Sonography-based multimodal information platform for identifying the surgical pathology of ductal carcinoma in situ.

BACKGROUND: The risk of ductal carcinoma in situ (DCIS) identified by biopsy often increases during surgery. Therefore, confirming the DCIS grade preoperatively is necessary for clinical decision-making. PURPOSE: To train a three-classification deep learning (DL) model based on ultrasound (US), combining clinical data, mammography (MG), US, and core needle biopsy (CNB) pathology to predict low-grade DCIS, intermediate-to-high-grade DCIS, and upstaged DCIS. MATERIALS AND METHODS: Data of 733 patients with 754 DCIS cases confirmed by biopsy were retrospectively collected from May 2013 to June 2022 (N1), and other data (N2) were confirmed by biopsy as low-grade DCIS. The lesions were randomly divided into training (n=471), validation (n=142), and test (n = 141) sets to establish the DCIS-Net. Information on the DCIS-Net, clinical (age and sign), US (size, calcifications, type, breast imaging reporting and data system [BI-RADS]), MG (microcalcifications, BI-RADS), and CNB pathology (nuclear grade, architectural features, and immunohistochemistry) were collected. Logistic regression and random forest analyses were conducted to develop Multimodal DCIS-Net to calculate the specificity, sensitivity, accuracy, receiver operating characteristic curve, and area under the curve (AUC). RESULTS: In the test set of N1, the accuracy and AUC of the multimodal DCIS-Net were 0.752-0.766 and 0.859-0.907 in the three-classification task, respectively. The accuracy and AUC for discriminating DCIS from upstaged DCIS were 0.751-0.780 and 0.829-0.861, respectively. In the test set of N2, the accuracy and AUC of discriminating low-grade DCIS from upstaged low-grade DCIS were 0.769-0.987 and 0.818-0.939, respectively. DL was ranked from one to five in the importance of features in the multimodal-DCIS-Net. CONCLUSION: By developing the DCIS-Net and integrating it with multimodal information, diagnosing low-grade DCIS, intermediate-to high-grade DCIS, and upstaged DCIS is possible. It can also be used to distinguish DCIS from upstaged DCIS and low-grade DCIS from upstaged low-grade DCIS, which could pave the way for the DCIS clinical workflow.

An artificial intelligence model based on transrectal ultrasound images of biopsy needle tract tissues to differentiate prostate cancer.

PURPOSE: We aimed to develop an artificial intelligence (AI) model based on transrectal ultrasonography (TRUS) images of biopsy needle tract (BNT) tissues for predicting prostate cancer (PCa) and to compare the PCa diagnostic performance of the radiologist model and clinical model. METHODS: A total of 1696 2D prostate TRUS images were involved from 142 patients between July 2021 and May 2022. The ResNet50 network model was utilized to train classification models with different input methods: original image (Whole model), BNT (Needle model), and combined image [Feature Pyramid Networks (FPN) model]. The training set, validation set, and test set were randomly assigned, then randomized 5-fold cross-validation between the training set and validation set was performed. The diagnostic effectiveness of AI models and image combination was accessed by an independent testing set. Then, the optimal AI model and image combination were selected to compare the diagnostic efficacy with that of senior radiologists and the clinical model. RESULTS: In the test set, the area under the curve, specificity, and sensitivity of the FPN model were 0.934, 0.966, and 0.829, respectively; the diagnostic efficacy was improved compared with the Whole and Needle models, with statistically significant differences (P < 0.05), and was better than that of senior radiologists (area under the curve: 0.667). The FPN model detected more PCa compared with senior physicians (82.9% vs. 55.8%), with a 61.3% decrease in the false-positive rate and a 23.2% increase in overall accuracy (0.887 vs. 0.655). CONCLUSION: The proposed FPN model can offer a new method for prostate tissue classification, improve the diagnostic performance, and may be a helpful tool to guide prostate biopsy.

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.

Head-to-head comparison of perfluorobutane contrast-enhanced US and multiparametric MRI for breast cancer: a prospective, multicenter study.

