UNASSIGNED: Researchers have delved into noninvasive diagnostic methods of renal fibrosis (RF) in chronic kidney disease, including ultrasound (US), magnetic resonance imaging (MRI), and radiomics. However, the value of these diagnostic methods in the noninvasive diagnosis of RF remains contentious. Consequently, the present study aimed to systematically delineate the accuracy of the noninvasive diagnosis of RF.
UNASSIGNED: A systematic search covering PubMed, Embase, Cochrane Library, and Web of Science databases for all data available up to 28 July 2023 was conducted for eligible studies.
UNASSIGNED: We included 21 studies covering 4885 participants. Among them, nine studies utilized US as a noninvasive diagnostic method, eight studies used MRI, and four articles employed radiomics. The sensitivity and specificity of US for detecting RF were 0.81 (95% CI: 0.76-0.86) and 0.79 (95% CI: 0.72-0.84). The sensitivity and specificity of MRI were 0.77 (95% CI: 0.70-0.83) and 0.92 (95% CI: 0.85-0.96). The sensitivity and specificity of radiomics were 0.69 (95% CI: 0.59-0.77) and 0.78 (95% CI: 0.68-0.85).
UNASSIGNED: The current early noninvasive diagnostic methods for RF include US, MRI, and radiomics. However, this study demonstrates that US has a higher sensitivity for the detection of RF compared to MRI. Compared to US, radiomics studies based on US did not show superior advantages. Therefore, challenges still exist in the current radiomics approaches for diagnosing RF, and further exploration of optimized artificial intelligence (AI) algorithms and technologies is needed.

OBJECTIVE: Beyond the Thyroid Imaging Reporting and Data System (TIRADS) classification of thyroid nodules, additional factors must be weighed in the decision to perform fine needle aspiration (FNA). In this study, we aimed to identify risk factors for malignancy in patients with ultrasound-classified Chinese-TIRADS (C-TIRADS) 4 A nodules.
METHODS: Patients who underwent thyroid FNA at our institution between May 2021 and September 2022 were enrolled. We collected demographic data, including age, sex, previous radiation exposure, and family history. An in-person questionnaire was used to collect lifestyle data, such as smoking habits and alcohol consumption. Body mass index (BMI) was calculated. The serum levels of thyroid stimulating hormone (TSH), thyroid peroxidase antibody (TPOAb), and thyroglobulin antibody (TGAb) were measured. Prior to FNA, ultrasonic inspection reports were reviewed. The cytologic diagnoses for FNA of thyroid nodules followed the Bethesda System for Reporting Thyroid Cytopathology (2017).
RESULTS: Among the 252 C-TIRADS 4 A nodules, 103 were malignant. Compared to those in the benign group, the patients in the malignant group had a younger age (42.2 ± 13.6 vs. 51.5 ± 14.0 years, P < 0.001). Logistic regression showed that advanced age was associated with a lower risk of malignancy in C-TIRADS 4 A nodules (OR = 0.95, 95% CI 0.93 ~ 0.97, P < 0.001). We demonstrated a decreased risk of malignancy in patients with 48.5 years or older.
CONCLUSIONS: Advanced age was associated with a decreased risk of malignancy in patients with C-TIRADS 4 A nodules. This study indicated that in addition to sonographic characteristics, patient age should be considered when assessing the risk of malignancy.

暂无摘要。

.

.

.

.

