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Introduction: Histiocytic sarcoma (HS) is a rare hematologic malignancy. HS of the breast is extremely rare, and we present a case of an elderly patient with breast HS. Case presentation: An 81-year-old woman with unremarkable past medical and family histories presented to our hospital with a palpable mass in her right upper breast. She had noticed a mass in her right breast 3 months before her first visit. Physical examination revealed a mass measuring approximately 30 mm in the right upper quadrant of the breast; there were no cervical or axillary lymphadenopathies. Mammography revealed a high-concentration mass with unclear margins in the upper and outer breast. Ultrasound and magnetic resonance imaging (MRI) revealed a 31 × 23-mm nodule with a relatively clear margin and necrotic sign on the T2-intensified image. A mastectomy was performed upon the patient's request, and the surgical specimen revealed a 35-mm hemorrhagic mass. The lesion was estrogen receptor-, progesterone receptor-, and HER2/neu-negative. The Ki-67 labeling index was approximately 30%. The immunohistochemical panel showed immune reactivity for the histiocytic markers CD68, CD163, and CD206 and was immune-negative for B lineage, T lineage, Langerhans cells, and keratins. The diagnosis of HS was based on the morphological and immunophenotypic characteristics of the mass. The patient received no systemic therapy and survived for 50 months without recurrence. Conclusions: Here, we report the case of an elderly patient with rare breast HS. Although the prognosis of HS seems poor, the breast HS was not as poor as expected, since it might have been discovered in the local region before it metastasized. [ABSTRACT FROM AUTHOR]

In this paper, a wideband antenna is proposed for ultra-wideband microwave imaging applications. The antenna is comprised of a tapered slot ground, a rectangular slotted patch and four star-shaped parasitic components. The added slotted patch is shown to be effective in improving the bandwidth and gain. The proposed antenna system provides a realized gain of 6 dBi, an efficiency of around 80% on the radiation bandwidth, and a wide impedance bandwidth (S11 < −10 dB) of 6.3 GHz (from 3.8 to 10.1 GHz). This supports a true wideband operation. Furthermore, the fidelity factor for face-to-face (FtF) direction is 91.6%, and for side by side (SbS) is 91.2%. This proves the excellent directionality and less signal distortion of the designed antenna. These high figures establish the potential use of the proposed antenna for imaging. A heterogeneous breast phantom with dielectric characteristics identical to actual breast tissue with the presence of tumors was constructed for experimental validation. An antenna array of the proposed antenna element was situated over an artificial breast to collect reflected and transmitted waves for tumor characterization. Finally, an imaging algorithm was used to process the retrieved data to recreate the image in order to detect the undesirable tumor object inside the breast phantom. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common type of cancer in women, and early detection is important to significantly reduce its mortality rate. This study introduces a detection and diagnosis system that automatically detects and classifies breast tumors in CT scan images. First, the contours of the chest wall are extracted from computed chest tomography images, and two-dimensional image characteristics and three-dimensional image features, together with the application of active contours without edge and geodesic active contours methods, are used to detect, locate, and circle the tumor. Then, the computer-assisted diagnostic system extracts features, quantifying and classifying benign and malignant breast tumors using a greedy algorithm and a support vector machine. The study used 174 breast tumors for experiment and training and performed cross-validation 10 times (k-fold cross-validation) to evaluate performance of the system. The accuracy, sensitivity, specificity, and positive and negative predictive values of the system were 99.43%, 98.82%, 100%, 100%, and 98.89% respectively. This system supports the rapid extraction and classification of breast tumors as either benign or malignant, helping physicians to improve clinical diagnosis. [ABSTRACT FROM AUTHOR]

