Features Of Matrix Research Articles

Ni-Doped Mo2C Anchored on Graphitized Porous Carbon for Boosting Electrocatalytic N2 Reduction.

Facilitating the efficient activation of N2 molecules and inhibiting the competing hydrogen evolution reaction remain a challenge in the nitrogen reduction reaction (NRR). A heteroatom doping strategy is an effective way to optimize the energy barrier during the NRR process to improve the catalytic efficiency. Herein, we report Ni-doped Mo2C anchored on graphitized porous conductive carbon for regulating the electronic structure and catalytic properties of electrocatalysts toward NRR. Benefiting from the porous structure and graphitization features of the carbon matrix, more active sites and high electronic conductivity were achieved. Meanwhile, with the doping of Ni atoms, the electronic configuration near the Ni-Mo active sites was optimized and the adsorption of N2 on them was also promoted due to the increased electron transfer. Moreover, the lowered energy barrier of the NRR process and the suppressed hydrogen adsorption on the active site all resulted in the high catalytic activity and selectivity of the catalyst. Therefore, a high NH3 yield rate of 46.49 μg h-1 mg-1 and a faradic efficiency of 29.05% were achieved. This work not only validates the important role of heteroatom doping on the regulation of NRR catalytic activity but also provides a promising avenue for the green synthesis of NH3.

IN VITRO CELL NICHE ENGINEERING FOR PHENOTYPE MAINTENANCE OF HUMAN OSTEOARTHRITIS CHONDROCYTES

Purpose: Cells in a native tissue resides in a unique 3D environment known as the cell niche, this microenvironment filled with cell-specific structural and biochemical cues determines correct cell fate, phenotype, and expression. Cell cultured in 2D plastic dish has shown increasing evidence in behaving differently to native cell niche, unable to provide biomimetic features of the native extracellular matrix. Chondrocyte cultured in 2D specifically were found to de-differentiate with altered physiological phenotype and genetic expression.

Subungual melanoma with blue naevus-like morphological features: a clinicopathological retrospective analysis of nine cases

Melanocytic lesions in the nail apparatus are often challenging. Both subungual melanomas (SUM) and blue naevus of the nail are very rare. Occasionally, melanomas may mimic blue naevus histologically. Benign and malignant blue melanocytic lesions are commonly associated with G protein mutations, a distinct abnormality not associated with conventional subungual melanomas. We describe the clinical, histological and immunohistochemical features of nine cases of SUM with blue naevus-like morphological features. Mutations in exon 4 and 5 of GNAQ and GNA11 were investigated in two cases, which showed no mutations. RNA-seq of one case revealed unknown mutations along with mutations in ATM, METK and ARID1A. Our study delineates a variant of SUM that mimics blue naevus. Awareness of this pitfall is important when evaluating heavily pigmented lesions around the nail in order to avoid misdiagnosis. Appropriate sampling of subungual lesions and clinicopathological correlation are paramount to reach the correct diagnosis.

Into the Tissues: Extracellular Matrix and Its Artificial Substitutes: Cell Signalling Mechanisms.

The existence of orderly structures, such as tissues and organs is made possible by cell adhesion, i.e., the process by which cells attach to neighbouring cells and a supporting substance in the form of the extracellular matrix. The extracellular matrix is a three-dimensional structure composed of collagens, elastin, and various proteoglycans and glycoproteins. It is a storehouse for multiple signalling factors. Cells are informed of their correct connection to the matrix via receptors. Tissue disruption often prevents the natural reconstitution of the matrix. The use of appropriate implants is then required. This review is a compilation of crucial information on the structural and functional features of the extracellular matrix and the complex mechanisms of cell–cell connectivity. The possibilities of regenerating damaged tissues using an artificial matrix substitute are described, detailing the host response to the implant. An important issue is the surface properties of such an implant and the possibilities of their modification.

