Comparative study of ultrasound attenuation analysis and controlled attenuation parameter in the diagnosis and grading of liver steatosis in non-alcoholic fatty liver disease patients.

PURPOSE: To assess the diagnostic performance of Ultrasound Attenuation Analysis (USAT) in the diagnosis and grading of hepatic steatosis in patients with non-alcoholic fatty liver disease (NAFLD) using Controlled Attenuation Parameters (CAP) as a reference. MATERIALS AND METHODS: From February 13, 2023, to September 26, 2023, participants underwent CAP and USAT examinations on the same day. We used manufacturer-recommended CAP thresholds to categorize the stages of hepatic steatosis: stage 1 (mild) - 240 dB/m, stage 2 (moderate) - 265 dB/m, stage 3 (severe) - 295 dB/m. Receiver Operating Characteristic curves were employed to evaluate the diagnostic accuracy of USAT and determine the thresholds for different levels of hepatic steatosis. RESULTS: Using CAP as the reference, we observed that the average USAT value increased with the severity of hepatic steatosis, and the differences in USAT values among the different hepatic steatosis groups were statistically significant (p < 0.05). There was a strong positive correlation between USAT and CAP (r = 0.674, p < 0.0001). When using CAP as the reference, the optimal cut-off values for diagnosing and predicting different levels of hepatic steatosis with USAT were as follows: the cut-off value for excluding the presence of hepatic steatosis was 0.54 dB/cm/MHz (AUC 0.96); for mild hepatic steatosis, it was 0.59 dB/cm/MHz (AUC 0.86); for moderate hepatic steatosis, it was 0.73 dB/cm/MHz (AUC 0.81); and for severe hepatic steatosis, it was 0.87 dB/cm/MHz (AUC 0.87). CONCLUSION: USAT exhibits strong diagnostic performance for hepatic steatosis and shows a high correlation with CAP values.

A validation of an entropy-based artificial intelligence for ultrasound data in breast tumors.

BACKGROUND: The application of artificial intelligence (AI) in the ultrasound (US) diagnosis of breast cancer (BCa) is increasingly prevalent. However, the impact of US-probe frequencies on the diagnostic efficacy of AI models has not been clearly established. OBJECTIVES: To explore the impact of using US-video of variable frequencies on the diagnostic efficacy of AI in breast US screening. METHODS: This study utilized different frequency US-probes (L14: frequency range: 3.0-14.0 MHz, central frequency 9 MHz, L9: frequency range: 2.5-9.0 MHz, central frequency 6.5 MHz and L13: frequency range: 3.6-13.5 MHz, central frequency 8 MHz, L7: frequency range: 3-7 MHz, central frequency 4.0 MHz, linear arrays) to collect breast-video and applied an entropy-based deep learning approach for evaluation. We analyzed the average two-dimensional image entropy (2-DIE) of these videos and the performance of AI models in processing videos from these different frequencies to assess how probe frequency affects AI diagnostic performance. RESULTS: The study found that in testing set 1, L9 was higher than L14 in average 2-DIE; in testing set 2, L13 was higher in average 2-DIE than L7. The diagnostic efficacy of US-data, utilized in AI model analysis, varied across different frequencies (AUC: L9 > L14: 0.849 vs. 0.784; L13 > L7: 0.920 vs. 0.887). CONCLUSION: This study indicate that US-data acquired using probes with varying frequencies exhibit diverse average 2-DIE values, and datasets characterized by higher average 2-DIE demonstrate enhanced diagnostic outcomes in AI-driven BCa diagnosis. Unlike other studies, our research emphasizes the importance of US-probe frequency selection on AI model diagnostic performance, rather than focusing solely on the AI algorithms themselves. These insights offer a new perspective for early BCa screening and diagnosis and are of significant for future choices of US equipment and optimization of AI algorithms.

