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Background: Data on the value of magnetic resonance imaging (MRI) in the preoperative evaluation and surgery planning of gynecomastia are limited. The purpose of this study is to reveal MRI features and categories of gynecomastia and compare surgical outcomes following MRI and sonography as well as their diagnostic accuracy. Methods: The area of the gland and the whole breast on the transverse plane via nipple of MRI were measured to calculate the ratio between them. Areola, mass and branch patterns were categorized to represent three different gynecomastia type on MRI. 183 patients were included, with 38 in MRI group and 145 in sonography group. Diagnostic accuracy was assessed by the level of agreement between preoperative imaging findings and intraoperative observations. Surgical data, patients' satisfaction and complications were compared between the two groups. Results: MRI in 75 gynecomastic breasts demonstrated the average ratio of the gland to the whole breast was 10.6%±13.3%. The most common MRI categories were branch patterns (45.3%). The diagnostic concordance rate of MRI was higher than sonography (100% vs. 86.8%, p = 0.001). Among those junior surgeons, the length of surgery was reduced in MRI group (100 min vs. 115 min, p = 0.048). There was no difference in terms of patient's satisfaction and complication rate between MRI and sonography. Conclusion: MRI was superior to sonography in diagnostic accuracy to assess the tissue components of gynecomastia and provided informative guidance especially for junior surgeons. Surgical outcomes were comparable regardless of the use of MRI or sonography for evaluation. Level of Evidence IV: IThis journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266. [ABSTRACT FROM AUTHOR]

Purpose: Screen-detected unilateral non-palpable breast cancer (NPBC) shows favorable prognosis, whereas bilateral breast cancer (BBC), especially synchronous BBC (SBBC) manifests worse survival than unilateral breast cancer (BC). It remains unclear whether screen-detected bilateral NPBC has compromised survival and requires intensified treatment or favorable prognosis and needs de-escalating therapy. Methods: From 2003 to 2017, 1,075 consecutive NPBC patients were retrospectively reviewed. There were 988 patients with unilateral NPBC (UniNPBC), and 87 patients with ipsilateral NPBC + any contralateral BC [(N + AnyContra) PBC], including 32 patients with bilateral NPBC (BiNPBC) and 55 patients with ipsilateral NPBC + contralateral palpable cancer [(N + Contra) PBC]. Median follow-up time was 91 (48–227) months. Clinicopathological characteristics were compared between UniNPBC and BBC, whereas relapse-free survival (RFS) and overall survival (OS) among BBC subgroups. RFS and OS factors of BBC were identified. Results: Compared to UniNPBC, patients with screen-detected bilateral BC had more invasive (85.1%, 74.8%), ER negative (26.4%, 17.1%), PR negative (36.8%, 23.5%), triple-negative (21.6%, 8.5%) BC as well as less breast conserving surgery (17.2%, 32.4%), radiotherapy (13.8%, 32.0%) and endocrine therapy (71.3%, 83.9%). 10 year RFS and OS rates of (N + AnyContra) PBC (72.8%, 81.5%), (N + Contra) PBC (60.6%, 73.9%), and synchronous (N + Contra) PBC (58.1%, 70.1%) were significantly compromised compared to UniNPBC (91.0%, 97.2%). RFS factors of BBC included pN3 (p = 0.048), lymphovascular invasion (p = 0.008) and existence of contralateral palpable interval BC (p = 0.008), while the OS relevant factor was pN3 (p = 0.018). Conclusion: Screen-detected bilateral NPBC including SynBiNPBC and MetaBiNPBC showed good prognosis as UniNPBC so that the therapy of BiNPBC could be de-escalated and optimized according to UniNPBC. Contrarily, screen-detected ipsilateral NPBC with contralateral palpable BC [(N + Contra) PBC] manifested unfavorable survival worse than UniNPBC and synchronous (N + Contra) PBC had the worst survival among all subgroups, implying that these were actually bilateral interval BC and required intensified treatment. [ABSTRACT FROM AUTHOR]

