Identification and validation of a glycosyltransferase gene signature as a novel prognostic model for lung adenocarcinoma.

Background: The role of glycosyltransferase (GT) genes in lung adenocarcinoma (LUAD) needs further elucidation. Thus, our study aims to identify the prognostic gene signature of LUAD and explore its molecular functions. Methods: We initially extracted GT gene sets from the database, and obtained mRNA expression levels and clinical data from The Cancer Genome Atlas (TCGA) database. For constructing a prognostic model for GT genes, we utilized univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Using the model, patients were categorized into high- and low-risk groups. Additionally, we evaluated differences in tumor immune infiltration between these groups and identified potential therapeutic drugs. Finally, we experimentally validated the expression levels of these crucial prognostic genes. Results: We developed a risk score comprising nine GT genes (C1GALT1, FUT1, GALNT2, PLOD2, POMK, PYGB, ST3GAL6, UGT2B11, UGT3A1). Patients were then categorized into low- and high-risk groups based on this score. The low-risk group showed superior overall survival (OS) compared to the high-risk group. There were significantly distinct tumor immune microenvironment statuses observed between the two groups. We identified potential therapeutic drugs, including the MEK inhibitor (PD-184352). Finally, we verified the expression of these nine GT genes through immunohistochemistry (IHC) staining and quantitative real-time PCR (qPCR). Conclusion: We identified a distinct LUAD GT gene signature, and these differentially expressed mRNAs could serve as valuable prognostic biomarkers and therapeutic targets. Furthermore, we experimentally validated their expression levels and identified potential therapeutic agents.

Predicting the malignancy of extremity soft-tissue tumors by an ultrasound-based radiomics signature.

BACKGROUND: Accurate differentiation of extremity soft-tissue tumors (ESTTs) is important for treatment planning. PURPOSE: To develop and validate an ultrasound (US) image-based radiomics signature to predict ESTTs malignancy. MATERIAL AND METHODS: A dataset of US images from 108 ESTTs were retrospectively enrolled and divided into the training cohort (78 ESTTs) and validation cohort (30 ESTTs). A total of 1037 radiomics features were extracted from each US image. The most useful predictive radiomics features were selected by the maximum relevance and minimum redundancy method, least absolute shrinkage, and selection operator algorithm in the training cohort. A US-based radiomics signature was built based on these selected radiomics features. In addition, a conventional radiologic model based on the US features from the interpretation of two experienced radiologists was developed by a multivariate logistic regression algorithm. The diagnostic performances of the selected radiomics features, the US-based radiomics signature, and the conventional radiologic model for differentiating ESTTs were evaluated and compared in the validation cohort. RESULTS: In the validation cohort, the area under the curve (AUC), sensitivity, and specificity of the US-based radiomics signature for predicting ESTTs malignancy were 0.866, 84.2%, and 81.8%, respectively. The US-based radiomics signature had better diagnostic predictability for predicting ESTT malignancy than the best single radiomics feature and the conventional radiologic model (AUC = 0.866 vs. 0.719 vs. 0.681 for the validation cohort, all P <0.05). CONCLUSION: The US-based radiomics signature could provide a potential imaging biomarker to accurately predict ESTT malignancy.

Improved estimation of general cognitive ability and its neural correlates with a large battery of cognitive tasks.

Elucidating the neural mechanisms of general cognitive ability (GCA) is an important mission of cognitive neuroscience. Recent large-sample cohort studies measured GCA through multiple cognitive tasks and explored its neural basis, but they did not investigate how task number, factor models, and neural data type affect the estimation of GCA and its neural correlates. To address these issues, we tested 1,605 Chinese young adults with 19 cognitive tasks and Raven's Advanced Progressive Matrices (RAPM) and collected resting state and n-back task fMRI data from a subsample of 683 individuals. Results showed that GCA could be reliably estimated by multiple tasks. Increasing task number enhances both reliability and validity of GCA estimates and reliably strengthens their correlations with brain data. The Spearman model and hierarchical bifactor model yield similar GCA estimates. The bifactor model has better model fit and stronger correlation with RAPM but explains less variance and shows weaker correlations with brain data than does the Spearman model. Notably, the n-back task-based functional connectivity patterns outperform resting-state fMRI in predicting GCA. These results suggest that GCA derived from a multitude of cognitive tasks serves as a valid measure of general intelligence and that its neural correlates could be better characterized by task fMRI than resting-state fMRI data.

