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Objective The objective is to evaluate the diagnostic effectiveness of contrast-enhanced spectral mammography (CESM) in the diagnosis of breast cancer.Design Data sources PubMed, Embase and Cochrane libraries up to 18 June 2022.Eligibility criteria for selecting studies We included trials studies, compared the results of different researchers on CESM in the diagnosis of breast cancer, and calculated the diagnostic value of CESM for breast cancer.Data extraction and synthesis Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) evaluated the methodological quality of all the included studies. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses specification. In addition to sensitivity and specificity, other important parameters were explored in an analysis of CESM accuracy for breast cancer diagnosis. For overall accuracy estimation, summary receiver operating characteristic curves were calculated. STATA V.14.0 was used for all analyses.Results This meta-analysis included a total of 12 studies. According to the summary estimates for CESM in the diagnosis of breast cancer, the pooled sensitivity and specificity were 0.97 (95% CI 0.92 to 0.98) and 0.76 (95% CI 0.64 to 0.85), respectively. Positive likelihood ratio was 4.03 (95% CI 2.65 to 6.11), negative likelihood ratio was 0.05 (95% CI 0.02 to 0.09) and the diagnostic odds ratio was 89.49 (95% CI 45.78 to 174.92). Moreover, there was a 0.95 area under the curve.Conclusions The CESM has high sensitivity and good specificity when it comes to evaluating breast cancer, particularly in women with dense breasts. Thus, provide more information for clinical diagnosis and treatment.

Abstract Background Marfan syndrome (MFS) is a hereditary connective tissue disorder involving multiple systems, including ophthalmologic abnormalities. Most cases are due to heterozygous mutations in the fibrillin‐1 gene (FBN1). Other associated genes include LTBP2, MYH11, MYLK, and SLC2A10. There is significant clinical overlap between MFS and other Marfan‐like disorders. Purpose To expand the mutation spectrum of FBN1 gene and validate the pathogenicity of Marfan‐related genes in patients with MFS and ocular manifestations. Methods We recruited 318 participants (195 cases, 123 controls), including 59 sporadic cases and 88 families. All patients had comprehensive ophthalmic examinations showing ocular features of MFS and met Ghent criteria. Additionally, 754 cases with other eye diseases were recruited. Panel‐based next‐generation sequencing (NGS) screened mutations in 792 genes related to inherited eye diseases. Results We detected 181 mutations with an 84.7% detection rate in sporadic cases and 87.5% in familial cases. The overall detection rate was 86.4%, with FBN1 accounting for 74.8%. In cases without FBN1 mutations, 23 mutations from seven Marfan‐related genes were identified, including four pathogenic or likely pathogenic mutations in LTBP2. The 181 mutations included 165 missenses, 10 splicings, three frameshifts, and three nonsenses. FBN1 accounted for 53.0% of mutations. The most prevalent pathogenic mutation was FBN1 c.4096G>A. Additionally, 94 novel mutations were detected, with 13 de novo mutations in 14 families. Conclusion We expanded the mutation spectrum of the FBN1 gene and provided evidence for the pathogenicity of other Marfan‐related genes. Variants in LTBP2 may contribute to the ocular manifestations in MFS, underscoring its role in phenotypic diversity.

Abstract Introduction Conventional magnetic resonance imaging (MRI) features have difficulty distinguishing glioma true tumor recurrence (TuR) from treatment-related effects (TrE). We aimed to develop a machine-learning model based on multimodality MRI radiomics to help improve the efficiency of identifying glioma TuR. Methods A total of 131 patients were enrolled and randomly divided into the training set (n = 91) and the test set (n = 40). Radiomic features were extracted from the postoperative enhancement (PoE) region and edema (ED) region from four routine MRI sequences. After analyses of Spearman's rank correlation coefficient, and least absolute shrinkage and selection operator, the key radiomic features were selected to construct support vector machine (SVM) and k-nearest neighbor (KNN) models. Decision curve analysis (DCA) and receiver operating characteristic (ROC) curves were used to analyze the performance. Results The PoE model had a significantly higher area under curve (AUC) than the ED model (p