Genetically Engineered Nanohyaluronidase Vesicles: A Smart Sonotheranostic Platform for Enhancing Cargo Penetration of Solid Tumors

AbstractPrecise anti‐cancer therapy is hampered by aberrant tumor microenvironments (TME), which include the important features of the dense tumor extracellular matrix (ECM) and hypoxia. The capability of hyaluronidase (HAase) to degrade hyaluronic acid (HA), the main component of ECM, can prompt its rapid development in sonotherapeutics aimed at regulating the microenvironment. In this study, the authors design a bioengineering‐based smart enzymatic nanovesicle mHAase combined with purpurin 18 (P18) to form mHAase@nP18 that naturally anchors native HAase to the matrix metalloproteinase‐2 (MMP‐2) cleavable peptide. This nanovesicle system demonstrates a high expression of HAase and drug loading capability of the sonosensitizer, purpurin 18, for dual‐mode fluorescence/photoacoustic imaging‐guided sonodynamic therapy (SDT). Notably, mHAase@nP18 shows enhanced enzyme stability and activity in comparison to free HAase owing to avoidance of the protein crown shield; meanwhile, the released HAase causes a further elevation of its activity for SDT in an MMP‐2‐dependent manner. In vitro and in vivo results indicate the mHAase@nP18 can greatly enhance tumor penetration and alleviate hypoxia via HA degradation, further causing a heightened SDT effect. This work provides a promising strategy of stimuli‐responsive bioengineering of cell membrane vesicles for effective TME modulation and enhanced therapeutic results.

A general topology identification framework for distribution systems using smart meter and μ-PMU measurements

To enhance topology observability of the power distribution network (PDN), a general topology identification framework using multiple measurement data records of nodal voltages and currents is constructed in this paper. The framework includes three topology identification approaches to handle different situations. The first approach describes a modified three-stage heuristic procedure for the single topology identification while leveraging the sparsity feature of the nodal admittance matrix, which can lead to more accurate identification results than the base model in the literature; The second approach presents a multi-topology identification model to process the measurement data set with a given number of topologies, which classifies records into a fixed number of topology categories and then determines the nodal admittance matrix for each topology category; The third approach designs a two-stage procedure for the measurement data set containing an unknown number of topologies, which determines the number of topologies, the record classification, and the nodal admittance matrices of individual topologies. Effectiveness of the proposed models in efficiently identifying the PDN’s topologies is illustrated via several cases, with measurement data sets containing a given or an unknown number of topologies. In addition, the sensitivity analysis quantifies the influence of the number of records, the standard deviation of measurement errors, and the pre-specified maximum number of nonzero elements in each column of the nodal admittance matrix on the accuracy of the identification results.

Metabolic labeling of secreted matrix to investigate cell-material interactions in tissue engineering and mechanobiology.

Re-creating features of the native extracellular matrix (ECM) with engineered biomaterials has become a valuable tool to probe the influence of ECM properties on cellular functions (e.g., differentiation) and toward the engineering of tissues. However, characterization of newly secreted (nascent) matrix and turnover, which are important in the context of cells interacting with these biomaterials, has been limited by a lack of tools. We developed a protocol to visualize and quantify the spatiotemporal evolution of newly synthesized and deposited matrix by cells that are either cultured atop (2D) or embedded within (3D) biomaterial systems (e.g., hydrogels, fibrous matrices). This technique relies on the incorporation of a noncanonical amino acid (azidohomoalanine) into proteins as they are synthesized. Deposited nascent ECM components are then visualized with fluorescent cyclooctynes via copper-free cycloaddition for spatiotemporal analysis or modified with cleavable biotin probes for identification. Here we describe the preparation of hyaluronic acid hydrogels through ultraviolet or visible light induced cross-linking for 2D and 3D cell culture, as well as the fluorescent labeling of nascent ECM deposited by cells during culture. We also provide protocols for secondary immunofluorescence of specific ECM components and ImageJ-based ECM quantification methods. Hyaluronic acid polymer synthesis takes 2 weeks to complete, and hydrogel formation for 2D or 3D cell culture is performed in 2-3 h. Lastly, we detail the identification of nascent proteins, including enrichment, preparation and analysis with mass spectrometry, which can be completed in 10 d.