Evaluating COVID-19 Risk to Essential Workers by Occupational Group: A Case Study in Massachusetts.

Occupational exposure to SARS-CoV-2 varies by profession, but "essential workers" are often considered in aggregate in COVID-19 models. This aggregation complicates efforts to understand risks to specific types of workers or industries and target interventions, specifically towards non-healthcare workers. We used census tract-resolution American Community Survey data to develop novel essential worker categories among the occupations designated as COVID-19 Essential Services in Massachusetts. Census tract-resolution COVID-19 cases and deaths were provided by the Massachusetts Department of Public Health. We evaluated the association between essential worker categories and cases and deaths over two phases of the pandemic from March 2020 to February 2021 using adjusted mixed-effects negative binomial regression, controlling for other sociodemographic risk factors. We observed elevated COVID-19 case incidence in census tracts in the highest tertile of workers in construction/transportation/buildings maintenance (Phase 1: IRR 1.32 [95% CI 1.22, 1.42]; Phase 2: IRR: 1.19 [1.13, 1.25]), production (Phase 1: IRR: 1.23 [1.15, 1.33]; Phase 2: 1.18 [1.12, 1.24]), and public-facing sales and services occupations (Phase 1: IRR: 1.14 [1.07, 1.21]; Phase 2: IRR: 1.10 [1.06, 1.15]). We found reduced case incidence associated with greater percentage of essential workers able to work from home (Phase 1: IRR: 0.85 [0.78, 0.94]; Phase 2: IRR: 0.83 [0.77, 0.88]). Similar trends exist in the associations between essential worker categories and deaths, though attenuated. Estimating industry-specific risk for essential workers is important in targeting interventions for COVID-19 and other diseases and our categories provide a reproducible and straightforward way to support such efforts.

Smoking and multiple sclerosis risk in black people: A nested case-control study.

BACKGROUND: Smoking is a well-established risk factor for MS; however, it is not known whether its effect on disease risk varies by race/ethnicity. METHODS: We conducted a nested case-control study among US military personnel who have serum samples stored at the Department of Defense Serum Repository. We measured serum cotinine levels, a marker of tobacco smoke exposure, in 157 Black and 23 White individuals who developed MS during follow-up. Controls were randomly selected and matched to each case by age, sex, race/ethnicity, dates of sample collection, and branch of military service. RESULTS: Smoking was not associated with an increased risk of MS in Black people (RR: 1.08, 95 % CI: 0.63-1.85). The results remained similar in analyses restricted to smoking status at baseline, to samples collected 5 years before symptom onset, and using different cut-off levels in cotinine to define smoking status. Smoking was not statistically significantly associated with MS risk in White people, but the point estimate was similar to what has previously been reported in other studies (RR: 1.85, 95 % CI: 0.56-6.16). CONCLUSIONS: Smoking was not associated with MS risk in Black people. Given the consistent association between smoking and MS risk in predominantly White populations, this may suggest that the association between smoking and MS varies by race/ethnicity.

A predictive model for the identification of the risk of sepsis in patients with Gram-positive bacteria in the intensive care unit.