Abstract Autonomous-rail rapid transit (ART) is a new medium-capacity rapid transportation system with punctuality, comfort and convenience, but low-cost construction. Combined velocity planning is a critical approach to meet the requirements of energy-saving and punctuality. An ART velocity pre-planning and re-planning strategy based on the combination of punctuality dynamic programming (PDP) and pseudospectral (PS) method is proposed in this paper. Firstly, the longitudinal dynamics model of ART is established by a multi-particle model. Secondly, the PDP algorithm with global optimal characteristics is adopted as the pre-planning strategy. A model for determining the number of collocation points of the real-time PS method is proposed to improve the energy-saving effect while ensuring computation efficiency. Then the enhanced PS method is utilized to design the velocity re-planning strategy. Finally, simulations are conducted in the typical scenario with sloping roads, traffic lights, and intrusion of the pedestrian. The simulation results indicate that the ART with the proposed velocity trajectory optimization strategy can meet the punctuality requirement, and obtain better economy efficiency compared with the punctuality green light optimal speed advisory (PGLOSA).

Abstract Background Uterine cervical cancer (UCC) was the fourth leading cause of cancer death among women worldwide. The conventional MRI hardly revealing the microstructure information. This study aimed to compare the value of amide proton transfer-weighted imaging (APTWI) and diffusion kurtosis imaging (DKI) in evaluating the histological grade of cervical squamous carcinoma (CSC) in addition to routine diffusion-weighted imaging (DWI). Methods Forty-six patients with CSC underwent pelvic DKI and APTWI. The magnetization transfer ratio asymmetry (MTRasym), apparent diffusion coefficient (ADC), mean diffusivity (MD) and mean kurtosis (MK) were calculated and compared based on the histological grade. Correlation coefficients between each parameter and histological grade were calculated. Results The MTRasym and MK values of grade 1 (G1) were significantly lower than those of grade 2 (G2), and those parameters of G2 were significantly lower than those of grade 3 (G3). The MD and ADC values of G1 were significantly higher than those of G2, and those of G2 were significantly higher than those of G3. MTRasym and MK were both positively correlated with histological grade (r = 0.789 and 0.743, P AUC (DKI + DWI) > AUC (APTWI+DKI) > AUC (APTWI+DWI) > AUC (MTRasym) > AUC (MK) > AUC (MD) > AUC (ADC), where the differences between AUC (APTWI+DKI + DWI), AUC (DKI + DWI) and AUC (ADC) were significant. For the diagnosis of G2 and G3 CSCs, AUC (APTWI+DKI + DWI) > AUC (APTWI+DWI) > AUC (APTWI+DKI) > AUC (DKI + DWI) > AUC (MTRasym) > AUC (MK) > AUC (MD > AUC (ADC), where the differences between AUC (APTWI+DKI + DWI), AUC (APTWI+DWI) and AUC (ADC) were significant. Conclusion Compared with DWI and DKI, APTWI is more effective in identifying the histological grades of CSC. APTWI is recommended as a supplementary scan to routine DWI in CSCs.

Existing studies on rehabilitation robots are generally devoted to robot-assisted active rehabilitation training, which is conducive to facilitating muscle and nerve regeneration. However, human–robot interaction (HRI) requires imposing a limit on the workspace within which the robot operates, so as to ensure patient safety. A safe admittance boundary algorithm for a rehabilitation robot is proposed based on the space classification model which works by constructing a virtual boundary for the HRI workspace in the control layer. First, point cloud isodensification is performed for the workspaces within which the human body and the robot operate. Next, the nearest neighbor density is determined for the point cloud, and the space classification model is built on this basis. Finally, the space classification model is integrated with admittance control to derive the safe admittance boundary algorithm, which can be used for safety control. This algorithm is then subjected to space verification experiments and out–of–bounds experiments using a dynamic arm simulator (DAS). As indicated by the experimental results, when the side length of the voxel grid for voxel center filtering is set to 0.06 m, the accuracy of space definition is 98.40%. The average maximum response time for out–of–bounds behaviors is 165.62 ms. The safe admittance boundary algorithm can impose reasonable virtual constraints on the robot workspace, thereby improving HRI safety.