Ensemble Boosted Tree based Mammogram image classification using Texture features and extracted smart features of Deep Neural Network


 
 
 
 
 This work proposes a technique of breast cancer detection from mammogram images. It is a multistage process which classifies the mammogram images into benign or malignant category. During preprocessing, images of Mammographic Image Analysis Society (MIAS) database are passed through a couple of filters for noise removal, thresholding and cropping techniques to extract the region of interest, followed by augmentation process on database to enhance its size. Features from Deep Convolution Neural Network (DCNN) are merged with texture features to form final feature vector. Using transfer learning, deep features are extracted from a modified DCNN, whose training is performed on 69% of randomly selected images of database from both categories. Features of Grey Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are merged to form texture features. Mean and variance of four parameters (contrast, correlation, homogeneity and entropy) of GLCM are computed in four angular directions, at ten distances. Ensemble Boosted Tree classifier using five-fold cross-validation mode, achieved an accuracy, sensitivity, specificity of 98.8%, 100% and 92.55% respectively on this feature vector.
 
 
 

Bamboo Forest Mapping in China Using the Dense Landsat 8 Image Archive and Google Earth Engine

It is of great significance to understand the extent and distribution of bamboo for its valuable ecological services and economic benefits. However, it is challenging to map bamboo using remote sensing images over a large area because of the similarity between bamboo and other vegetation types, the availability of clear optical images, huge workload of image processing, and sample collection. In this study, we use the Landsat 8 times series images archive to map bamboo forests in China via the Google Earth engine. Several spectral indices were calculated and used as classification features, including the normalized difference vegetation index (NDVI), the normalized difference moisture index (NDMI) and textural features of the gray-level co-occurrence matrix (GLCM). We found that the bamboo forest covered an area of 709.92 × 104 hectares, with the provinces of Fujian, Jiangxi, and Zhejiang containing the largest area concentrations. The bamboo forest map was accurate and reliable with an average producer’s accuracy of 89.97%, user’s accuracy of 78.45% and kappa coefficient of 0.7789. In addition, bamboo was mainly distributed in forests with an elevation of 300–1200 m above sea level, average annual precipitation of 1200–1500 mm and average day land surface temperature of 19–25 °C. The NDMI is particularly useful in differentiating bamboo from other vegetation because of the clear difference in canopy moisture content, whilst NDVI and elevation are also helpful to improve the bamboo classification accuracy. The bamboo forest map will be helpful for bamboo forest industry planning and could be used for evaluating the ecological service of the bamboo forest.

Decoding the annulus fibrosus cell atlas by scRNA-seq to develop an inducible composite hydrogel: A novel strategy for disc reconstruction

Low back pain is one of the most serious public health problems worldwide and the major clinical manifestation of intervertebral disc degeneration (IVDD). The key pathological change during IVDD is dysfunction of the annulus fibrosus (AF). However, due to the lack of an in-depth understanding of AF biology, the methods to reconstruct the AF are very limited. In this study, the mice AF cell atlas were decoded by single-cell RNA sequencing to provide a guide for AF reconstruction. The results first identify a new population of AF cells, fibrochondrocyte-like AF cells, which synthesize both collagen I and collagen II and are potential functional cells for AF reconstruction. According to the dual features of the AF extracellular matrix, a composite hydrogel based on the acylation of methacrylated silk fibroin with methacrylated hyaluronic acid was produced. To obtain the ability to stimulate differentiation, the composite hydrogels were combined with a fibrochondrocyte-inducing supplement. Finally, reconstruction of the AF defects, by the novel AF stem cell-loaded composite hydrogel, could be observed, its amount of chondroid matrices recovered to 31.7% of AF aera which is significantly higher than that in other control groups. In summary, this study decodes the AF cell atlas, based on which a novel strategy for AF reconstruction is proposed.

Dual Attention Mechanisms and Feature Fusion Networks Based Method for Predicting LncRNA-Disease Associations.

LncRNAs play a part in numerous momentous processes of biology such as disease diagnoses, preventions and treatments. The associations between various diseases and lncRNAs are one of the crucial approaches to learn the role and status of lncRNAs in human diseases. With the researches on lncRNA and diseases, multiple methods based on neural network have been employed to predict these associations. However, the deep and complicated characteristic representations of lncRNA-disease associations were failed to be extracted, and the discriminative contributions of the interactions, correlations, and similarities among miRNAs diseases, and lncRNAs for the correlation predictions were ignored. In this paper, based on the multibiology premise of lncRNAs, miRNAs, and diseases, a dual attention network was proposed to predict the model of lncRNA-disease associations for miRNAs, the disease characteristic matrix, and lncRNAs. Through two attention modules, we enable the model to learn the nonlinear, more complex and useful features of lncRNA, miRNA, and disease characteristic matrix. For the feature embedding matrix composed of lncRNA-disease, the connection between lncRNA-disease feature embedding matrix and lncRNA, miRNA, and disease characteristic matrix was enhanced through deconvolution and feature fusion layer. Compared with several latest methods, the method proposed in this paper can produce better performance. Researches on the cases of osteosarcoma, lung cancer, and gastric cancer have confirmed the effective recognition of potential lncRNA-disease associations.