Purpose: A sensitive and specific imaging method to detect metastatic cancer cells in lymph nodes to detect the early-stage breast cancer is still a challenge. The purpose of this study was to investigate a novel breast cancer–targeting and tumour microenvironment ATP-responsive superparamagnetic iron oxide nanoparticles (SPIOs) imaging probe (abbreviated as SPIOs@A-T) that was developed to detect lymph node metastasis through fluorescence molecular imaging (FMI) and magnetic particle imaging (MPI). Methods: The conjugation of the targeted peptide CREKA and SPIOs was via linker sulfo-SMCC, while the dsDNA-Cy5.5 was modified on SPIOs through the conjugation between maleimide group in sulfo-SMCC and sulfydryl group in dsDNA-Cy5.5. SPIOs@A-T was characterised for its imaging properties, targeting ability and toxicity in vitro. Mice with metastatic lymph node (MLN) of breast cancer were established to evaluate the FMI and MPI imaging strategy in vivo. Healthy mice with normal lymph node (NLN) were used as control group. Histological examination and biosafety evaluation were performed for further assessment. Results: After injection with SPIOs@A-T, the obvious high fluorescent intensity and MPI signal were observed in MLN group than those in NLN group. FMI can specifically light up MLN using an ATP-responsive fluorescence design. On the other hand, MPI could complement the limitation of imaging depth from FMI and could detect MLN more sensitively. Besides, the biosafety evaluation results showed SPIOs@A-T had no detectable biological toxicity. Conclusion: SPIOs@A-T imaging probe in combination with FMI and MPI can provide a promising novel method for the precise detection of MLN in vivo. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate the role of early postoperative CT texture analysis in aneurysm progression. Ninety-nine patients who had undergone post-endovascular aneurysm repair (EVAR) infra-renal abdominal aortic aneurysm CT serial scans were enrolled from July 2014 to December 2019. The clinical and traditional imaging features were obtained. Aneurysm texture analysis was performed using three methods—the grey-level co-occurrence matrix (GLCM), the grey-level run length matrix (GLRLM), and the grey-level difference method (GLDM). A multilayer perceptron neural network was applied as a classifier, and receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) analysis were employed to illustrate the classification performance. No difference was found in the morphological and clinical features between the expansion (+) and (−) groups. GLCM yielded the best performance with an accuracy of 85.17% and an AUC of 0.90, followed by GLRLM with an accuracy of 87.23% and an AUC of 0.8615, and GLDM with an accuracy of 86.09% and an AUC of 0.8313. All three texture analyses showed superior predictive ability over clinical risk factors (accuracy: 69.41%; AUC: 0.6649), conventional imaging features (accuracy: 69.02%; AUC: 0.6747), and combined (accuracy: 75.29%; AUC: 0.7249). Early post-EVAR arterial phase-derived aneurysm texture analysis is a better predictor of later aneurysm expansion than clinical factors and traditional imaging evaluation combined. [ABSTRACT FROM AUTHOR]

Hyperspectral Image (HSI) classification is one of the fundamental tasks in the field of remote sensing data analysis. CNN (Convolutional Neural Network) has been proven to be an effective deep learning model, which can extract high-level features directly from the raw data and thereby utilize rich information contained in HSI data. However, labor cost to label enough HIS data for training model is usually expensive, so that it is a strong demand of utilizing limited training data to get a satisfied classification accuracy. In this paper, we put forward a deep cube CNN model - DCNR, which is composed of a cube neighbor HSI pixels strategy, a deep CNN and a random forest classifier. In DCNR model, cubic samples, containing spectral-spatial information, are generated by putting each target pixel and its neighbors together. Then features with high representative ability, extracted by applying a specially designed cube CNN model on each cubic sample, are fed into the random forest classifier for the classification of the target pixel. Results show that DCNR model can achieve classification accuracy of 96.78%, 96.08% and 94.85% on KSC, IP and SA datasets respectively with 20% samples as training set, and 85.03%, 83.45 and 62.17% on KSC, IP and SA datasets respectively with only 1% samples as training set, significantly outperforming random forest and cube CNN models. [ABSTRACT FROM AUTHOR]

Twin-hypersphere support vector machine (THSVM) for binary pattern recognition aims at generating two hyperspheres in the feature space such that each hypersphere contains as many as possible samples in one class and is as far as possible from the other one. THSVM has a fast learning speed since it solves two small sized support vector machine (SVM)-type quadratic programming problems (QPPs). However, it only simply considers the prior class-based structural information in the optimization problems. In this paper, a structural information-based THSVM (STHSVM) classifier for binary classification is presented. This proposed STHSVM focuses on the cluster-based structural information of the corresponding class in each optimization problem, which is vital for designing a good classifier in different real-world problems. In addition, it also leads to a fast learning speed since this STHSVM solves a series of smaller-sized QPPs compared with THSVM. Experimental results demonstrate that STHSVM is superior in generalization performance to other classifiers. [ABSTRACT FROM AUTHOR]