The research on artificial intelligence-assisted breast diagnosis often relies on static images or dynamic videos obtained from ultrasound probes with different frequencies. However, the effect of frequency-induced image variations on the diagnostic performance of artificial intelligence models remains unclear. In this study, we aimed to explore the impact of using ultrasound images with variable frequencies on AI's diagnostic efficacy in breast ultrasound screening. Our approach involved employing a video and entropy-based feature breast network to compare the diagnostic efficiency and average two-dimensional image entropy of the L14 (frequency range: 3.0-14.0 MHz, central frequency 9 MHz), L9 (frequency range: 2.5-9.0 MHz, central frequency 6.5 MHz) linear array probe and L13 (frequency range: 3.6-13.5 MHz, central frequency 8 MHz), and L7 (frequency range: 3-7 MHz, central frequency 4.0 MHz) linear array probes. The results revealed that the diagnostic efficiency of AI models differed based on the frequency of the ultrasound probe. It is noteworthy that ultrasound images acquired with different frequency probes exhibit different average two-dimensional image entropy, while higher average two-dimensional image entropy positively affect the diagnostic performance of the AI model. We concluded that a dataset with higher average two-dimensional image entropy is associated with superior diagnostic efficacy for AI-based breast diagnosis. These findings contribute to a better understanding of how ultrasound image variations impact AI-assisted breast diagnosis, potentially leading to improved breast cancer screening outcomes.

Artificial intelligence for non-mass breast lesions detection and classification on ultrasound images: a comparative study.

BACKGROUND: This retrospective study aims to validate the effectiveness of artificial intelligence (AI) to detect and classify non-mass breast lesions (NMLs) on ultrasound (US) images. METHODS: A total of 228 patients with NMLs and 596 volunteers without breast lesions on US images were enrolled in the study from January 2020 to December 2022. The pathological results served as the gold standard for NMLs. Two AI models were developed to accurately detect and classify NMLs on US images, including DenseNet121_448 and MobileNet_448. To evaluate and compare the diagnostic performance of AI models, the area under the curve (AUC), accuracy, specificity and sensitivity was employed. RESULTS: A total of 228 NMLs patients confirmed by postoperative pathology with 870 US images and 596 volunteers with 1003 US images were enrolled. In the detection experiment, the MobileNet_448 achieved the good performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.999 (95%CI: 0.997-1.000),96.5%,96.9% and 96.1%, respectively. It was no statistically significant compared to DenseNet121_448. In the classification experiment, the MobileNet_448 model achieved the highest diagnostic performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.837 (95%CI: 0.990-1.000), 70.5%, 80.3% and 74.6%, respectively. CONCLUSIONS: This study suggests that the AI models, particularly MobileNet_448, can effectively detect and classify NMLs in US images. This technique has the potential to improve early diagnostic accuracy for NMLs.

Differential diagnostic value of periampullary mass: A nomogram established by random forest based on clinical characteristics and contrast-enhanced ultrasound.

PURPOSES: To develop a nomogram model for distinguishing benign from malignant ampullary lesions more intuitive and accurate. MATERIALS AND METHODS: A total of 124 patients with periampullary lesions from January 2016 to June 2020 were enrolled in this retrospective study. Their clinical information, ultrasound (US), dual contrast-enhanced ultrasound (DCEUS) and MRI image features were used for research. Twenty features were collected in our study. Random forest was used to select the first five most important indicators to construct the prediction model. RESULTS: Patients' age, common bile duct (CBD) diameter, the shape, vascularity, and boundary of lesion, lesion size with or without enlarged after CEUS, the enhancement patterns of arterial phase, the washout patterns of venous phase, CEUS diagnosis, and MRI diagnosis were statistically significant (p < 0.05). After screening for statistically significant indicators by random forest, the first five most important indicators were age, CBD diameter, the enhancement patterns of arterial phase, the washout patterns of venous phase, lesion size with or without enlarged after CEUS, which were used to construct nomogram. The area under curves (AUC) and 95% confidence intervals (CI) for nomogram, MRI + MRCP + DCEUS, DCEUS, MRI + MRCP were 0.98(0.94-1.00), 0.91(0.84-0.97), 0.89(0.80-0.98), 0.68(0.60-0.77), respectively. The sensitivity and specificity were 100.00% and 84.62% for nomogram, 88.29% and 92.31% for MRI + MRCP+DCEUS, 86.49% and 92.31% for DCEUS, 51.35%, and 100.00% for MRI + MRCP. CONCLUSIONS: We combined clinical indicators, gray-scale ultrasound characteristics, and CEUS characteristics to build the nomogram, which can be intuitively and accurately used for preoperative malignant prediction of ampullary lesion patients, worthy of clinical generalizability and application.