BACKGROUND: Multiparametric magnetic resonance imaging (MP-MRI) has high sensitivity for diagnosing breast cancers but cannot always be used as a routine diagnostic tool. The present study aimed to evaluate whether the diagnostic performance of perfluorobutane (PFB) contrast-enhanced ultrasound (CEUS) is similar to that of MP-MRI in breast cancer and whether combining the two methods would enhance diagnostic efficiency. PATIENTS AND METHODS: This was a head-to-head, prospective, multicenter study. Patients with breast lesions diagnosed by US as Breast Imaging Reporting and Data System (BI-RADS) categories 3, 4, and 5 underwent both PFB-CEUS and MP-MRI scans. On-site operators and three reviewers categorized the BI-RADS of all lesions on two images. Logistic-bootstrap 1000-sample analysis and cross-validation were used to construct PFB-CEUS, MP-MRI, and hybrid (PFB-CEUS + MP-MRI) models to distinguish breast lesions. RESULTS: In total, 179 women with 186 breast lesions were evaluated from 17 centers in China. The area under the receiver operating characteristic curve (AUC) for the PFB-CEUS model to diagnose breast cancer (0.89; 95% confidence interval [CI] 0.74, 0.97) was similar to that of the MP-MRI model (0.89; 95% CI 0.73, 0.97) (P = 0.85). The AUC of the hybrid model (0.92, 95% CI 0.77, 0.98) did not show a statistical advantage over the PFB-CEUS and MP-MRI models (P = 0.29 and 0.40, respectively). However, 90.3% false-positive and 66.7% false-negative results of PFB-CEUS radiologists and 90.5% false-positive and 42.8% false-negative results of MP-MRI radiologists could be corrected by the hybrid model. Three dynamic nomograms of PFB-CEUS, MP-MRI and hybrid models to diagnose breast cancer are freely available online. CONCLUSIONS: PFB-CEUS can be used in the differential diagnosis of breast cancer with comparable performance to MP-MRI and with less time consumption. Using PFB-CEUS and MP-MRI as joint diagnostics could further strengthen the diagnostic ability. Trial registration Clinicaltrials.gov; NCT04657328. Registered 26 September 2020. IRB number 2020-300 was approved in Chinese PLA General Hospital. Every patient signed a written informed consent form in each center.

A new xAI framework with feature explainability for tumors decision-making in Ultrasound data: comparing with Grad-CAM.

BACKGROUND AND OBJECTIVE: The value of implementing artificial intelligence (AI) on ultrasound screening for thyroid cancer has been acknowledged, with numerous early studies confirming AI might help physicians acquire more accurate diagnoses. However, the black box nature of AI's decision-making process makes it difficult for users to grasp the foundation of AI's predictions. Furthermore, explainability is not only related to AI performance, but also responsibility and risk in medical diagnosis. In this paper, we offer Explainer, an intrinsically explainable framework that can categorize images and create heatmaps highlighting the regions on which its prediction is based. METHODS: A dataset of 19341 thyroid ultrasound images with pathological results and physician-annotated TI-RADS features is used to train and test the robustness of the proposed framework. Then we conducted a benign-malignant classification study to determine whether physicians perform better with the assistance of an explainer than they do alone or with Gradient-weighted Class Activation Mapping (Grad-CAM). RESULTS: Reader studies show that the Explainer can achieve a more accurate diagnosis while explaining heatmaps, and that physicians' performances are improved when assisted by the Explainer. Case study results confirm that the Explainer is capable of locating more reasonable and feature-related regions than the Grad-CAM. CONCLUSIONS: The Explainer offers physicians a tool to understand the basis of AI predictions and evaluate their reliability, which has the potential to unbox the "black box" of medical imaging AI.

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.

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.

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.

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.

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.

Development and validation of a nomogram based on multiparametric magnetic resonance imaging and elastography-derived data for the stratification of patients with prostate cancer.