OBJECTIVE: To evaluate the agreement between quantitative ultrasound system fat fraction (USFF) and proton magnetic resonance spectroscopy (1H-MRS) and the diagnostic value of USFF in assessing metabolic-associated fatty liver disease (MAFLD).
METHODS: The participants with or suspected of MAFLD were prospectively recruited and underwent 1H-MRS, USFF, and controlled attenuation parameter (CAP) measurements. The correlation between USFF and 1H-MRS was assessed using Pearson correlation coefficients. The USFF diagnostic performance for different grades of steatosis was evaluated using receiver operating characteristic curve analysis (ROC) and was compared with CAP, visual hepatic steatosis grade (VHSG).
RESULTS: A total of 113 participants (mean age 44.79 years ± 13.56 (SD); 71 males) were enrolled, of whom 98 (86.73%) had hepatic steatosis (1H-MRS ≥ 5.56%). USFF showed a good correlation (Pearson r = 0.76) with 1H-MRS and showed a linear relationship, which was superior to the correlation between CAP and 1H-MRS (Pearson r = 0.61). The USFF provided high diagnostic performance for different grades of hepatic steatosis, with ROC from 0.84 to 0.98, and the diagnostic performance was better than that of the CAP and the VHSG. The cut-off values of the USFF were different for various grades of steatosis, and the cut-off values for S1, S2, and S3 were 12.01%, 19.98%, and 22.22%, respectively.
CONCLUSIONS: There was a good correlation between USFF and 1H-MRS. Meanwhile, USFF had good diagnostic performance for hepatic steatosis and was superior to CAP and VHSG. USFF represents a superior method for noninvasive quantitative assessment of MAFLD.
UNASSIGNED: Quantitative ultrasound system fat fraction (USFF) accurately assesses liver fat content and has a good correlation with magnetic resonance spectroscopy (1H-MRS) for the assessment of metabolic-associated fatty liver disease (MAFLD), as well as for providing an accurate quantitative assessment of hepatic steatosis.
CONCLUSIONS: Current diagnostic and monitoring modalities for metabolic-associated fatty liver disease have limitations. USFF correlated well with 1H-MRS and was superior to the CAP. USFF has good diagnostic performance for steatosis, superior to CAP and VHSG.

OBJECTIVE: This study was designed to explore and validate the value of different machine learning models based on ultrasound image-omics features in the preoperative diagnosis of lymph node metastasis in pancreatic cancer (PC).
METHODS: This research involved 189 individuals diagnosed with PC confirmed by surgical pathology (training cohort: n = 151; test cohort: n = 38), including 50 cases of lymph node metastasis. Image-omics features were extracted from ultrasound images. After dimensionality reduction and screening, eight machine learning algorithms, including logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), extra trees (ET), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP), were used to establish image-omics models to predict lymph node metastasis in PC. The best omics prediction model was selected through ROC curve analysis. Machine learning models were used to analyze clinical features and determine variables to establish a clinical model. A combined model was constructed by combining ultrasound image-omics and clinical features. Decision curve analysis (DCA) and a nomogram were used to evaluate the clinical application value of the model.
RESULTS: A total of 1561 image-omics features were extracted from ultrasound images. 15 valuable image-omics features were determined by regularization, dimension reduction, and algorithm selection. In the image-omics model, the LR model showed higher prediction efficiency and robustness, with an area under the ROC curve (AUC) of 0.773 in the training set and an AUC of 0.850 in the test set. The clinical model constructed by the boundary of lesions in ultrasound images and the clinical feature CA199 (AUC = 0.875). The combined model had the best prediction performance, with an AUC of 0.872 in the training set and 0.918 in the test set. The combined model showed better clinical benefit according to DCA, and the nomogram score provided clinical prediction solutions.
CONCLUSIONS: The combined model established with clinical features has good diagnostic ability and can be used to predict lymph node metastasis in patients with PC. It is expected to provide an effective noninvasive method for clinical decision-making, thereby improving the diagnosis and treatment of PC.

Untethered miniature soft robots have significant application potentials in biomedical and industrial fields due to their space accessibility and safe human interaction. However, the lack of selective and forceful actuation is still challenging in revolutionizing and unleashing their versatility. Here, we propose a focused ultrasound-controlled phase transition strategy for achieving millimeter-level spatially selective actuation and Newton-level force of soft robots, which harnesses ultrasound-induced heating to trigger the phase transition inside the robot, enabling powerful actuation through inflation. The millimeter-level spatial resolution empowers single robot to perform multiple tasks according to specific requirements. As a concept-of-demonstration, we designed soft robot for liquid cargo delivery and biopsy robot for tissue acquisition and patching. Additionally, an autonomous control system is integrated with ultrasound imaging to enable automatic acoustic field alignment and control. The proposed method advances the spatiotemporal response capability of untethered miniature soft robots, holding promise for broadening their versatility and adaptability.