Journal of Clinical Ultrasound最新文献

Purpose: To analyze ultrasound findings and investigate the epidemiology, intervention efficacy, and associated factors of developmental dysplasia of the hip (DDH) in infants aged 0-6 months.

Methods: This retrospective study analyzed 6189 infants (3360 males, 54.3%) who underwent initial routine hip ultrasound at Shaoxing Maternal and Child Health Hospital (April 2022 to June 2024). Outcomes included DDH detection rate, Graf classification, laterality, and early intervention success rate (conversion to Graf Type I). Descriptive statistics summarized outcomes including detection rates, distributions, and intervention success rates while univariate and multivariate logistic regression identified influencing factors.

Results: The DDH detection rate was 3.2% (196/6189), primarily left-sided (64.73%), and Graf Type IIa/b (90.82%). The early intervention success rate was 98.95% (189/191). Female sex (aOR = 19.76, 95% CI: 11.91-32.81), breech delivery (aOR = 3.97, 95% CI: 2.47-6.39), family history (aOR = 1.54, 95% CI: 1.01-2.33), swaddling history (aOR = 2.44, 95% CI: 1.58-3.76), and gluteal fold asymmetry (aOR = 2.67, 95% CI: 1.86-3.84) were risk factors (all p < 0.05). First-born (aOR = 0.29, 95% CI: 0.20-0.42) and vaginal delivery (aOR = 0.25, 95% CI: 0.18-0.35) were protective.

Conclusion: This single-center study delineates the clinical profile of DDH, demonstrates a remarkably high success rate of early intervention, and identifies its independent influencing factors, providing evidence for risk-stratified screening while noting limited generalizability.

Purpose: In patients with intermediate-risk chest pain, a functional stress study is important for therapeutic strategies. However, many physicians encounter challenges in clinical decisions with biases of non-ischemic pain from resting-regional wall motion abnormality (RWMA) related to the prior myocardial scar. We aimed to use adenosine stress myocardial contrast echocardiography (MCE) to assess perfusion defects (PD) and predict cardiac events in these patients.

Methods: We retrospectively analyzed 496 patients, categorizing them into four groups based on resting-RWMA and PD on MCE. The study endpoint comprised coronary revascularization, worsening heart failure, and cardiac death.

Results: Patients with severe RWMA and PD were older and exhibited a higher prevalence of diabetes, atrial fibrillation, heart failure, coronary artery disease, and coronary intervention. Their echocardiography revealed increased cardiac chamber dilation, lower left ventricular ejection fraction, and reduced tissue Doppler velocities. Over a median follow-up of 38 months, a study outcome occurred in 54 (11%) patients. Multivariate analysis indicated a 13-fold increase in outcomes among patients with PD and resting-RWMA ≥ 2 segments, respectively. PD ≥ 2 segments, rather than resting-RWMA, independently carried an increased outcome on adjusted Kaplan-Meier analysis.

Conclusion: PD could complement resting-RWMA and be a robust predictor of cardiac events in intermediate-risk patients.

Double orifice mitral valve (DOMV) is a very rare cardiac lesion that presents primarily as a left ventricular inflow tract lesion, often combined with outflow tract lesions such as bicuspid aortic valve, aortic stenosis, and subaortic stenosis. However, reports involving DOMVs are rare. Echocardiography is the primary method of examination of DOMVs. This article focuses on describing the case of congenital double-orifice mitral valve combined with bicuspid aortic valve in an elderly patient.

Objective: This study aims to develop and validate a deep learning radiomics (DLR) model based on ultrasound images for non-invasively distinguishing the myometrial infiltration (MI) of patients with endometrial cancer (EC).

Methods: We retrospectively collected 310 patients with EC who underwent surgical resection from center 1 from September 2017 to January 2025, forming a training group and a validation group. An external testing group was comprised of 80 patients from center 2. We extracted deep learning (DL) features and radiomics features from ultrasound images, establishing a DLR model after dimensionality reduction. The receiver operating characteristic analysis was used to evaluate the practicality of the proposed model. The utility of the proposed model was evaluated using receiver operating characteristic, calibration, and decision curve analysis.

Results: A total of 390 EC patients were included in the study. In the validation set, the AUC of the radiomics model was 0.874 (0.774-0.949), and in the testing group, the AUC of the DL model was 0.844 (0.699-0.957). The DLR model was superior to radiomics models and DL models in interpreting images, and also outperformed the diagnostic performance of advanced ultrasound physicians.

Conclusion: The DLR model based on ultrasound images can accurately and non-invasively distinguish the MI depth of EC patients, assisting doctors in formulating more favorable treatment plans for patients.