Computer vision is one of the significant trends in computer science. It plays as a vital role in many applications, especially in the medical field. Early detection and segmentation of different tumors is a big challenge in the medical world. The proposed framework uses ultrasound images from Kaggle, applying five diverse models to denoise the images, using the best possible noise-free image as input to the U-Net model for segmentation of the tumor, and then using the Convolution Neural Network (CNN) model to classify whether the tumor is benign, malignant, or normal. The main challenge faced by the framework in the segmentation is the speckle noise. It's is a multiplicative and negative issue in breast ultrasound imaging, because of this noise, the image resolution and contrast become reduced, which affects the diagnostic value of this imaging modality. As result, speckle noise reduction is very vital for the segmentation process. The framework uses five models such as Generative Adversarial Denoising Network (DGAN-Net), Denoising U-Shaped Net (D-U-NET), Batch Renormalization U-Net (Br-UNET), Generative Adversarial Network (GAN), and Nonlocal Neutrosophic of Wiener Filtering (NLNWF) for reducing the speckle noise from the breast ultrasound images then choose the best image according to peak signal to noise ratio (PSNR) for each level of speckle-noise. The five used methods have been compared with classical filters such as Bilateral, Frost, Kuan, and Lee and they proved their efficiency according to PSNR in different levels of noise. The five diverse models are achieved PSNR results for speckle noise at level (0.1, 0.25, 0.5, 0.75), (33.354, 29.415, 27.218, 24.115), (31.424, 28.353, 27.246, 24.244), (32.243, 28.42, 27.744, 24.893), (31.234, 28.212, 26.983, 23.234) and (33.013, 29.491, 28.556, 25.011) forDGAN, Br-U-NET, D-U-NET, GAN and NLNWF respectively. According to the value of PSNR and level of speckle noise, the best image passed for segmentation using U-Net and classification usingCNNto detect tumor type. The experiments proved the quality of U-Net and CNN in segmentation and classification respectively, since they achieved 95.11 and 95.13 in segmentation and 95.55 and 95.67 in classification as dice score and accuracy respectively. [ABSTRACT FROM AUTHOR]

Breast tumor segmentation is vital to tumor detection at the early stages. Deep learning methods are typically used in automatic tumor segmentation tasks. However, in existing methods, the difference between pixels is disregarded, and the union network architecture is used to segment all pixels; these methods involve a tradeoff between accuracy and efficiency. A novel, difficulty-aware, prior-guided hierarchical network for the adaptive segmentation of breast tumors is presented herein. A difficulty prior learning module is proposed to learn the pixel’s difficulty prior to guild adaptive segmentation in the proposed network. To achieve a more accurate segmentation of hard pixels, a hard pixel processing unit is presented to learn more discriminative features for hard pixels. Experiments are conducted based on three datasets. The experimental results show that the proposed methods outperform traditional deep learning methods and achieve a balance between accuracy and efficiency. [ABSTRACT FROM AUTHOR]

To observe the characteristics of a new extracorporeal high intensity focused ultrasound transducer, titled Haifu system JCQ-B, and to compare its safety and efficacy for breast ablation with the standard Haifu system JC transducer. Ox liver with pig skin and pork ribs were prepared in a semi-sphere shape, served as in vitro acoustic model. The udders of female goats were used as in vivo acoustic model. Both in vitro and in vivo models were ablated by either JCQ-B or JC transducer. The morphology of biological focal region (BFR), the coagulative necrosis volume, and the temperature increase were observed and compared. The BFR morphology of JCQ-B transducer was circular both in vitro and in vivo, with a length-width ratio close to one. Under the same sonication parameters (sonication power, time and depth in tissue), coagulation necrosis volume caused by JCQ-B transducer was larger than that caused by JC transducer both in vitro and in vivo. The increase in temperature in the near and far acoustic pathways with JCQ-B transducer was significantly lower than that of JC transducer in vitro. After receiving high sonication energy during in vivo experimentation, there were no complications observed after the ablation of JCQ-B transducer, while small skin damage was observed after the ablation of JC transducer. The JCQ-B transducer improved the safety and efficacy of treatment by optimizing BFR morphology and ablation efficiency, which could be applied in the treatment of breast tumor. [ABSTRACT FROM AUTHOR]