Nonalcoholic Fatty Liver Disease: Changes in Gut Microbiota and Blood Lipids.

Background and Aims: The global prevalence of nonalcoholic fatty liver disease (NAFLD) is 25%. This study aimed to explore differences in the gut microbial community and blood lipids between normal livers and those affected by NAFLD using 16S ribosomal deoxyribonucleic acid sequencing. Methods: Gut microbiome profiles of 40 NAFLD and 20 non-NAFLD controls were analyzed. Information about four blood lipids and 13 other clinical features was collected. Patients were divided into three groups by ultrasound and FibroScan, those with a normal liver, mild FL (FL1), and moderate-to-severe FL (FL2). FL1 and FL2 patients were divided into two groups, those with either hyperlipidemia or non-hyperlipidemia based on their blood lipids. Potential keystone species within the groups were identified using univariate analysis and a specificity-occupancy plot. Significant difference in biochemical parameters ion NAFLD patients and healthy individuals were identified by detrended correspondence analysis and canonical correspondence analysis. Results: Decreased gut bacterial diversity was found in patients with NAFLD. Firmicutes/Bacteroidetes decreased as NAFLD progressed. Faecalibacterium and Ruminococcus 2 were the most representative fatty-related bacteria. Glutamate pyruvic transaminase, aspartate aminotransferase, and white blood cell count were selected as the most significant biochemical indexes. Calculation of areas under the curve identified two microbiomes combined with the three biochemical indexes that identified normal liver and FL2 very well but performed poorly in diagnosing FL1. Conclusions: Faecalibacterium and Ruminococcus 2, combined with glutamate pyruvic transaminase, aspartate aminotransferase, and white blood cell count distinguished NAFLD. We speculate that regulating the health of gut microbiota may release NAFLD, in addition to providing new targets for clinicians to treat NAFLD.

Assessment of Oxygen Saturation in Breast Lesions Using Photoacoustic Imaging: Correlation With Benign and Malignant Disease.

BACKGROUND: Hypoxia is a hallmark of breast cancer (BC). Photoacoustic (PA) imaging, based on the use of laser-generated ultrasound (US), can detect oxygen saturation (So2) in the tissues of breast lesion patients. PURPOSE: To measure the oxygenation status of tissue in and on both sides of the lesion in breast lesion participants using a multimodal Photoacoustic/ultrasound (PA/US) imaging system and to determine the correlation between So2 measured by PA imaging and benign or malignant disease. MATERIALS AND METHODS: Multimodal PA/US imaging and gray-scale US (GSUS) of breast lesion was performed in consecutive breast lesion participants imaged in the US Outpatient Clinic between 2022 and 2023. Dual-wavelength PA imaging was used to measure the So2 value inside the lesion and on both sides of the tissue, and to distinguish benign from malignant lesions based on the So2 value. The ability of So2 to distinguish benign from malignant breast lesions was evaluated by the receiver operating characteristic curve (ROC) and the De-Long test. RESULTS: A total of 120 breast lesion participants (median age, 42.5 years) were included in the study. The malignant lesions exhibited lower So2 levels compared to benign lesions (malignant: 71.30%; benign: 83.81%; P < .01). Moreover, PA/US imaging demonstrates superior diagnostic results compared to GSUS, with an area under the curve (AUC) of 0.89 versus 0.70, sensitivity of 89.58% versus 85.42%, and specificity of 86.11% versus 55.56% at the So2 cut-off value of 78.85 (P < .001). The false positive rate in GSUS reduced by 30.75%, and the false negative rate diminished by 4.16% with PA /US diagnosis. Finally, the So2 on both sides tissues of malignant lesions are lower than that of benign lesions (P < .01). CONCLUSION: PA imaging allows for the assessment of So2 within the lesions of breast lesion patients, thereby facilitating a superior distinction between benign and malignant lesions.