BACKGROUND: This study sought to develop and validate a nomogram combining the elastographic Q-analysis score (EQS), the Prostate Imaging Reporting and Data System (PI-RADS) score, and clinical parameters for the stratification of patients with prostate cancer (PCa). METHODS: A retrospective study was conducted of 375 patients with 375 lesions who underwent volume-navigation transrectal ultrasound (TRUS) and multiparametric magnetic resonance imaging (MP-MRI)-fusion targeted biopsies between April 2017 and January 2020. Based on a multivariate logistic regression model, a nomogram was created to assess any PCa and high-risk PCa [Gleason score (GS) ≥4+3] using data from patients diagnosed between April 2017 and June 2019 (n=271), and was validated in patients diagnosed after July 2019 (n=104). The nomogram's performance was evaluated based on its discrimination, calibration, and clinical usefulness. RESULTS: The areas under the curve (AUCs) of the nomogram for predicting any PCa and high-risk PCa were 0.949 [95% confidence interval (CI), 0.921 to 0.978] and 0.936 (95% CI, 0.906 to 0.965), respectively, in the training cohort, and 0.946 (95% CI, 0.894 to 0.997) and 0.971 (95% CI, 0.9331 to 1), respectively, in the validation cohort. The nomogram was well calibrated, and no significant difference was found between the predicted and observed probabilities. A decision curve analysis (DCA) for the nomogram with and without the EQS showed that the threshold probability of for any PCa was <67%. CONCLUSIONS: The nomogram that combined elastography-derived and MP-MRI data was more clinically useful than the model based on PI-RADS and clinical parameters alone. Our nomogram could aid urologists to make decisions and avoid unnecessary biopsies.

Prediction of postpartum pelvic floor dysfunction with a nomogram model based on big data collected during pregnancy.

BACKGROUND: Pregnancy and childbirth are the main causes of pelvic floor dysfunction (PFD). Although pelvic floor muscle tension is typically measured at 42 days postpartum to assess the severity of PFD and provide timely rehabilitation, it is still impossible to predict PFD and take targeted preventive measures in clinical practice. A PFD prediction model based on big data obtained in prenatal check-ups was established in this study to allow the formulation of personalized preventive strategies to reduce the incidence of PFD. METHODS: A total of 1,500 women who underwent regular prenatal checkups and examinations for PFD at 42 days postpartum at the Zhuji Maternal and Child Health Hospital between May 2015 and May 2020 were selected. The data from 1,000 of them were selected as the training cohort, and the data from 500 of them were used as the validation cohort. The women were divided into a PFD group and a non-PFD group according to whether PFD was diagnosed at 42 days postpartum. A nomogram prediction model was created using the influencing factors that lead to PFD, and the discrimination and calibration of the nomogram were evaluated through internal and external validation. RESULTS: A total of 389 cases (38.9%) of PFD were included in the training cohort. Multivariate analysis showed that age (odds ratio (OR) =1.896, P<0.001), history of childbirth (OR =4.531, P<0.001), history of constipation (OR =2.475, P<0.001), urinary incontinence during pregnancy (OR =4.416, P<0.001), and biparietal diameter at 32 weeks of gestation (OR =51.672, P=0.012) were independent influencing factors of PFD at 42 days postpartum. These factors were used to establish a nomogram prediction model. This prediction model maintained good discrimination between the training cohort and the external validation cohort (the area under the curve was 0.893 and 0.842 for the training and validation cohorts, respectively). CONCLUSIONS: The study validated that the nomogram prediction model based on the factors influencing PFD can be used to predict PFD at 32 weeks of gestation for timely intervention and prevention of PFD.

Development and validation of a nomogram for predicting prostate cancer in men with prostate-specific antigen grey zone based on retrospective analysis of clinical and multi-parameter magnetic resonance imaging/transrectal ultrasound fusion-derived data.