Background: Gram-positive bacterial infections are very common in the intensive care unit (ICU) and may lead to sepsis. However, there are no models to predict the risk of sepsis in persons with Gram-positive bacterial infections. Therefore, the purpose of this study was to create and validate a nomogram for predicting the risk of sepsis in patients with common gram-positive bacterial infections. Methods: Patients infected with three common Gram-positive bacteria who were admitted to the Multiparameter Intelligent Monitoring in Intensive Care IV (MIMIC IV) database were included in this retrospective cohort study. A Cox regression model was used to develop a nomogram for predicting 3-day, 1-week, 2-week, and 1-month sepsis probability. The performance of the nomogram was analyzed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results: In total, 19,961 eligible patients were enrolled from MIMIC IV datasets. All participants were allocated to training and validation cohorts at random in a 7:3 ratio. The use of more than 3 types of antibiotics, dementia, ethnicity, aspartate aminotransferase (AST), neutrophils, the use of antifungal drug, ventilation and need for vasopressors were all discovered to be highly correlated with enhanced probability of sepsis in patients with Gram-positive bacteria. A prediction nomogram was constructed using these 8 predictors. The area under the curve (AUC) for predicting 3-day, 1-week, 2-week, and 1-month sepsis risk in the training cohort was 0.857, 0.774, 0.740, and 0.728, respectively, and that in the validation cohort was 0.855, 0.781, 0.742, and 0.742, respectively. The predictive power of our model is better than the SOFA score. The model had good predictive performance in all three classes of Gram-positive bacteria. Based on the calibration and clinical decision curves, the nomogram correctly predicted sepsis in patients with Gram-positive bacteria. Conclusions: We were able to build a nomogram to predict the probability of sepsis in patients with Gram-positive bacteria, particularly those infected with Streptococcus spp. and Staphylococcus spp. This model performs effectively, and it might be used clinically to manage patients with Gram-positive bacteria.

Metabolomic pathway regulation for prevention and control of granule sludge bulking in thiosulfate-driven denitrification.

The performance of thiosulfate-driven denitrification (TDD) granule reactor and the mechanism of granule sludge bulking were investigated in this study. The results showed that TDD granule bulking occurred under 12 kgNm-3d-1 of nitrogen loading rate (NLR). The higher NLR promoted accumulation of intermediates in the carbon fixation pathway, including citrate, oxaloacetate, oxoglutarate and fumarate. The carbon fixation improved amino acids biosynthesis, which increased proteins (PN) in extracellular polymers (EPS) to 134.6 ± 11.8 mg/gVSS. The excessive PN altered the content, components and chemical groups of EPS, leading to change of granule structure and decline in settling property, permeability and nitrogen removal. By adopting the strategy of intermittently reducing NLR, excess amino acids in sulfur-oxidizing bacteria was consumed through microbial growth-related metabolism instead of EPS synthesis. Therefore, the nitrogen removal rate increased to 10.23 kg-Nm-3d-1 and maintained stable in the long term. The EPS contents decreased from 168.8 ± 13.5 mg/gVSS to 93 ± 11.5 mg/gVSS and the SVI5 decreased from 66 ± 3.5 ml/g to 25 ± 1.5 ml/g. These findings provide an effective strategy to prevent granule bulking and guide practical application of TDD process.

A multiparametric clinic-ultrasomics nomogram for predicting extremity soft-tissue tumor malignancy: a combined retrospective and prospective bicentric study.

OBJECTIVE: We aimed at building and testing a multiparametric clinic-ultrasomics nomogram for prediction of malignant extremity soft-tissue tumors (ESTTs). MATERIALS AND METHODS: This combined retrospective and prospective bicentric study assessed the performance of the multiparametric clinic-ultrasomics nomogram to predict the malignancy of ESTTs, when compared with a conventional clinic-radiologic nomogram. A dataset of grayscale ultrasound (US), color Doppler flow imaging (CDFI), and elastography images for 209 ESTTs were retrospectively enrolled from one hospital, and divided into the training and validation cohorts. A multiparametric ultrasomics signature was built based on multimodal ultrasomic features extracted from the grayscale US, CDFI, and elastography images of ESTTs in the training cohort. Another conventional radiologic score was built based on multimodal US features as interpreted by two experienced radiologists. Two nomograms that integrated clinical risk factors and the multiparameter ultrasomics signature or conventional radiologic score were respectively developed. Performance of the two nomograms was validated in the retrospective validation cohort, and tested in a prospective dataset of 51 ESTTs from the second hospital. RESULTS: The multiparametric ultrasomics signature was built based on seven grayscale ultrasomic features, three CDFI ultrasomic features, and one elastography ultrasomic feature. The conventional radiologic score was built based on five multimodal US characteristics. Predictive performance of the multiparametric clinic-ultrasomics nomogram was superior to that of the conventional clinic-radiologic nomogram in the training (area under the receiver operating characteristic curve [AUC] 0.970 vs. 0.890, p = 0.006), validation (AUC: 0.946 vs. 0.828, p = 0.047) and test (AUC: 0.934 vs. 0.842, p = 0.040) cohorts, respectively. Decision curve analysis of combined training, validation and test cohorts revealed that the multiparametric clinic-ultrasomics nomogram had a higher overall net benefit than the conventional clinic-radiologic model. CONCLUSION: The multiparametric clinic-ultrasomics nomogram can accurately predict the malignancy of ESTTs.