Photodynamic Activity of Protoporphyrin IX-Immobilized Cellulose Monolith for Nerve Tissue Regeneration.

The development of nerve conduits with a three-dimensional porous structure has attracted great attention as they closely mimic the major features of the natural extracellular matrix of the nerve tissue. As low levels of reactive oxygen species (ROS) function as signaling molecules to promote cell proliferation and growth, this study aimed to fabricate protoporphyrin IX (PpIX)-immobilized cellulose (CEPP) monoliths as a means to both guide and stimulate nerve regeneration. CEPP monoliths can be fabricated via a simple thermally induced phase separation method and surface modification. The improved nerve tissue regeneration of CEPP monoliths was achieved by the activation of mitogen-activated protein kinases, such as extracellular signal-regulated kinases (ERKs). The resulting CEPP monoliths exhibited interconnected microporous structures and uniform morphology. The results of in vitro bioactivity assays demonstrated that the CEPP monoliths with under 0.54 ± 0.07 μmol/g PpIX exhibited enhanced photodynamic activity on Schwann cells via the generation of low levels of ROS. This photodynamic activation of the CEPP monoliths is a cell-safe process to stimulate cell proliferation without cytotoxic side effects. In addition, the protein expression of phospho-ERK increased considerably after the laser irradiation on the CEPP monoliths with low content of PpIX. Therefore, the CEPP monoliths have a potential application in nerve tissue regeneration as new nerve conduits.

Morphological features of the cornea in eyes with pseudophakic bullous keratopathy and Fuchs endothelial corneal dystrophy

To evaluate the morphological features of posterior corneal layers in pseudophakic bullous keratopathy (PBK) and Fuchs endothelial corneal dystrophy (FECD). The study included 20 patients (20 eyes) aged 73.8±7.8 with PBK and 24 patients (25 eyes) aged 72.5±10.6 with FECD. Central corneal thickness was measured in all eyes before the surgery by anterior segment optical coherence tomography (AS-OCT, RTVue-100, Optovue, USA). All patients underwent corneal transplantation (DSAEK or DMEK). Postoperative donor graft status was assessed as attached or detached by AS-OCT (RTVue-100, Optovue, USA). Specimens of Descemet's membranes (DMs) were obtained intraoperatively and investigated histologically (hematoxylin and eosin staining). Morphometric analysis was performed using optical microscope Leica DM-2500 (Digital Color Camera Leica DFC295) and ImageScope M software. There was no difference in thickness of DMs' prenatal anterior banded layer (2.58±0.49 μm) and postnatal posterior non-banded layer (11.30±2.64 μm) between PBK and normal cornea. In FECD eyes, DM was significantly thickened by additional collagen layer and guttate excrescences (postnatal layer thickness - 16.62±4.34 μm). Morphological study of the DMs of patients with graft detachment in the early postoperative period revealed residual stromal collagen fibrils that were fixed to the area with dense extracellular matrix. The study shows that chronic corneal edema is associated with pathological morphological changes in the posterior corneal layers in PBK and FECD. Individual features of the dense extracellular matrix in the DMs may play a key role in the attachment of donor graft to the corneal stroma and affect the postoperative outcome.

A Unified Protocol to Streamline Molecular and Cellular Analysis for Three-Dimensional Cell Cultures.

Three-dimensional (3D) cell cultures based on reconstituted basement membrane materials recapitulate features of extracellular matrix (ECM) and tissue stiffness in vivo and provide a physiologically relevant platform to study complex cellular processes, such as stem cell differentiation and tissue morphogenesis, that are otherwise difficult in animal models. The form and composition of 3D matrices in culture can interfere with and pose challenges for different experimental setups and assays, which necessitate alterations to facilitate analysis. Here, we provide a unified protocol for 3D cell cultures with modular workflows that streamline procedures for compatibility with common molecular and cellular assays such as live-cell imaging, immunofluorescence , qPCR, RNAseq, western blotting, and quantitative mass spectrometry.