BACKGROUND: Urologists face a dilemma when deciding whether prostate biopsy is required for patients with prostate-specific antigen (PSA) levels in the grey zone (4 to 10 ng/mL). METHODS: We retrospectively analyzed data from consecutive patients with PSA levels in grey zone, who underwent targeted multiparametric magnetic resonance imaging (MP-MRI)/transrectal ultrasound (TRUS) fusion biopsy with elastography between November 2017 and December 2019 in our hospital. The patientse data including age, PSA, fPSA (free PSA), fPSA/PSA, PSA density (PSAD), prostate volume, elastography Q-analysis score (EQS), and prostate imaging-reporting and data system (PI-RADS) score were collected. The nomogram was built using logistic regression and the final cohort of patients was randomly divided into a training cohort (70%) and a validation cohort (30%) by R software. The models were evaluated by receiver operating characteristic curve (ROC) analysis and calibration curve analysis. The nomogram was constructed from the best model. RESULTS: The final study cohort consisted of 155 patients (training cohort, 109 patients; validation cohort, 46 patients) with PSA in the grey zone, of which 36 patients were pathologically diagnosed with PCa. The EQS model, -EQS model, +EQS model were built. The +EQS model that consisted of fPSA/PSA, EQS, and PI-RADS score had the best PCa diagnostic accuracy (development and validation, 0.783 and 0.781) and probability score (development and validation, 0.939 vs. 0.622). The new nomogram based on this model was constructed, in which fPSA/PSA ratio had the largest impact, followed by PI-RADS and EQS. CONCLUSIONS: Elastography and pre-biopsy MP-MRI has clinical significance for patients with PSA in the grey zone. The new nomogram, which is based on pre biopsy data including serological analysis, PI-RADS score, and EQS, can be helpful for clinical decision-making to avoid unnecessary biopsy.

Ultrasonic irradiation and SonoVue microbubbles-mediated RNA interference targeting PRR11 inhibits breast cancer cells proliferation and metastasis, but promotes apoptosis.

The present study compared the effects of ultrasonic irradiation and SonoVue microbubbles (US) or Lipofectamine 3000 on the transfection of small interfering RNA for PRR11 (siPRR11) and Proline-rich protein 11 (PRR11) overexpression plasmid into breast cancer cells. SiPRR11 and PRR11 overexpression plasmid were transfected into breast cancer MCF7 cells mediated by US and Lipofectamine 3000. PRR11 expressions in breast cancer and normal tissues were determined using Gene Expression Profiling Interactive Analysis (GEPIA). The viability, proliferation, migration, invasion and apoptosis of breast cancer cells were respectively measured by MTT assay, clone formation assay, scratch wound-healing assay, Transwell assay and flow cytometry. PRR11 and epithelial-to-mesenchymal transition (EMT)-related and apoptosis-related (B-cell lymphoma 2, Bcl-2; Bcl-2-associated protein X, Bax) proteins' expressions were detected by quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot as appropriate. As ultrasonic intensity increased, the viability of MCF7 cells was decreased. Results from GEPIA suggested that PRR11 was up-regulated in breast cancer. Silencing PRR11 mediated by US showed a higher efficiency than by Lipofectamine 3000. SiPRR11 transfected by Lipofectamine 3000 suppressed cells growth and metastasis, while promoted cell apoptosis. Moreover, E-cadherin (E-cad) and Bax expressions were high but N-cadherin (N-cad), Snail and Bcl-2 expressions were low. However, overexpressed PRR11 caused the opposite effects. More importantly, transfection of siPRR11 and PRR11 overexpression plasmid using US had a higher efficacy than using Lipofectamine 3000. US transfection of PRR11 siRNA showed better effects on inhibiting breast cancer progression. The current findings contribute to a novel treatment for breast cancer.

Clinical Value of the Elastographic Q-Analysis Score in Assisting Real-Time Elastography-Guided Prostate Biopsy: A Retrospective Study of 125 Patients.