BACKGROUND: Antennas for the microwave imaging system are large which results in higher radiation, manufacturing cost, poor radiation characteristics and it will be difficult to locate on breast tissues. OBJECTIVE: We propose a wearable ultra-wide band antenna for use in the diagnosis of breast cancer bio-medical applications. METHODS: The antenna has been fabricated on 1.6 mm FR4 substrate with a dimension of 28 × 14.4 mm 2 and can operate between 2 GHz–12 GHz with S 11 < - 10 dB with best radiation characteristics. The prototype of the proposed antenna was fabricated and practically tested and the results were found to be consistent with the simulated results. The proposed UWB antenna is intended to radiate and receive information covering the entire spectrum from 3 GHz to 13 GHz. For good impendence matching throughout the larger spectrum, the defected ground structure (diamond shape) was exploited. All the dimensions of the proposed design are confirmed by parametric study and optimization. RESULTS: The maximum simulated efficiency was ranging from 80 to 84% in the desired operating frequency. The maximum Specific Absorption Rate of the proposed antenna was 0.98 W/Kg. Therefore, the proposed UWB antenna could be the right structure for breast cancer diagnosis in terms of SAR. The antenna was found to have a substantial radiation efficiency of around 78%–84% in the desired operating bandwidth. The overall realized gain of the proposed UWB antenna was seen ranging from 1.8–4.2 dB which is sufficient for bio-medical applications. CONCLUSION: The breast phantom was modeled for the validation of the performance of the antenna and SAR was analyzed. The value of SAR of the designed antenna was observed at about 0.98 W/Kg, which is suitable for medical applications. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the mortal cancerous for women and an early diagnosis, applying an appropriate treatment and prognosis increases the survival chance of the patients. There are different screening methods and thermal imaging is one of the noninvasive promising diagnosis techniques to detect thermal profile anomalies in breasts. This work includes both simulation and experimental studies for the detection of breast tumors by using thermal images. The first step is the simulation studies based on heat transfer in biological tissues. By using the Bio-Heat transfer theory, temperature differences between the healthy and tumorous tissues are acquired. The second step consists of phantom designs and detection of breast tumor via thermographic imaging in in-vitro. Designing an appropriate phantom is tremendously crucial for the calibration of the thermal imaging system and diagnosis of breast cancer. As a result of the study, it is presented that the detection of temperatures difference especially with asymmetry factor between the tumor and healthy tissue region is feasible. Also, it is shown that the simulation based results are consistent with the experimental as well. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN) based computer-aided breast tumor diagnosis (CABTD) method plays a vital role in the early detection and diagnosis of breast tumors. However, a Brightness mode (B-mode) ultrasound image derives training feature samples that make closer isolation toward the infection part. Hence, it is expensive due to a meta-heuristic search of features occupying the global region of interest (ROI) structures of input images. Thus, it may lead to the high computational complexity of the pre-trained DNN-based CABTD method. This paper proposes a novel ensemble pre-trained DNN-based CABTD method using global- and local-ROI-structures of B-mode ultrasound images. It conveys the additional consideration of a local-ROI-structures for further enhancing the pre-trained DNN-based CABTD method’s breast tumor diagnostic performance without degrading its visual quality. The features are extracted at various depths (18, 50, and 101) from the global and local ROI structures and feed to support vector machine for better classification. From the experimental results, it has been observed that the combined local and global ROI structure of small depth residual network ResNet18 (0.8 in %) has produced significant improvement in pixel ratio as compared to ResNet50 (0.5 in %) and ResNet101 (0.3 in %), respectively. Subsequently, the pre-trained DNN-based CABTD methods have been tested by influencing local and global ROI structures to diagnose two specific breast tumors (Benign and Malignant) and improve the diagnostic accuracy (86%) compared to Dense Net, Alex Net, VGG Net, and Google Net. Moreover, it reduces the computational complexity due to the small depth residual network ResNet18, respectively [ABSTRACT FROM AUTHOR]