Influence of geospatial resolution on sociodemographic predictors of COVID-19 in Massachusetts.

PURPOSE: When studying health risks across a large geographic region such as a state or province, researchers often assume that finer-resolution data on health outcomes and risk factors will improve inferences by avoiding ecological bias and other issues associated with geographic aggregation. However, coarser-resolution data (e.g., at the town or county-level) are more commonly publicly available and packaged for easier access, allowing for rapid analyses. The advantages and limitations of using finer-resolution data, which may improve precision at the cost of time spent gaining access and processing data, have not been considered in detail to date. METHODS: We systematically examine the implications of conducting town-level mixed-effect regression analyses versus census-tract-level analyses to study sociodemographic predictors of COVID-19 in Massachusetts. In a series of negative binomial regressions, we vary the spatial resolution of the outcome, the resolution of variable selection, and the resolution of the random effect to allow for more direct comparison across models. RESULTS: We find stability in some estimates across scenarios, changes in magnitude, direction, and significance in others, and tighter confidence intervals on the census-tract level. Conclusions regarding sociodemographic predictors are robust when regions of high concentration remain consistent across town and census-tract resolutions. CONCLUSIONS: Inferences about high-risk populations may be misleading if derived from town- or county-resolution data, especially for covariates that capture small subgroups (e.g., small racial minority populations) or are geographically concentrated or skewed (e.g., % college students). Our analysis can help inform more rapid and efficient use of public health data by identifying when finer-resolution data are truly most informative, or when coarser-resolution data may be misleading.

The neural representations underlying asymmetric cross-modal prediction of words.

Cross-modal prediction serves a crucial adaptive role in the multisensory world, yet the neural mechanisms underlying this prediction are poorly understood. The present study addressed this important question by combining a novel audiovisual sequence memory task, functional magnetic resonance imaging (fMRI), and multivariate neural representational analyses. Our behavioral results revealed a reliable asymmetric cross-modal predictive effect, with a stronger prediction from visual to auditory (VA) modality than auditory to visual (AV) modality. Mirroring the behavioral pattern, we found the superior parietal lobe (SPL) showed higher pattern similarity for VA than AV pairs, and the strength of the predictive coding in the SPL was positively correlated with the behavioral predictive effect in the VA condition. Representational connectivity analyses further revealed that the SPL mediated the neural pathway from the visual to the auditory cortex in the VA condition but was not involved in the auditory to visual cortex pathway in the AV condition. Direct neural pathways within the unimodal regions were found for the visual-to-visual and auditory-to-auditory predictions. Together, these results provide novel insights into the neural mechanisms underlying cross-modal sequence prediction.

Added value of contrast-enhanced ultrasound to conventional ultrasound for characterization of indeterminate soft-tissue tumors.