It is necessary to use interval data to define terms or describe extreme behaviors because of the existence of uncertainty in many real-world problems. In this paper, a novel efficient interval twin support vector regression (ITSVR) is proposed to handle such interval data. This ITSVR employs two nonparallel functions to identify the upper and lower sides of the interval output data, respectively, in which the Hausdorff distance is incorporated into the Gaussian kernel as the interval kernel for interval input data. Compared with other support vector regression (SVR)-based interval regression methods, such as the interval support vector interval regression networks (ISVIRN), this ITSVR algorithm is more efficient since only two smaller-sized QPPs are solved, respectively. The experimental results on several artificial datasets and three stock index datasets show the validity of ITSVR. [ABSTRACT FROM AUTHOR]

This paper investigated the influence of heat treatment on the microstructure of Al-Si alloy coatings on γ-TiAl alloy. The coatings were prepared by cold spraying with Al-12Si and Al-20Si alloy powders as the feedstock, and then the as-sprayed coatings were subjected to heat treatment. The microstructure, chemical composition, and phase transformation of the coatings were studied by SEM, XRD, and EPMA. The diffusing behavior of Al and Si during heat treatment was investigated. The results showed that a silicon-aluminizing coating was formed through the inward diffusion of Al/Si elements into the substrate. The obtained kinetics curve of the formation of silicon-aluminizing coating at 580 °C similarly followed parabolic law. [ABSTRACT FROM AUTHOR]

The thermal barrier coatings investigated in this paper included a TiAl bond coat and a yttria partially stabilized zirconia (YSZ) layer. The TiAl bond coat was prepared by deposition of aluminum by cold spray, followed by a heat-treatment. The YSZ layer was prepared by air plasma spray. The isothermal and cyclic oxidation tests were conducted at 900 °C for 1000 h and 500 cycles to test the oxidation resistance of the thermal barrier coatings. The microstructure and composition of the γ-TiAl alloy with and without the thermal barrier coatings after oxidation were investigated. The results showed that a dense TGO layer about 5 μm had grown between the YSZ layer and the TiAl bond coat. The TGO had good adhesion to both the YSZ layer and the bond coat even after the TiAl bond coat entirely degraded into the TiAl phase, which decreased the inward oxygen diffusion. Thus, the thermal barrier coatings improved the oxidation resistance of γ-TiAl alloy effectively. [ABSTRACT FROM AUTHOR]

In this work, finite element simulations were performed to investigate the competition between bonding and rebounding when a Cu particle of 25 μm in diameter impinged Cu substrate. With the help of cohesive zone model, the trend of the minimal average bonding strength to suppress rebounding (MABSSR) was predicted as a function of initial velocity under certain conditions. Result shows, MABSSR has a nonlinear trend versus initial velocity under the conditions considered. If the real trend of MABSSR is similar to that observed, a hypothesis is presented to explain the critical deposition velocity. Finally, defects in the current work are discussed. Analysis shows the defects have no influence on predicting the overall trend of MABSSR in the current work. [ABSTRACT FROM AUTHOR]

Surface treatment of AISI 52100 steel by supersonic fine particles bombarding (SFPB) was studied in this article. The surface topography, morphology of the surface layer, and microhardness distribution of the surface layer have been investigated using a surface profiler system, a scanning electron microscopy (SEM), and a microvickers hardness tester. The microstructure, phase composition, and residual stress distribution of the surface layer in AISI 52100 steel after the SFBP treatment have been characterized by means of x-ray diffraction, SEM, and transmission electron microscopy. The results showed that a nanocrystalline surface (NS) layer was formed on the top surface of the SFBP-treated AISI 52100 steel samples. The NS layer is about 2 μm in thickness with a surface roughness of R = 1.2 μm, R = 6.7 μm, R = 6.0 μm. Phase transitions occurred in the surface of the SFBP-treated samples. Residual compressive stress is obtained at the surface of the SFBP-treated samples. The maximum value of compressive stress appears at the outermost of the surface, and the affection region of the whole surface is about 60 μm in thickness. A hardened surface layer has been fabricated in the AISI 52100 steel. The thickness of the hardened surface layer is about 70 μm. The maximum value of hardness occurs at the depth of 20 μm from the outermost surface. [ABSTRACT FROM AUTHOR]