• This research aims to address the research gaps in the classification of breast tumors from ultrasound images. • The study proposes a novel stacking ensemble CNN architecture to detect breast tumors. • The use of gaussian dropout layer and a customized alternative pooling scheme have been explored in this study. Breast cancer and breast tumors have been considered to be the most pervasive form of cancer in medical practice. Breast tumors are life-threatening to women, and their early detection could save lives with the proper treatment. Physical methods for detection of Breast Cancer are time-consuming and often prone to a misdiagnosis at classifying tumors. Recent trends in radiological imaging have significantly improved the efficiency and veracity of breast tumor classification. Artificial intelligence techniques could be used as an automated detection and classification system. In this research, we propose a novel configuration of a Stacking Ensemble with custom Convolutional Neural Network architectures to classify breast tumors from ultrasound images into 'Normal', 'Benign', and 'Malignant' categories. After thorough experimentation, our ensemble has performed with an accuracy, f1-score, precision, and recall of 92.15%, 92.21%, 92.26%, 92.17% respectively. The presented ensemble leverages three Stacked Feature Extractors coupled with a characteristic meta-learner to provide an overall balanced classification performance, with better accuracy and lower false positives. The architecture works in association with gaussian dropout layers to improve the computation and an alternative pooling scheme to retain essential features. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to comprehensively evaluate the diagnostic value of quantitative parameters extracted from apparent diffusion coefficient (ADC) maps in distinguishing fibroepithelial tumors using whole-tumor histogram and texture analysis.Materials and Methods: This retrospective study included 66 female patients with single phyllodes tumor (PT) and 29 female patients with single fibroadenoma (FA) who underwent preoperative magnetic resonance imaging. By independently performing whole-tumor histogram and texture analysis based on ADC maps, two radiologists extracted seven histogram parameters and four texture parameters. The extracted parameters were compared using univariate analysis to determine their ability to distinguish FAs from PTs, benign PTs from FAs, as well as benign PTs from borderline and malignant PTs.Results: When FAs and PTs were compared, ADC_skewness values of PTs were significantly lower than those of FAs (p < 0.05), whereas other significant extracted parameter values of PTs were significantly higher than those of FAs (p ≤ 0.001); the area under the curve of significant parameters combined was 0.936. Regarding the differences between FAs and benign PTs, ADC_SD, ADC_95th percentile and ADC_kurtosis of FAs were significantly lower than those of benign PT group, and ADC_skewness was higher than that of benign PT group (all p < 0.05). Furthermore, ADC_SD, ADC_95th percentile and all texture parameters are significantly higher in the borderline and malignant PT group than in FA and benign PT group (p < 0.05). In addition, ADC_kurtosis of malignant PT group was significantly lower than that of borderline PT group (p = 0.045).Conclusion: The extracted whole-tumor histogram and texture features of ADC maps can improve differential diagnosis of breast fibroepithelial tumors and contribute to optimal selection for clinical management of patients with fibroepithelial tumors. [ABSTRACT FROM AUTHOR]

Breast tumors are the major malignancy in females and diagnostic systems using artificial intelligence algorithms for breast imaging have shown promising results. Among many algorithms, a deep convolutional neural network (DCNN) using K-means clustering and a multiclass support vector machine model enhance the precision of categorizing breast tumors from mammogram images. Nonetheless, effective breast tumor identification is still difficult without partitioning the pectoral muscle (PM) boundary from the remaining breast area. Therefore, this article proposes an Ensemble-Net model by ensembling the transfer learning model with different pre-trained CNN structures for partitioning the PM boundary from the remaining breast area in the mammographic scans. The segmentation process has 2 phases. In the initial phase, different region-of-interests are generated that include the object according to the input images. In the secondary phase, the object class is predicted after the areas of bounding boxes are refined and a pixel-range mask is created for the entity. These 2 different phases are associated with the backbone structure which creates the pyramid hierarchy of DCNN to acquire the features from the raw images. Moreover, it employs global average pooling followed by the softmax classification to recognize the normal, benign and malignant cases. Finally, the experimental outcomes demonstrate that the Ensemble-Net achieves 96.72% accuracy than the other classical classifiers. [ABSTRACT FROM AUTHOR]

A rectangular DRA with a Sierpinski fractal defected ground structure has been simulated and experimentally tested for UWB operation to be used in the detection of breast tumor using monostatic radar-based microwave imaging (MRMI). The proposed fractal DRA is designed to cover UWB characteristics (5.6–14.2 GHz) and high peak gain (5.8 dB). Then it is used as a sensor for extracting S-parameters from the breast phantom for tumor detection. In this regard, the fractal DRA is positioned in front of phantom, revolved around it (10°) in elevation and azimuthal plane to collect backscattered signals (with/without tumor). For validation, an artificial phantom of breast consisting of three layers, gelatin (skin), petroleum jelly (fat) and wheat flour (tumor), with electrical properties same as that of human breast is prepared and the S-parameters are recorded at different intervals. The captured reflection parameters processed in different imaging algorithms are: delay and sum and coherent factor (DAS-CF) and delay multiply and sum and coherent factor (DMAS-CF) to reconstruct 2D images of the breast tumor using MATLAB. The simulated specific absorption rate of the proposed DRA on the breast phantom at the resonant frequencies of 6.1 and 13.9 GHz was observed to be 1.02 and 1.13 W/Kg, respectively, which is safe limit for human exposure. [ABSTRACT FROM AUTHOR]