Development and validation of a nomogram for discriminating between benign and malignant breast masses by conventional ultrasound and dual-mode elastography: a multicenter study.

Background: This study developed and validated an ultrasound nomogram based on conventional ultrasound and dual-mode elastography to differentiate breast masses. Methods: The data of 234 patients were collected before they underwent breast mass puncture or surgery at 4 different centers between 2016 and 2021. Patients were divided into 5 datasets: internal validation and development sets from the same hospital, and external validation sets from the 3 other hospitals. In the development cohort, age and 294 different ultrasound and elastography features were obtained from ultrasound images. Univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression were used for data reduction and visualization. Multivariable logistic regression analysis was used to develop the prediction model and ultrasound nomogram. Receiver operating characteristic (ROC) curve analysis, calibration curves, integrated discrimination improvement, and the net reclassification index were used to evaluate nomogram performance; decision curve analysis (DCA) and clinical impact curves were used to estimate clinical usefulness. Results: In the development cohort, margin, posterior features, shape, vascularity, (the mean shear wave elastography value of 1.5 mm surrounding tissues in a breast mass) divided by (the mean shear wave elastography value of the breast mass)-shell mean/A mean1.5(E), (the ratio of strain elastography of adipose tissue near a breast mass) divided by [the ratio of strain elastography of (the breast mass adds the 1.5 mm surrounding tissues in the breast mass)]-B/A'1.5 were selected as predictors in multivariable logistic regression analysis, comprising Model 1. Among the 5 cohorts, Model 1 performed best, with areas under the curve (AUC) of 0.92, 0.84, 0.87, 0.93, and 0.89, respectively. The AUCs were 0.90, 0.82, 0.83, 0.91, and 0.85, respectively, in Model 2 (margin + posterior features + shape + vascularity) and 0.80, 0.76, 0.77, 0.87, and 0.80, respectively, in Model 3 [shell mean/A mean1.5(E) + B/A'1.5]. Conclusions: Our ultrasound nomograms facilitate exposure to the features and visualization of breast cancer. Shell mean/A mean1.5(E), B/A'1.5 integrated with margin, posterior features, shape, and vascularity are superior at identifying breast cancer, and are worthy of further clinical investigation.

Feasibility of using AI to auto-catch responsible frames in ultrasound screening for breast cancer diagnosis.

The research of AI-assisted breast diagnosis has primarily been based on static images. It is unclear whether it represents the best diagnosis image.To explore the method of capturing complementary responsible frames from breast ultrasound screening by using artificial intelligence. We used feature entropy breast network (FEBrNet) to select responsible frames from breast ultrasound screenings and compared the diagnostic performance of AI models based on FEBrNet-recommended frames, physician-selected frames, 5-frame interval-selected frames, all frames of video, as well as that of ultrasound and mammography specialists. The AUROC of AI model based on FEBrNet-recommended frames outperformed other frame set based AI models, as well as ultrasound and mammography physicians, indicating that FEBrNet can reach level of medical specialists in frame selection.FEBrNet model can extract video responsible frames for breast nodule diagnosis, whose performance is equivalent to the doctors selected responsible frames.

The effect of image resolution on convolutional neural networks in breast ultrasound.