Flexural performance of low density foamed concrete (LDFC) incorporated with GGBS and RHA as filler

The incorporation of industrial and agricultural wastes consisting pozzolanicity into low density foamed concrete (LDFC) attains the various features of concrete matrix. The by-product from steel or iron making industries produces ground granulated blast furnace slag (GGBS) and rice husk ash (RHA) is generated from agricultural industries in the form of residual solid wastes. Such industrial and agricultural wastes can be utilized into LDFC because of its chemical compositions. The utilization of residual wastes in concrete matrix minimizes CO2 emissions, consumption of natural resources and attains economy. This study represents the effects on GGBS and RHA blended LDFC with 1300 ± 50 kg/m3 casting density considering flexural strength and toughness. The LDFC specimens were produced for experimental testing were 100% cement (LDFC-CM) as control mix, LDFC with 5% replacement of cement using GGBS and RHA (LDFC-G5 and LDFC-R5) and, LDFC with 10% GGBS and RHA (LDFC-G10 and LDFC-R10). Also, using ANSYS Workbench 14.5, the modeling of samples was carried out to evaluate various parameters like deflection, stress intensity and strain intensity. The performance of LDFC incorporated with GGBS and RHA as replacement filler shows superior outcomes in comparison with LDFC-CM considering of flexural strength and toughness. The existing research highlights the possibilities of GGBS and RHA as an effective supplementary material which achieves the characteristics of reasonable and durable concrete.

Fibrous Polymer-Based Composites Obtained by Electrospinning for Bone Tissue Engineering.

Currently, the significantly developing fields of tissue engineering related to the fabrication of polymer-based materials that possess microenvironments suitable to provide cell attachment and promote cell differentiation and proliferation involve various materials and approaches. Biomimicking approach in tissue engineering is aimed at the development of a highly biocompatible and bioactive material that would most accurately imitate the structural features of the native extracellular matrix consisting of specially arranged fibrous constructions. For this reason, the present research is devoted to the discussion of promising fibrous materials for bone tissue regeneration obtained by electrospinning techniques. In this brief review, we focus on the recently presented natural and synthetic polymers, as well as their combinations with each other and with bioactive inorganic incorporations in order to form composite electrospun scaffolds. The application of several electrospinning techniques in relation to a number of polymers is touched upon. Additionally, the efficiency of nanofibrous composite materials intended for use in bone tissue engineering is discussed based on biological activity and physiochemical characteristics.

Internal Damage Analysis of Braided Composites Embedded in Carbon Nanotube Yarn

Carbon nanotube (CNT) yarn sensors were embedded in 3D braided composites in the form of arrays to detect the internal damage of specimens and study the internal damage monitoring of the 3D braided composites. The signals collected by the sensor array of CNT yarn were preprocessed using the dynamic wavelet threshold algorithm. The exact position of the damage was calculated based on the main features of the resistance signal matrix, which was calculated using the quadratic matrix singular value. The results show that the internal damage localization of the specimens was consistent with the actual damage. The localizations in this study can provide a basis for enhancing the structural health monitoring of smart 3D braided composites.

Prediction of Clinically Significant Cancer Using Radiomics Features of Pre-Biopsy of Multiparametric MRI in Men Suspected of Prostate Cancer.