Frozen shoulder (FS), or adhesive capsulitis, remains a challenging clinical entity due to its insidious onset, variable presentation, and diagnostic overlap with other shoulder pathologies. While FS is traditionally diagnosed clinically, musculoskeletal ultrasound (MSKUS) is emerging as a valuable adjunct tool capable of identifying early-stage structural and vascular changes. This review synthesizes the current evidence on MSKUS's diagnostic utility, highlighting its capacity to visualize characteristic features such as capsular thickening, rotator interval (RI) hypervascularity, and effusion in the long head of the biceps tendon (LHBT) sheath-particularly prominent in early or intermediate disease stages. Although MSKUS is highly operator-dependent and demands substantial training, its integration into clinical assessment may enhance diagnostic precision, particularly when FS is suspected early. As the field advances, combining MSKUS findings with metabolic and inflammatory biomarkers may support a pathophysiological classification of FS, paving the way for more personalized, algorithm-based interventions. In summary, MSKUS provides a safe, accessible, and dynamic modality for detecting capsuloligamentous changes in FS. Its role in early diagnosis, differential screening, treatment monitoring, and future integration with systemic biomarkers supports its growing clinical relevance in both orthopedic and interdisciplinary settings.

Adrenal teratoma, a rare germ cell neoplasm, is often clinically underrecognized due to nonspecific manifestations, complicating radiological characterization. This article summarizes its ultrasonographic and computed tomography (CT) features, establishes evidence-based differential diagnosis strategies, and presents a representative case.

Malignant melanoma exhibits high metastatic potential, yet metastasis to the gallbladder and pancreas is exceptionally rare. We report a 78-year-old male patient who developed epigastric discomfort 7 months after undergoing amputation for right-foot malignant melanoma. Imaging studies revealed space-occupying lesions in the gallbladder and pancreatic head, which were pathologically confirmed as melanoma metastases. This case underscores the importance of maintaining vigilance for metastases to unusual anatomical sites in melanoma patients presenting with gastrointestinal symptoms, and highlights the diagnostic value of multimodal imaging approaches in such scenarios.

Background: Deep learning (DL) has enabled advances in ultrasound imaging, but challenges like limited datasets and device variability hinder progress. This study provides the first bibliometric overview of DL research in medical ultrasound.

Method: We retrieved related publications (2004-Apr 2025) on medical ultrasound and DL from the Web of Science Core Collection. Bibliometric tools (Bibliometrix, VOSviewer, CiteSpace) were used to quantify publication growth, country/institution contributions, and thematic evolution.

Results: A total of 3386 publications were included, with the annual output increasing steadily at an average growth rate of 31.02%. While China led all countries in publication volume (1355), the United Kingdom achieved the highest citation rate per article (55.5 citations). Fudan University was the most prolific institution (110), and key authors such as Luca Saba and Jasjit S. Suri formed the principal collaboration hubs. In terms of venues, Diagnostics published the most papers, whereas IEEE Transactions on Medical Imaging exerted the greatest citation impact (5646 citations; IF 8.9). Keyword analysis revealed core themes in classification, segmentation, and CNN-based diagnosis. From 2020 onward, ultrasound-specific topics (e.g., diagnosis, images) dominate; advanced architectures (e.g., U-Net) and emerging clinical concepts such as noninvasive assessment.

Conclusion: This mapping highlights a rapidly growing, internationally collaborative field. Emerging trends include advanced artificial intelligence (AI) architectures (U-Net, attention-based networks, vision Transformers) and a shift toward surgical and therapeutic applications. These insights underscore the need for multicenter validation, standardized data protocols, and clinically interpretable AI models to accelerate safe translation into practice.

Purpose: Accurate grading of prostate cancer is critical for treatment strategies and risk stratification. This study aims to develop a machine learning (ML) model integrating Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) pharmacokinetic parameters with Prostate-Specific Antigen (PSA) values to predict ISUP grade metastatic risk groups.

Methods: This retrospective study included 102 patients with histologically confirmed prostate cancer. DCE-MRI pharmacokinetic parameters (Ktrans, Kep, Ve, CER, MaxSlope, IAUGC) were standardized. The dataset was balanced using the Synthetic Minority Oversampling Technique and split into training, validation, and test sets. ML models, including Random Forest, were evaluated using Area Under the Curve (AUC) values.

Results: The Random Forest classifier achieved the highest performance, with an AUC of 0.92. Precision-recall analysis identified an optimal threshold of 0.3, balancing sensitivity and specificity for high-risk group detection. SHAP analysis highlighted PSA, MaxSlope, and Kep as key predictors contributing to model accuracy.

Conclusion: Integrating DCE-MRI parameters with PSA values using ML algorithms enhances the prediction of ISUP grade metastatic risk groups. This method provides a robust tool for metastasis screening and personalized treatment in prostate cancer.