Purpose: The objective of this research was to investigate the efficacy of various parameter combinations of Convolutional Neural Networks (CNNs) models, namely MobileNet and DenseNet121, and different input image resolutions (REZs) ranging from 64×64 to 512×512 pixels, for diagnosing breast cancer. Materials and methods: During the period of June 2015 to November 2020, two hospitals were involved in the collection of two-dimensional ultrasound breast images for this retrospective multicenter study. The diagnostic performance of the computer models MobileNet and DenseNet 121 was compared at different resolutions. Results: The results showed that MobileNet had the best breast cancer diagnosis performance at 320×320pixel REZ and DenseNet121 had the best breast cancer diagnosis performance at 448×448pixel REZ. Conclusion: Our study reveals a significant correlation between image resolution and breast cancer diagnosis accuracy. Through the comparison of MobileNet and DenseNet121, it is highlighted that lightweight neural networks (LW-CNNs) can achieve model performance similar to or even slightly better than large neural networks models (HW-CNNs) in ultrasound images, and LW-CNNs' prediction time per image is lower.

S-Thyroid Computer-Aided Diagnosis Ultrasound System of Thyroid Nodules: Correlation Between Transverse and Longitudinal Planes.

Introduction: We compare the differences in the diagnostic results of S-thyroid, a computer-aided diagnosis (CAD) software, based on two mutually perpendicular planes. Methods: Initially, 149 thyroid nodules confirmed by surgical pathology were enrolled in our study. CAD in our study was based on the ACR TI-RADS lexicon. t test, rank-sum test, and Chi-square test were used. The interclass correlation coefficient and Cohen's kappa were used to explore the correlation between CAD features. Receiver operating characteristic was plotted for different combinations of CAD features. Results: The patient's age, transverse diameter, longitudinal diameter, shape, margin, echogenicity, echogenic foci, composition, TI-RADS classification, and risk probability of nodules in the transverse and longitudinal planes were related to thyroid cancer (p < 0.05). The AUC (95%CI) of TI-RADS classification in the transverse plane of CAD is better than that of the longitudinal plane [0.90 (0.84-0.95) vs. 0.83 (0.77-0.90), p = 0.04]. The AUC (95%CI) of risk probability of nodules in the transverse planes shows no difference from that in the longitudinal plane statistically [0.90 (0.85-0.95) vs. 0.88 (0.82-0.94), p = 0.52]. The AUC (95% CI), specificity, sensitivity, and accuracy [TI-RADS classification (transverse plane) + TI-RADS classification (longitudinal plane) + risk (transverse plane) + risk (longitudinal plane)] are 0.93 (0.89-0.97), 86.15%, 90.48%, and 88.59%, respectively. Conclusion: The diagnosis of thyroid cancer in the CAD transverse plane was superior to that in the CAD longitudinal plane when using the TI-RADS classification, but there was no difference in the diagnosis between the two planes when using risk. However, the combination of CAD transverse and longitudinal planes had the best diagnostic ability.

Assessment of elastographic Q-analysis score combined with Prostate Imaging-Reporting and Data System (PI-RADS) based on transrectal ultrasound (TRUS)/multi-parameter magnetic resonance imaging (MP-MRI) fusion-guided biopsy in differentiating benign and malignant prostate.