Simple SummaryRadiomics is the field of computer-based medical image analysis that incorporates various radiological imaging features, such as texture and shape parameters, from scans to derive algorithms. These mathematical algorithms have the potential to predict the biological characteristics of disease. In this study, we obtained quantitative imaging texture features of pre-biopsy multiparametric MRI of men suspected of prostate cancer and extracted from the T2WI and ADC images focusing on gray-level co-occurrence matrices (GLCM). These were correlated with the Gleason score of the histopathology of radical prostatectomy specimen, including the prediction of clinically significant prostate cancer. The knowledge gained through this prospective protocol-based study should facilitate establishing that GLCM texture features alone can be used as a biomarker for predicting the presence of clinically significant PCa.Background: Texture features based on the spatial relationship of pixels, known as the gray-level co-occurrence matrix (GLCM), may play an important role in providing the accurate classification of suspected prostate cancer. The purpose of this study was to use quantitative imaging parameters of pre-biopsy multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer. Methods: This was a prospective study, recruiting 200 men suspected of having prostate cancer. Participants were imaged using a protocol-based 3T MRI in the pre-biopsy setting. Radiomics parameters were extracted from the T2WI and ADC texture features of the gray-level co-occurrence matrix were delineated from the region of interest. Radical prostatectomy histopathology was used as a reference standard. A Kruskal–Wallis test was applied first to identify the significant radiomic features between the three groups of Gleason scores (i.e., G1, G2 and G3). Subsequently, the Holm–Bonferroni method was applied to correct and control the probability of false rejections. We compared the probability of correctly predicting significant prostate cancer between the explanatory GLCM radiomic features, PIRADS and PSAD, using the area under the receiver operation characteristic curves. Results: We identified the significant difference in radiomic features between the three groups of Gleason scores. In total, 12 features out of 22 radiomics features correlated with the Gleason groups. Our model demonstrated excellent discriminative ability (C-statistic = 0.901, 95%CI 0.859–0.943). When comparing the probability of correctly predicting significant prostate cancer between explanatory GLCM radiomic features (Sum Variance T2WI, Sum Entropy T2WI, Difference Variance T2WI, Entropy ADC and Difference Variance ADC), PSAD and PIRADS via area under the ROC curve, radiomic features were 35.0% and 34.4% more successful than PIRADS and PSAD, respectively, in correctly predicting significant prostate cancer in our patients (p < 0.001). The Sum Entropy T2WI score had the greatest impact followed by the Sum Variance T2WI. Conclusion: Quantitative GLCM texture analyses of pre-biopsy MRI has the potential to be used as a non-invasive imaging technique to predict clinically significant cancer in men suspected of having prostate cancer.

Fully Biobased Elastomer Composites with Mechanically Robust, Reprocessable, and Biocompatible Properties

It is of importance, though difficult, to integrate reinforcement, functionalization, and recyclability into elastomeric materials. Here we describe a biomass-derived epoxidized natural rubber (ENR) cross-linked with dynamic covalent bonds and reinforced with functional carboxymethyl chitosan (CMCS). The ENR/CMCS composites show enhanced mechanical properties and thermal stability, due to the uniform dispersion of the CMCS fillers and strong interfacial interaction between the ENR matrix and fillers. These cross-linked rubbers can undergo topological network rearrangements through dynamic exchange reactions, endowing the materials with reprocessability and recyclability. Due to the biologically derived and green features of the both polymeric matrix and filler, the fully biobased ENR/CMCS composites also show excellent biocompatibility, which will expand their application in the field of biomaterials.

The Potential Value of Texture Analysis Based on Dynamic Contrast-Enhanced MR Images in the Grading of Breast Phyllode Tumors.

PurposeTo explore the value of texture analysis (TA) based on dynamic contrast-enhanced MR (DCE-MR) images in the differential diagnosis of benign phyllode tumors (BPTs) and borderline/malignant phyllode tumors (BMPTs).MethodsA total of 47 patients with histologically proven phyllode tumors (PTs) from November 2012 to March 2020, including 26 benign BPTs and 21 BMPTs, were enrolled in this retrospective study. The whole-tumor texture features based on DCE-MR images were calculated, and conventional imaging findings were evaluated according to the Breast Imaging Reporting and Data System (BI-RADS). The differences in the texture features and imaging findings between BPTs and BMPTs were compared; the variates with statistical significance were entered into logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of models from image-based analysis, TA, and the combination of these two approaches.ResultsRegarding texture features, three features of the histogram, two features of the gray-level co-occurrence matrix (GLCM), and three features of the run-length matrix (RLM) showed significant differences between the two groups (all p < 0.05). Regarding imaging findings, however, only cystic wall morphology showed significant differences between the two groups (p = 0.014). The areas under the ROC curve (AUCs) of image-based analysis, TA, and the combination of these two approaches were 0.687 (95% CI, 0.518–0.825, p = 0.014), 0.886 (95% CI, 0.760–0.960, p < 0.0001), and 0.894 (95% CI, 0.754–0.970, p < 0.0001), respectively.ConclusionTA based on DCE-MR images has potential in differentiating BPTs and BMPTs.