OBJECTIVES: This study aimed to evaluate the clinical value of the elastographic Q-analysis score (EQS) in assisting real-time elastography- and transrectal US-guided prostate biopsy. METHODS: A total of 125 patients with 301 lesions were enrolled in this study; all were confirmed by pathologic results. The patients underwent transrectal US and elastographic examinations before biopsy. Elastographic Q-analysis score analysis software was used for measuring the mean EQS of the elastic images. First, the suspicious regions on elastography underwent biopsy. Then 12-core systematic prostate biopsy was performed. An EQS curve was used to calculate the mean EQS, and a receiver operating characteristic curve was drawn to find the cutoff point for the EQS to predict prostate cancer. RESULTS: Of the 301 lesions in this study, 125 were malignant, and 176 were benign. The mean EQS values of benign and malignant lesions ± SD were 1.47 ± 0.75 and 2.98 ± 1.06, respectively. The difference was statistically significant (P < .05). The area under the receiver operating characteristic curve was 0.87. When the cutoff point was 1.95 for diagnosing malignant and benign lesions, the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio were 83.5%, 84.4%, 76.8%, 89.2%, 5.35, and 0.20. CONCLUSIONS: The EQS could be used as a way to predict benign and malignant lesions and thus could serve as guidance for adding targeted biopsy.

Long Noncoding RNA COPDA1 Promotes Airway Smooth Muscle Cell Proliferation in Chronic Obstructive Pulmonary Disease.

Abnormal expression of long noncoding RNAs (lncRNAs) has been confirmed to be associated with many diseases, including chronic obstructive pulmonary disease (COPD). To gain better understanding of the mechanism of COPD, we investigated the lncRNA and mRNA profiles in the lung tissue of patients with COPD. According to the analysis, one of the significantly different lncRNAs, COPDA1, might participate in the occurrence and development of COPD. Lung tissues were collected from nonsmokers, smokers, or smokers with COPD for RNA sequencing. Bioinformatic analysis and cell experiments were used to define the function of COPDA1, and the effects of COPDA1 on intracellular Ca2+ concentration and cell proliferation were examined after knockdown or overexpression of COPDA1. A number of variations of lncRNAs were found in the comparison of nonsmokers, smokers, and smokers with COPD. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analyses indicated that smoking was involved in the activation of cytokines and the cell cycle, which is associated with COPD. According to the lncRNA-mRNA-coexpressing network and enrichment analysis, COPDAz1 and one of its target genes, MS4A1 (membrane-spanning 4-domains family, subfamily A) were investigated, and we discovered that the expression of MS4A1 was closely associated with lncRNA COPDA1 expression in human bronchial smooth muscle cells (HBSMCs). Further study showed that lncRNA COPDA1 upregulated the expression of MS4A1 to increase store-operated calcium entry in the HBSMCs, resulting in the promotion of the proliferation of smooth muscle cells as well as of airway remodeling. COPDA1 might be involved in the regulation of certain signaling pathways in COPD, might promote the proliferation of HBSMCs, and might also be involved in facilitating airway remodeling.

Evaluation of the Performance of the Ultrasound (US) Elastographic Q-Analysis Score Combined With the Prostate Imaging Reporting and Data System for Malignancy Risk Stratification in Prostate Nodules Based on Transrectal US-Magnetic Resonance Imaging Fusion Imaging.

OBJECTIVES: This study retrospectively evaluated the prognostic performance of the ultrasound elastographic Q-analysis score (EQS) combined with the Prostate Imaging Reporting and Data System (PI-RADS) for malignancy risk stratification in prostate nodules based on transrectal ultrasound-magnetic resonance imaging fusion imaging. METHODS: Sixty-two patients who were suspected to have PCa between October 2017 and May 2018 in our hospital were retrospectively evaluated. The performance of the EQS and PI-RADS was evaluated by patients' receiver operating characteristic curves in differentiating malignant and benign prostate nodules. The combination of the EQS and PI-RADS methods for prostate imaging was evaluated. RESULTS: Sixty-two prostate nodules in 62 patients were included. All of the patients underwent biopsy; 29 cases were prostate cancer, and the rest were benign prostate lesions. Both the EQS and PI-RADS were significantly higher in malignant nodules than in benign nodules. The sensitivity, specificity, area under the curve, positive likelihood ratio, negative likelihood ratio, positive predictive value, negative predictive value, and Youden index of an EQS cutoff of 2.05 were 86.2%, 81.8%, 85.9%, 4.73, 0.169, 80.6%, 87.1%, and 68%, respectively. The corresponding numbers for a PI-RADS cutoff of 4 were 82.7%, 69.7%, 84.2%, 2.72, 0.25, 70.6%, 82.1%, and 52.4%. The "tandem" method had a higher diagnostic specificity (87.9%), positive likelihood ratio (6.55), and positive predictive value (85.1%). The "parallel" method had a higher diagnostic sensitivity (96.5%), negative likelihood ratio (0.06), and negative predictive value (95.2%). CONCLUSIONS: both the EQS and PI-RADS had good diagnostic performance in differentiating between malignant and benign prostate lesions. The combination of the EQS and PI-RADS improved the diagnostic performance to a certain degree.