Background: Magnetic resonance imaging (MRI) has advantages in the diagnosis of prostate diseases, but there is also overdiagnosis. We compensate for this with fusion imaging and elastography. In this study, we want to evaluate Elastographic Q-analysis score (EQS) combined with Prostate Imaging Reporting and Data System (PI-RADS), based on transrectal ultrasound (TRUS)/multi-parameter magnetic resonance imaging (MP-MRI) fusion biopsy in differentiating benign and malignant prostate lesions. Methods: A total of 296 patients with 318 prostate lesions who underwent TRUS/MP-MRI fusion biopsy between October 2017 and October 2019 were retrospectively analysed. The performance of the EQS was evaluated on the sites of the suspicious areas of MP-MRI. The cut-off value of EQS was obtained according to receiver operating characteristic (ROC) curve, which was used to upgrade and downgrade the PI-RADS scores. The area under the curve (AUC), integrated discrimination improvement, and decision curve analysis were used to assess the new PI-RADS performance. Results: In total, 318 MP-MRI suspicious prostate lesions (94 malignant vs. 224 benign lesions). The EQS optimal threshold was 1.85, and the AUC was 0.816. All cases were constructed three models by using 1.85 as the cut-off value: upgrade-PI-RADS, downgrade-PI-RADS and complex-PI-RADS. The AUC of PI-RADS, upgrade-PI-RADS, downgrade-PI-RADS and complex-PI-RADS were 0.869, 0.867, 0.872 and 0.873 respectively. The diagnostic coincidence rate of PI-RADS was increased from 0.667 to 0.874 by using strain elastography, among which the diagnostic rate of prostate cancer was increased from 0.557 to 0.806, and the diagnostic rate of non-prostate cancer was increased from 0.775 to 0.967. The integrated discrimination improvement indicated that downgrade-PI-RADS had a better diagnostic capability (P<0.05). The net benefit of all models, which downgrade-PI-RADS can maximize the net benefit value of patients by decision curve analysis. Conclusions: The combination of PI-RADS and EQS with TRUS/MP-MRI fusion, particularly downgrade-PI-RADS, can reduce unnecessary biopsy procedures and prevent overdiagnosis.

Evaluation of the Transverse Carpal Ligament in Carpal Tunnel Syndrome by Shear Wave Elastography: A Non-Invasive Approach of Diagnosis and Management.

Objectives: The goal of this work is to determine the clinical value of the transverse carpal ligament (TCL) in carpal tunnel syndrome (CTS) for guiding subsequent treatment. Methods: This study analyzed patients who underwent median nerve (MN) ultrasound (US) examination of the wrist from April 2020 to April 2021. The cross-sectional area and anteroposterior diameter of the MN, as well as the TCL thickness and stiffness, were measured from images. The intra-group and intra-patient subgroup differences were compared using a t-test and a rank test. We also utilized receiver operating characteristic (ROC) curves to diagnose CTS and evaluate the severity. Results: The final cohort consisted of 120 wrists (bilateral) from 60 samples, evenly balanced across the patient and control groups according to their CTS diagnosis. In the unilateral positive patient subgroup, the MN and TCL of the positive hand were significantly thicker and stiffer than the negative counterparts (both, p < 0.05). The values from the right were also thicker and stiffer than the left (both, p < 0.05) in patients with bilateral CTS. The MN and TCL of the patient group were also significantly thicker and stiffer than those of the control group (both, p < 0.001). For diagnosing CTS, the area under the curve (AUC) of TCL thickness and stiffness at the distal carpal tunnel (DCT) ranged between 0.925 and 0.967. For evaluating CTS severity, we found that the optimal TCL stiffness is sufficient for diagnosing mild and non-mild patient cases (AUC: Emean = 0.757, Emax = 0.779). Conclusions: Shear wave elastography is therefore an effective method for CTS diagnosis and management.

The significance of dual-mode elastography in the diagnosis of breast lesions by physicians with different levels of experience.