Background: The prognostic factors and optimal choice of treatment for primary neuroendocrine neoplasms of the breast (BNEN) remain to be defined. Methods: Patients diagnosed with BNEN in China were retrospectively reviewed from the literature following the systematic search of China National Knowledge Infrastructure (CNKI), Chinese biomedical literature service system (sinomed), wanfang medical network, and Pubmed database. The clinical characteristics and different treatment modalities of patients with BNEN were evaluated. Results: A total of 209 cases with BNEN were enrolled. There were 204 female and 5 male patients. The median age was 51 years old (range, 17–82). Out of 209 patients with BNEN, 208 (99.5%) patients were treated with surgery (SG), 44 patients (21.1%) had received radiotherapy (RT), 173 patients (82.8%) experienced chemotherapy (CT). A total of 158 patients with hormone receptor (HR) positive (87.8%, 158/180) were treated with endocrine treatment (ET). The median follow-up time was 52.4 months (range, 6–144). The 3-year overall survival (OS) rate and 3-year disease-free survival (DFS) rate for the whole group were 93.7% and 85.3%, respectively. In univariate analyses, Ki67 expression ≥20%, HR negative, neuroendocrine carcinomas (NECs) were associated with decreased OS and DFS (P 0.05). Conclusions: The best selection of patients to get the most benefit from different treatment modalities warrant further exploration. The clinicopathological parameters including gender, HR expression, ki67 expression, pathologic type, stage, tumor size, and lymph node status may serve as both indicators of diagnosis and prognosis, and guide treatment decisions for BNEN.

As an emerging cancer therapy with high selectivity, minimally invasive and low side effects, photodynamic therapy (PDT) has become the focus of current researches. The breast cancer is a malignant tumor with the highest incidence in women. Thus, it is valuable for improving its treatment efficiency to construct efficient photosensitizer. To this end, we engineered a high stable near-infrared photosensitizer NPA-NH 2 based on "step by step" strategy. By modifying the optically tunable amino group of NPA-NH 2 , a peroxynitrite (ONOO-) activated NPA-ONOO - theranostic agent with a new trifluoromethylsulfonamide recognization site was designed and synthesized. It possessed a good singlet oxygen-generating ability after ONOO- activation under 660 nm light, and a high sensitivity (limit of detection: 0.038 μM) and selectivity respond toward ONOO- in buffer solution. The theranostic agent was able to specially monitor ONOO- in tumor cells. Furthermore, we have succeeded in applying the NPA-ONOO - for efficient and precise photodynamic therapy of breast cancer cells and tumors in vivo. This work provides a new way for the design of highly stable photosensitizers and a reliable tool for effective treatment of breast tumors. [Display omitted] • Engineering a high stable theranostic agent with a new trifluoromethylsulfonamide recognization site. • The agent can response ONOO- with high sensitivity and selectivity in vitro and in vivo. • The agent showed a significant killing effect on breast cancer cells under 660 nm light. • Efficient photodynamic therapy of breast tumors in vivo was achieved by NPA-ONOO - . [ABSTRACT FROM AUTHOR]