Screening Breast Lesions Using Shear Modulus and Its 1-mm Shell in Sound Touch Elastography.

The aim of the study described here was to screen breast lesions using either or both shear modulus (G) and its 1-mm shell (S) in sound touch elastography through a retrospective study of 209 consecutive women with breast lesions. The ability of G and S data to differentiate between malignant and benign lesions was evaluated using the receiver operating characteristic (ROC) curve. The optimal cutoff point, sensitivity, specificity, positive likelihood ratio (LR+) and negative likelihood ratio (LR-) were calculated. Then, the parameters were pooled to determine the area under the summary receiver operating curve (AUSROC). The pooled sensitivity (PSen), pooled specificity (PSpe), pooled LR+ (PLR+), pooled LR- (PLR-) and diagnostic score (DS) were calculated. Pathologic examination results were used as the reference. In total, 209 patients with 155 benign and 54 malignant lesions were enrolled. For Gmax, Gmean and Gsd, the cutoff values were 35.15 kPa (p = 0.0001), 10.18 kPa (p = 0.0001) and 5.18 kPa (p = 0.0001), respectively. For Smax, Smean and Ssd, the cutoff values were 40.94 kPa (p = 0.001), 13.12 kPa (p = 0.0001) and 7.97 kPa (p = 0.0001), respectively. There were no significant differences in Gmin and Smin between benign and malignant lesions. For the pooled six parameters, the PSen, PSpe, PLR+, PLR-, DS and AUSROC were 86% (95% confidence interval: 82%-89%), 82% (80%-85%), 4.90 (4.24-5.68), 0.17 (0.13-0.22), 3.36 (3.00-3.72) and 91% (88-93%), respectively. The G and S parameters of sound touch elastography could provide valuable data for the evaluation of breast lesions. Additionally, use of multiple parameters or combined use of the six parameters may be more effective in the evaluation of breast lesions.

Diagnostic Performance of Multiple Sound Touch Elastography for Differentiating Benign and Malignant Thyroid Nodules.

This study evaluated the ability of Sound Touch Elastography (STE) to distinguish malignant from benign thyroid nodules by quantifying tumor stiffness using the elastic ratio (EI) and shear modulus (G). Eighty-six patients with 86 nodules were enrolled in this study. There were 24/86 (27.90%) thyroid papillary carcinomas (TPC) and 62/86 (72.10%) benign nodules. The mean EI was significantly lower in TPCs than in benign nodules. The EI area under the receiver operating characteristic curve (ROC) was 80%. The EI cutoff value for TPCs was 0.215%. The sensitivity (Sen), specificity (Spe), positive likelihood ratio (LR+), and negative likelihood ratio (LR-) were 71%, 73%, 2.58, and 0.40, respectively. G max, G mean, and G sd were significantly higher in TPCs than in benign nodules. There were no significant differences in G min. Compared with other G parameters, G max with an optimal cutoff value of 15.82 kPa had the highest AUROC value (84%). The Sen, Spe, LR+, and LR- were 79.17%, 79.03%, 3.776, and 0.261, respectively. We pooled the EI, G max, G mean, and G sd and the pooled-Sen, Spe, LR+, LR-, diagnostic odds ratio and odds ratio, and area under the summary ROC were 79%, 71%, 2.73, 0.29, 2.23, 9.29, and 82%, respectively. STE could be a new ultrasound diagnostic method for evaluating benign and malignant thyroid nodules.