BACKGROUND: This study aimed to assess the diagnostic value of dual-mode elastography for benign and malignant breast lesions and determine whether this technique can improve the diagnostic ability of physicians with different levels of experience. METHODS: One hundred and eighty-three breast lesions were analyzed retrospectively, and the following values were calculated for the lesions with various shells: shear modulus (G), Young's modulus (E), shear wave velocity (Cs), and strain ratio (SR). A random forest algorithm was used to select the optimal modes for elastography. A receiver operating characteristic curve was used to assess the diagnostic efficacy for benign and malignant breast lesions. Sensitivity and specificity values were calculated to evaluate any improvements in the diagnostic efficacy of physicians with different levels of experience (junior, intermediate-level, and senior) in the evaluation of malignant breast lesions using dual-mode elastography. RESULTS: The best-performing mode of shear wave elastography (SWE) in the diagnosis of breast lesions was the A'min 1.0 (Cs) mode (minimum shear wave velocity of the area of interest and 1.0 mm around the area of interest), and the best-performing mode of strain elastography (SE) was the B/A' 0.5 (ratio of fat to the elasticity of the area of interest and 0.5 mm around the area of interest). When the two methods were used in series, results showed high specificity (98%), positive likelihood ratio (PLR) (21.2), and positive predictive value (PPV) (95%). Series means that if SE and SWE were malignant, the result in series was malignant, and that if either SE or SWE was benign, the result in series was benign. When the methods were used in parallel, the results showed high sensitivity (91%), negative likelihood ratio (NLR) (0.15), and negative predictive value (NPV) (89%). Parallel means that if SE and SWE were benign, the result in parallel was benign, and that if either SE or SWE was malignant, the result in parallel was malignant. When conventional ultrasound was combined with dual-mode elastography, the intermediate-level and junior physicians' diagnoses of breast lesions showed a higher sensitivity, specificity, and area under the curve than conventional ultrasound diagnosis alone. CONCLUSIONS: Dual-mode elastography is effective in the diagnosis of breast lesions. The sensitivity and specificity values in this study show that diagnoses made by junior and intermediate-level physicians improve when dual-mode elastography is used, although diagnoses made by senior physicians do not improve significantly.

Knockdown of ARL5B Induces Mitochondrial-mediated Apoptosis and Inhibits Glycolysis in Breast Cancer Cells by Activating MDA5 Signaling.

AIM: Mitochondria are essential for energy metabolism in the tumor microenvironment and the survival of cancer cells. BACKGROUND: ADP-ribosylation factor-like GTPase 5b (ARL5B) has been found to be associated with mitochondrial dysfunction and breast cancer (BC) progression, but the underlying mechanism needs to be further understood. OBJECTIVE: We investigated the effects of ARL5B on the apoptosis and glycolysis of breast cancer cells and its underlying mechanisms. METHODS: Quantitative reverse transcription-PCR (qRT-PCR) and western blot assays were used to detect the expression of ARL5B in breast cancer tissues and cells. An ARL5B loss-of-function assay was performed to verify its role in BC development. RESULTS: ARL5B was upregulated in breast cancer tissues and cell lines. ARL5B knockdown induced apoptosis and activated the mitochondrial pathway in breast cancer cells. Interestingly, the inhibition of ARL5B repressed the aerobic glycolysis of breast cancer cells. The role of ARL5B in breast cancer cells was exerted by mediating the activation of viral RNA sensor MDA5-evoked signaling. Silencing ARL5B triggered MDA5 signaling by upregulating the key proteins involved in the MDA5 pathway. Importantly, MDA5 silencing reversed the effects of ARL5B knockdown on mitochondrial-mediated apoptosis and glycolysis, whereas poly (I:C), as a ligand for MDA5, further enhanced ARL5B knockdown- facilitated mitochondrial apoptosis and the inhibition of glycolysis. CONCLUSION: The knockdown of ARL5B activated MDA5 signaling and thus led to the enhanced mitochondrial- mediated apoptosis and glycolysis inhibition in breast cancer cells. Our study suggested that ARL5B might be a potential therapy target for BC.

A Comparative Study of Multiple Deep Learning Models Based on Multi-Input Resolution for Breast Ultrasound Images.