BackgroundEndometrial cancer (EC) is one of the most common gynecologic malignancies in clinical practice. This study aimed to compare the value of diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and amide proton transfer-weighted imaging (APTWI) in the assessment of risk stratification factors for stage I EC including histological subtype, grade, stage, and lymphovascular space invasion (LVSI).MethodsA total of 72 patients with stage I EC underwent pelvic MRI. The apparent diffusion coefficient (ADC), mean diffusivity (MD), mean kurtosis (MK), and magnetization transfer ratio asymmetry (MTRasym at 3.5 ppm) were calculated and compared in risk groups with the Mann–Whitney U test or independent samples t-test. Spearman’s rank correlation was applied to depict the correlation of each parameter with risk stratification. The diagnostic efficacy was evaluated with receiver operating characteristic (ROC) curve analysis and compared using the DeLong test. A multivariate logistic regression was conducted to explore the optimal model for risk prediction.ResultsThere were significantly greater MTRasym (3.5 ppm) and MK and significantly lower ADC and MD in the non-adenocarcinoma, stage IB, LVSI-positive, high-grade, and non-low-risk groups (all p < 0.05). The MK and MTRasym (3.5 ppm) were moderately positively correlated with risk stratification as assessed by the European Society for Medical Oncology (EMSO) clinical practice guidelines (r = 0.640 and 0.502, respectively), while ADC and MD were mildly negatively correlated with risk stratification (r = −0.358 and −0.438, respectively). MTRasym (3.5 ppm), MD, and MK were identified as independent risk predictors in stage I EC, and optimal predictive performance was obtained with their combinations (AUC = 0.906, sensitivity = 70.97%, specificity = 92.68%). The results of the validation model were consistent with the above results, and the calibration curve showed good accuracy and consistency.ConclusionsAlthough similar performance was obtained with each individual parameter of APTWI, DWI, and DKI for the noninvasive assessment of aggressive behavior in stage I EC, the combination of MD, MK, and MTRasym (3.5 ppm) provided improved predictive power for non-low-risk stage I EC and may serve as a superior imaging marker.

Graph visualization plays an important role in several fields, such as social media networks, protein–protein interaction networks, and traffic networks. A number of visualization design tools and programming toolkits have been widely used in graph-related applications. However, a key challenge remains in the high-efficiency visualization of large-scale graph data. In this study, we present NetV.js, an open-source and WebGL-based JavaScript library that supports the fast visualization of large-scale graph data (up to 50 thousand nodes and 1 million edges) at an interactive frame rate with a commodity computer. Experimental results demonstrate that our library outperforms existing toolkits (Sigma.js, D3.js, Cytoscape.js, and Stardust.js) in terms of performance.

ObjectivesThis study aims to evaluate and compare the diagnostic value of DKI and APT in prostate cancer (PCa), and their correlation with Gleason Score (GS).Materials and MethodsDKI and APT imaging of 49 patients with PCa and 51 patients with benign prostatic hyperplasia (BPH) were collected and analyzed, respectively. According to the GS, the patients with PCa were divided into high-risk, intermediate-risk and low-risk groups. The mean kurtosis (MK), mean diffusion (MD) and magnetization transfer ratio asymmetry (MTRasym, 3.5 ppm) values among PCa, BPH, and different GS groups of PCa were compared and analyzed respectively. The diagnostic accuracy of each parameter was evaluated by using the receiver operating characteristic (ROC) curve. The correlation between each parameter and GS was analyzed by using Spearman’s rank correlation.ResultsThe MK and MTRasym (3.5 ppm) values were significantly higher in PCa group than in BPH group, while the MD value was significantly lower than in BPH group. The differences of MK/MD/MTRasym (3.5 ppm) between any two of the low-risk, intermediate-risk, and high-risk groups were all statistically significant (p

Abstract Purpose This study evaluated and compared the performances of diffusion kurtosis imaging (DKI) and diffusion-weighted imaging (DWI) for diagnosing and histologically grading endometrial cancer. Materials and methods In this retrospective study, DKI and DWI data for 61 patients with endometrial cancer and 30 patients with a normal endometrium were analyzed, and the mean kurtosis (MK), mean diffusion coefficient (MD) and apparent diffusion coefficient (ADC) values for the endometrial cancer tissue and normal endometrial tissue were acquired. The parameters for the normal endometrium group (G0) and the endometrial cancer groups (G1, G2 and G3) were compared and analyzed. The receiver operating characteristic (ROC) curve was used to evaluate each parameter’s diagnostic accuracy and threshold. Spearman correlation analysis was used to analyze the correlations between all parameters and histological grades. Results The MK values for the G0, G1, G2 and G3 groups increased gradually, while the MD and ADC values decreased gradually. Except for the differences in the ADC values between G0 and G1, the differences among the groups were statistically significant (P MD > ADC; r = − 0.85, + 0.82, + 0.76, P