OBJECTIVE: To assess the added value of contrast-enhanced ultrasound (CEUS) to conventional ultrasound in differentiating benign soft-tissue tumors from malignant ones. METHODS: 197 soft-tissue tumors underwent ultrasound examination with confirmed histopathology were retrospectively evaluated. The radiologists classified all the tumors as benign, malignant, or indeterminate according to ultrasound features. The indeterminate tumors underwent CEUS were reviewed afterwards for malignancy identification by using individual and combined CEUS features. RESULTS: Ultrasound analysis classified 62 soft-tissue tumors as benign, 111 tumors as indeterminate and 24 tumors as malignant. There 104 indeterminate tumors were subject to CEUS. Three CEUS features including enlargement of enhancement area, infiltrative enhancement boundary, and intratumoral arrival time difference were significantly associated with the tumor nature in both univariable and multivariable analysis for the indeterminate tumors (all p < 0.05). When at least one out of the three discriminant CEUS features were present, the best sensitivity of 100% for malignancy identification was obtained with the specificity of 66.7% and the AUC of 0.833. When at least two of the three discriminant CEUS features were present, the best area under the receiver operating characteristic curve (AUC) of 0.924 for malignancy identification was obtained. The combination of at least two discriminant CEUS features showed much better diagnostic performance than the optimal combination of ultrasound features in terms of AUC (0.924 vs 0.608, p < 0.0001), sensitivity (94.0% vs 42.0%, p < 0.0001), and specificity (90.7% vs 79.6%, p = 0.210) for the indeterminate tumors. CONCLUSION: The combination CEUS features of enlargement of enhancement area, infiltrative enhancement boundary and intratumoral arrival time difference are valuable to improve the discriminating performance for indeterminate soft-tissue tumors on conventional ultrasound. ADVANCES IN KNOWLEDGE: The combination of peritumoral and arrival-time CEUS features can improve the discriminating performance for indeterminate soft-tissue tumors on conventional ultrasound.

Influence of Gestational Hypertension on Cardiovascular Health Behaviors and Factors.

Objective: A survey was conducted to analyze the epidemiological differences in ideal cardiovascular health (CVH) behaviors and factors after delivery in females with and without gestational hypertension (GH) and evaluate the influence of GH on cardiovascular health behaviors and factors. Methods: The present study adopted a cross-sectional design. A total of 4620 female workers who gave birth between 1976 and 2012 and received the annual health examination (2012 to 2013) at hospitals belonging to the Kailuan Medical Group were recruited. These subjects were divided into the GH group and non-GH (NGH) group, depending on whether they were combined with GH or not at delivery. The epidemiological differences in CVH behaviors and factors were compared between the two groups. Result: In both groups, the percentage of subjects achieving ideal smoking status was the highest, while the percentage of subjects achieving an ideal level of physical activity was the lowest among all behaviors and factors. Compared with the NGH group, the percentages of subjects achieving each of the seven ideal CVH metrics decreased in the GH group. The percentages of subjects achieving ideal body mass index (BMI), blood pressure, blood glucose level, and cholesterol level were significantly lower in the GH group than in the NGH group (P < 0.05). The percentage of subjects with an ideal level of physical activity was higher in the NGH group than in the GH group. After stratification by age, the percentages of patients achieving ideal BMI, blood pressure, and blood glucose decreased with age regardless of the history of GH (P < 0.05). In the younger age group, the percentage of subjects with GH achieving ideal body mass index was significantly lower than that of those without GH. Conclusion: Compared with females without GH, those with GH had higher BMI, blood pressure, blood glucose level, and cholesterol level among the seven CVH metrics surveyed.

A system dynamic model for simulating the potential of prefabrication on construction waste reduction.

Prefabrication is a promising method for minimizing construction waste since it is conducted in a controlled environment. This paper develops a simulation model for quantitatively evaluating the potential of prefabrication on construction waste reduction by considering interaction behaviors among factors influencing the application of prefabrication and construction waste reduction during the design stage. The theory of planned behavior is applied to determine the system boundary, and a system dynamic model is applied for establishing the simulation model. A case project in Anhui, China, is selected for demonstrating the established model. Results show that the (1) Application of prefabrication method contributes to construction waste reduction by reducing material wastes and reworking due to design changes. (2) Impacts of prefabrication method on concrete waste reduction is the most significant. (3) Increasing investment on designers' professional training and strengthening policies is two efficient strategies to make full use of the potential of the prefabrication method on construction waste reduction during the design stage. The developed model can offer designers as well as policymakers with references for applying prefabrication method for construction waste reduction by comparing outcomes under various scenarios with different strategies and policies ahead of implementation.