Purpose: The purpose of this study was to explore the performance of different parameter combinations of deep learning (DL) models (Xception, DenseNet121, MobileNet, ResNet50 and EfficientNetB0) and input image resolutions (REZs) (224 × 224, 320 × 320 and 488 × 488 pixels) for breast cancer diagnosis. Methods: This multicenter study retrospectively studied gray-scale ultrasound breast images enrolled from two Chinese hospitals. The data are divided into training, validation, internal testing and external testing set. Three-hundreds images were randomly selected for the physician-AI comparison. The Wilcoxon test was used to compare the diagnose error of physicians and models under P=0.05 and 0.10 significance level. The specificity, sensitivity, accuracy, area under the curve (AUC) were used as primary evaluation metrics. Results: A total of 13,684 images of 3447 female patients are finally included. In external test the 224 and 320 REZ achieve the best performance in MobileNet and EfficientNetB0 respectively (AUC: 0.893 and 0.907). Meanwhile, 448 REZ achieve the best performance in Xception, DenseNet121 and ResNet50 (AUC: 0.900, 0.883 and 0.871 respectively). In physician-AI test set, the 320 REZ for EfficientNetB0 (AUC: 0.896, P < 0.1) is better than senior physicians. Besides, the 224 REZ for MobileNet (AUC: 0.878, P < 0.1), 448 REZ for Xception (AUC: 0.895, P < 0.1) are better than junior physicians. While the 448 REZ for DenseNet121 (AUC: 0.880, P < 0.05) and ResNet50 (AUC: 0.838, P < 0.05) are only better than entry physicians. Conclusion: Based on the gray-scale ultrasound breast images, we obtained the best DL combination which was better than the physicians.

Metanephric adenoma: association between the imaging features of contrast-enhanced ultrasound and clinicopathological characteristics.

BACKGROUND: This study aimed to improve the understanding of metanephric adenoma (MA) by retrospective analysis of contrast-enhanced ultrasound (CEUS) findings and clinicopathological characteristics of MAs. METHODS: Gray-scale ultrasound (US) and CEUS findings of 7 adult MA patients, confirmed by postoperative pathology, were summarized via collection of clinicopathological and ultrasonographic imaging data, including tumor location, size, echo intensity, color flow, presence or absence of calcification, and liquefactive necrosis, contrast-enhanced pattern, enhancement characteristics, and contrast wash-out compared with adjacent parenchyma, and the presence or absence of a pseudocapsule. Histopathological analyses, including hematoxylin and eosin (HE) and immunohistochemical (IHC) staining, were conducted with the EnVision method. RESULTS: All 7 participants were female, aged 29-73 years (mean age, 54 years), with flank pain (3/7). All tumors were solid (7/7) with sizes of 2.0-5.0 cm (mean diameter, 3.07 cm), including 4 in the left kidney, 3 in the right kidney, 2 in the renal pelvis, and 5 in the renal parenchyma. On the gray-scale US, MA was shown as hypoechoic (4/7), slightly hyperechoic (2/7), isoechoic (1/7), and with a defined border. The morphology was regular and rounded (7/7), internal echogenicity was homogeneous (5/7), and no calcification was seen (7/7). The CEUS showed clear boundaries (7/7), homogeneous isodensity (5/7), with calcification (0/7), necrosis (2/11), heterogeneous hyperattenuation (2/7), pseudocapsule (2/7), and medullary phase fast wash-out (7/7). The surgical methods were radical nephrectomy (4/7) and partial nephrectomy (3/7). The duration of follow-up period for all participants was 3-74 months, and no local or distant recurrences were found. The IHC staining showed that most tumor cells were positive for WT1, cytokeratins AE1/AE3, vimentin, and CD57, and exhibited focal positivity for CK7, while negative for CD10, AMACR, and CK720. The proliferative index (Ki-67) was 2-3%. CONCLUSIONS: On gray-scale US, MA appears as a solid nodule with a well-defined boundary, regular morphology, and homogeneous echogenicity; CEUS shows slow progression and slightly lower homogeneous enhancement and fast wash-out in the medullary phase. These findings may provide insight into the progression of MA and aid in the development of diagnostic and therapeutic strategies.