmed on a larger number of participants (Pearson correlation coefficient = 0.91, P = 0.03).The 2D-panoramic ultrasound system for the determination of MV was reliable in older hospitalized patients with measurements performed on the patient's bed. MV seemed to be more correlated to LLM than muscle thickness. Future research will need to reduce the data processing time maybe with automation of data measurement.

Abstract: Cranial ultrasound (CUS) is an indispensable tool in the evaluation of intracranial pathology in premature and term neonates and older infants. Familiarity with standard cranial ultrasound techniques and parameters, normal anatomy, and commonly encountered abnormalities is crucial for providing appropriate care for these patients. This review provides a comprehensive overview of cranial ultrasound in clinical practice.

Abstract: This study aims to explore the value of real-time strain elastography (RTE) and contrast-enhanced ultrasonography (CEUS) in the diagnosis of breast BI-RADS 4 lesions. It collected 85 cases (totaling 85 lesions) diagnosed with breast BI-RADS 4 through routine ultrasound from October 2020 to December 2022 in Huangshan City People's Hospital. All lesions underwent RTE and CEUS examination before surgery, and the ImageJ software was used to measure the periphery of lesion images in the enhancement peak mode and grayscale mode to calculate the contrast-enhanced ultrasound area ratio. The diagnostic capabilities of single-modal and multimodal ultrasound examination for the malignancy of breast BI-RADS 4 lesions were compared using the receiver operating characteristic curve; the Spearman correlation analysis was adopted to evaluate the correlation between multimodal ultrasound and CEUS area ratio. As a result, among the 85 lesions, 51 were benign, and 34 were malignant. The areas under the curve (AUCs) of routine ultrasound (US), US + RTE, US + CEUS, and US + RTE + CEUS were 0.816, 0.928, 0.953, and 0.967, respectively, with the combined method showing a higher AUC than the single application. The AUC of the CEUS area ratio diagnosing breast lesions was 0.888. There was a strong positive correlation (r = 0.819, P < 0.001) between the diagnostic performance of US + RTE + CEUS and the CEUS area ratio. In conclusion, based on routine ultrasound, the combination of RTE and CEUS can further improve the differential diagnosis of benign and malignant lesions in breast BI-RADS 4.

Abstract: 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.

Methods: Twenty-three peer-reviewed articles on HRI measurements published between 2018 through 2023 were reviewed, and 11 were selected based on common subjects. The search terms included "hepatorenal index," "HRI," "HRI ultrasound," "hepatorenal ultrasound index," and "HRI ultrasound measurement."Three common subject areas were identified in the literature and synthesized down to 11 articles. The common subjects identified were HRI technique, HRI limitations, and HRI diagnostic accuracy. The matrix provided a quick overview of the general information in each piece, aiding in the paper's overall organization. Thirteen articles were rejected as not relevant or out of date. The research question leading this review was, "What does the literature say about the value of HRI in determining moderate to severe hepatic steatosis?"

Results: The literature revealed that HRI could be valuable in determining moderate to severe hepatic steatosis. HRI could not accurately determine normal or mild steatosis and has several limitations.

Conclusions: HRI is a more objective method for determining the degree of hepatic steatosis compared with traditional B-mode ultrasound scoring and does not require additional or specialized equipment. Many studies excluded patients with various liver diseases, which may not make HRI a practical tool for clinical usefulness. Further studies should be conducted with larger patient cohorts, a greater degree of hepatic steatosis, and determine specific standardized cutoff values.

Abstract: We aimed to develop and validate a nomogram based on conventional ultrasound (CUS) radiomics model to differentiate radial scar (RS) from invasive ductal carcinoma (IDC) of the breast. In total, 208 patients with histopathologically diagnosed RS or IDC of the breast were enrolled. They were randomly divided in a 7:3 ratio into a training cohort (n = 145) and a validation cohort (n = 63). Overall, 1316 radiomics features were extracted from CUS images. Then a radiomics score was constructed by filtering unstable features and using the maximum relevance minimum redundancy algorithm and the least absolute shrinkage and selection operator logistic regression algorithm. Two models were developed using data from the training cohort: one using clinical and CUS characteristics (Clin + CUS model) and one using clinical information, CUS characteristics, and the radiomics score (radiomics model). The usefulness of nomogram was assessed based on their differentiating ability and clinical utility. Nine features from CUS images were used to build the radiomics score. The radiomics nomogram showed a favorable predictive value for differentiating RS from IDC, with areas under the curve of 0.953 and 0.922 for the training and validation cohorts, respectively. Decision curve analysis indicated that this model outperformed the Clin + CUS model and the radiomics score in terms of clinical usefulness. The results of this study may provide a novel method for noninvasively distinguish RS from IDC.