Community predictors of COVID-19 cases and deaths in Massachusetts: Evaluating changes over time using geospatially refined data.

BACKGROUND: The COVID-19 pandemic has highlighted the need for targeted local interventions given substantial heterogeneity within cities and counties. Publicly available case data are typically aggregated to the city or county level to protect patient privacy, but more granular data are necessary to identify and act upon community-level risk factors that can change over time. METHODS: Individual COVID-19 case and mortality data from Massachusetts were geocoded to residential addresses and aggregated into two time periods: "Phase 1" (March-June 2020) and "Phase 2" (September 2020 to February 2021). Institutional cases associated with long-term care facilities, prisons, or homeless shelters were identified using address data and modeled separately. Census tract sociodemographic and occupational predictors were drawn from the 2015-2019 American Community Survey. We used mixed-effects negative binomial regression to estimate incidence rate ratios (IRRs), accounting for town-level spatial autocorrelation. RESULTS: Case incidence was elevated in census tracts with higher proportions of Black and Latinx residents, with larger associations in Phase 1 than Phase 2. Case incidence associated with proportion of essential workers was similarly elevated in both Phases. Mortality IRRs had differing patterns from case IRRs, decreasing less substantially between Phases for Black and Latinx populations and increasing between Phases for proportion of essential workers. Mortality models excluding institutional cases yielded stronger associations for age, race/ethnicity, and essential worker status. CONCLUSIONS: Geocoded home address data can allow for nuanced analyses of community disease patterns, identification of high-risk subgroups, and exclusion of institutional cases to comprehensively reflect community risk.

Qualitative and Quantitative Contrast-Enhanced Ultrasound Combined with Conventional Ultrasound for Predicting the Malignancy of Soft Tissue Tumors.

This study was aimed at evaluating the performance of perfusion patterns and the quantitative parameters of contrast-enhanced ultrasound (CEUS) in the detection of soft tissue tumors (STTs) and establishing a US workflow for STTs to improve patient management. Conventional ultrasound (US) and CEUS data were retrospectively collected from 156 soft tissue masses. Six perfusion patterns (P1-P6) were applied for CEUS qualitative analysis. Multivariate logistic regression was used to evaluate the performance of conventional US and qualitative and quantitative CEUS in distinguishing benign and malignant STTs. The malignancy rates of P1-P6 in STTs were 0%, 50.0%, 9.1%, 33.3%, 73.4% and 61.0%, respectively. For "non-P1" STTs, the predictive model combining quantitative CEUS parameters with conventional US features, including margin (odds ratio [OR] = 4.490, p = 0.000), vascular density (OR = 2.307, p = 0.013), 50% wash-out intensity (OR = 1.904, p = 0.032) and 50% wash-out time (OR = 1.031, p = 0.019), performed favorably in predicting malignancy, with an accuracy of 81.0% and an area under the receiver operating characteristic curve of 0.868. Furthermore, a US workflow for the detection of STTs based on conventional US and CEUS was established. CEUS with qualitative and quantitative analyses could be an effective tool for STT diagnosis. The US workflow in this study may improve the management of STT patients.

Relationship Between Ultrasound Features and Ki-67 Labeling Index of Soft Tissue Sarcoma.