Background: Preoperative grading of hepatocellular carcinoma (HCC) is an important factor associated with prognosis after liver resection. The promising prediction of the differentiation of HCC remains a challenge. The purpose of our study was to investigate the value of amide proton transfer (APT) imaging in predicting the histological grade of HCC, compared with the intravoxel incoherent motion (IVIM) imaging.Methods: From September 2018 to February 2020, 88 patients with HCC were enrolled and divided into four groups (G1, G2, G3, and G4) based on the histologic grades. Preoperative APT signal intensity (SI), apparent diffusion coefficient (ADC), true molecular diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f ) of HCC were independently measured by two radiologists. The averaged values of those parameters were compared using an analysis of variance. The Spearman rank analysis was used to compare the correlation between those imaging parameters and the histological grades. Receiver operating characteristic (ROC) curve analysis was used to explore the predictive performance.Results: There were significant differences in APT SI, ADC, D, and f among the four grades of HCC (all P < 0.001). A moderate to good relationship was found between APT SI and the histologic grade of HCC (r = 0.679, P < 0.001). APT SI had an area under the ROC curve (AUC) of 0.890 (95% CI: 0.805–0.947) for differentiating low- from high-grade HCC, and the corresponding sensitivity and specificity were 85.71% and 82.05%, respectively. Comparison of ROC curves demonstrated that the AUC of APT SI was significantly higher than those of IVIM-derived parameter (Z = 2.603, P = 0.0092; Z = 2.099, P = 0.0358; Z = 4.023, P = 0.0001; Z = 2.435, P = 0.0149, compared with ADC, D, D*, and f , respectively). Moreover, the combination of both techniques further improved the diagnostic performance, with an AUC of 0.929 (95% CI: 0.854–0.973).Conclusion: APT imaging may be a potential noninvasive biomarker for the prediction of histologic grading of HCC and complements IVIM imaging for the more accurate and comprehensive characterization of HCC.

Background: Neonatal hyperbilirubinemia (NHB) is a common clinical disease and can cause bilirubin encephalopathy in severe cases. It is now widely accepted that increased signal intensity in the globus pallidus on MR T1WI is an important sign of neonatal bilirubin encephalopathy. And brain diffusion tensor imaging (DTI) has not been used extensively to study hyperbilirubinemia (HB). So we compared newborns with different hyperbilirubinemia of different severities and healthy newborns in order to determine the relationships among MRI signal intensities, serum bilirubin levels, and the molecular changes in brain water diffusion in hyperbilirubinemia. Methods: Seventy-three newborns with hyperbilirubinemia were grouped into three groups: the mild increase group (M, 27 cases), the moderate increase group (O, 28 cases), and the severe group (S, 18 cases). The groups were based on serum bilirubin levels. We performed cranial MRI in these newborns, as well as 29 healthy full-term infants (group N). We compared and analyzed the mean signal values for the globus pallidus and the relationship between the bilirubin level and the score on the neonatal behavioral neurological assessment. Fifteen, 10, and 10 patients in groups M, O + S, and N were successfully examined using diffusion tensor imaging (DTI). We assessed the relationships among the signal from the globus pallidus, fractional anisotropy (FA), and average diffusion coefficient (DCav) of the posterior limb of the internal capsule (PLIC). Results: There were significant differences in the mean signal value of bilateral globus pallidus between group O/S and group N [p = 0.029 and 0.000 (left), 0.038 and 0.000 (right)]. There were no significant differences in bilateral FA or DCav values between the patient groups and group N. The bilateral PLIC-FA and DCav values were significantly different between the patient groups and group N (P = 0.014 and 0.047, respectively). Conclusions: Increased signal intensity in the globus pallidus on T1-weighted imaging can be used as an objective index to evaluate neonatal bilirubin encephalopathy. Globus pallidus and PLIC injuries are likely to occur when the total serum bilirubin level is ≥20 mg/dl. Key Words: neonates, neonatal hyperbilirubinemia, neonatal bilirubin encephalopathy, MRI, diffusion tensor imaging (DTI)