OBJECTIVES: To explore the relationship between ultrasound (US) features and Ki-67 labeling index (LI) of soft tissue sarcoma (STS). METHODS: Forty-six patients with 47 STS lesions, between September 2014 and April 2020, were enrolled in the study. Point-biserial correlation analysis and Spearman's correlation analysis were utilized to examining the relationship between the US features and the Ki-67 LI of STS. The differences of US features between high and low Ki-67 proliferation groups were statistically analyzed by independent t test, Wilcoxon rank-sum test, and Fisher's exact test. The optimal cut-off points of US features revealing significant differences were estimated by the maximum Youden index. RESULTS: A moderate correlation between the vascular density grade and the Ki-67 LI (ρ = 0.409, P = 0.004) was found in this study. In addition, other ultrasound features were irrelevant to the Ki-67 LI. The cut-off for differentiating low- and high-proliferation groups was grade II according to the best Youden index. The area under receiver operating characteristic (ROC) curve was 0.74 (p = 0.011) with a sensitivity of 60.6% and specificity of 78.6%. CONCLUSIONS: Only the vascular density grade of STS had a weak positive correlation with Ki-67 LI, and might be capable of predicting the proliferation of STS. Other ultrasonographic features of STS such as shape and tumor margin have no correlation with Ki-67 LI.

Nomogram Based on Ultrasonography and Clinical Features for Predicting Malignancy in Soft Tissue Tumors.

PURPOSE: The objective of this study was to establish a predictive nomogram based on ultrasound (US) and clinical features for patients with soft tissue tumors (STTs). PATIENTS AND METHODS: A total of 260 patients with STTs were enrolled in this retrospective study and were divided into a training cohort (n=200, including 110 malignant and 90 benign masses) and a validation cohort (n=60, including 30 malignant and 30 benign masses). Multivariate analysis was performed by binary logistic regression analysis to determine the significant factors predictive of malignancy. A simple nomogram was established based on these independent risk factors including US and clinical features. The predictive accuracy and discriminative ability of the nomogram were measured by the calibration curve and the concordance index (C-index). RESULTS: The nomogram, comprising US features (maximum diameter, margin and vascular density) and clinical features (sex, age, and duration of disease), showed a favorable performance for predicting malignancy, with a sensitivity of 88.2% and a specificity of 78.7%. The calibration curve for malignancy probability in the training cohort showed good agreement between the nomogram predictions and actual observations. The C-indexes of the training cohort and validation cohort for predicting malignancy were 0.89 (95% CI: 0.85-0.94) and 0.83 (95% CI: 0.73-0.94), respectively. CONCLUSION: The nomogram based on US and clinical features could be a simple, intuitive and reliable tool to individually predict malignancy in patients with STTs.

Diagnostic Performance of Elastography in Malignant Soft Tissue Tumors: A Systematic Review and Meta-analysis.

Malignant soft tissue tumors (STTs) are often mistaken for benign tumors, leading to inappropriate treatment including unplanned resection. Elastography, as a non-invasive measurement of tissue mechanical properties, makes use of the different soft tissue elasticity in diverse pathologies to generate information that can be used for diagnostic purposes. Elastography for STTs carries important information that is helpful in differentiating malignant and benign masses. The present study was undertaken to systematically review existing trials on the reliability of elastography in assessment of malignant STTs. A comprehensive literature exploration of the PubMed, EMbase and China National Knowledge Infrastructure databases was conducted for published articles involving the application of elastography in distinguishing malignant STTs. The diagnostic performance of elastography was evaluated with pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and area under the summary receiver operating characteristic curve. Publication bias was also evaluated. This meta-analysis enrolled 18 eligible studies with a total of 1420 patients. The overall number of reported STTs was 1569, of which 478 were classified as positive and 1091 as negative at elastography. The pooled sensitivity, specificity, positive likelihood ratio and negative likelihood ratio of elastography were 0.82 (95% confidence interval: 0.74-0.87), 0.80 (0.71-0.86), 3.99 (2.65-6.01) and 0.23 (0.15-0.34), respectively. The diagnostic odds ratio and area under the curve were 17.36 (8.28-36.38) and 0.88 (0.84-0.90), respectively (Glas et al. 2003). The results of meta-regression analysis revealed that the total number of patients and prevalence of malignant STTs were significant factors in sensitivity, and the year of publication, total number of patients and index test were significant factors affecting study heterogeneity for specificity (p < 0.05). No significant publication bias was observed. This meta-analysis indicates that ultrasound elastography achieves relatively good performance in discriminating between malignant and benign STTs. Nevertheless, further research is needed to verify this finding.