The therapeutic efficacy of tea polyphenols (TP) has been demonstrated in various tumor types, and research has indicated its potential role in modulating the intestinal microbiota. However, the impact of it on tumor microbiota remains uncertain. The research aimed to investigate how TP treatment affects microorganisms in breast tumors (4T1) and explore their potential influence on the TP's therapeutic efficacy. The TP significantly inhibited the growth of 4T1 tumors, and DOX-TP significantly enhanced the efficacy of TP against the tumors, whereas DOX alone did not show similar effects. The results of 16 S rDNA sequences indicated that Herbaspirillum, Herminiimonas, Stenotrophomonas exhibited a remarkable increase (regulation rate≥99.72% and the abundance up-regulation by 7.91%, 5.17% and 5.77%, respectively), transitioning from absence to presence in the TP group to the DOX-TP group. Conversely, Halomonas, ZOR0006, Hydrogenophilus, Rhodococcus, Vibrio were significantly decreased (regulation rate≥99.91% and the abundance down-regulation by 3.16%, 2.78%, 3.15%, 12.92% and 16.03%, respectively) after DOX-TP treatment, resulting in a shift from presence to absence. Compared to the TP group, DOX-TP group demonstrated enhanced infiltration of CD4+ T and CD8+ T cells, while exhibiting significantly elevated expression levels of IL-2, IFN-α, IFN-γ and TNF-α. Additionally, Rhodococcus and Halomonas were observed to be negatively correlated with IFN-α expression. The co-administration of TP and antibiotics further modified the tumor microbiota composition, influencing the tumor immune microenvironment. The study demonstrated that DOX-TP can enhance TP's anticancer effect by modulating the tumor microbiota. • The findings highlight four key observations. • TP can alter the tumor microbiota. • The regulatory effect of DOX-TP on tumor microbiota was superior to that of the TP group. • The co-administration of antibiotics with TP enhanced the anti-tumor effect of TP. • The combination of antibiotics and TP treatment optimized the tumor microenvironment. [ABSTRACT FROM AUTHOR]

Background: Muscle wasting is a common phenomenon in oncology and seems to be attenuated by exercise training. The aim of this study is to determine the degree of aggressiveness of cancer-induced muscle wasting in two different phenotypic muscles. It will also determine whether exercise training can attenuate this muscle dysfunction. Methods: Fifty Sprague Dawley rats were randomly assigned to four experimental groups: two breast cancer model groups (sedentary and exercise) and two control groups (sedentary and exercise). Breast cancer was induced by 1-methyl-1-nitrosoureia (MNU). After 35 weeks of endurance training, animals were sacrificed, and gastrocnemius and soleus muscles harvested for morphometric analysis. Results: In sedentary tumor-bearing animals, a significant reduction in cross-sectional area was found in both muscles (p < 0.05). Interstitial fibrosis was significantly higher in the gastrocnemius muscle of the sedentary tumor-bearing animals (p < 0.05), but not in the soleus muscle. In the gastrocnemius of sedentary tumor-bearing animals, a shift from large to small fibers was observed. This cancer-related muscle dysfunction was prevented by long-term exercise training. Conclusions: In sedentary animals with tumors, the gastrocnemius muscle showed a very pronounced reduction in cross-sectional area and a marked degree of interstitial fibrosis. There was no difference in collagen deposition between tumor groups, and the soleus muscle showed a less pronounced but significant reduction in cross-sectional area. These contrasting results confirm that cancer-induced muscle wasting can affect specific types of fibers and specific muscles, namely fast glycolytic muscles, and that exercise training can be used to improve it.

Breast tumor is one of the most prominent indicators for the diagnosis of breast cancer. The precise segmentation of tumors is crucial for enhancing the accuracy of breast cancer detection. A physician’s assessment of the MRI scan is time-consuming and require a lot of human effort and expertise. Furthermore, traditional medical segmentation approaches frequently need prior information or manual feature extraction, resulting in a subjective diagnosis. Therefore, the development of an automated image segmentation approach is essential for clinical applications. This work presents BTS-GAN, an automatic breast tumor segmentation process using conditional GAN (cGAN) in Magnetic Resonance Imaging (MRI) scans. First, we used an encoder-decoder deep network with skip connections between encoder and decoder for the generator to increase the localization efficiency. Second, we utilized a parallel dilated convolution (PDC) module to retain the features of various sizes of masses and to effectively extract information about the masses’ edges and interior texture. Third, an extra classification-related constraint is included to the loss function of the cGAN for mitigating the hard-to-converge challenge in image-to-image (I2I) translation tasks based on classification. The generator side of our proposed model learns to detect the tumor and construct a binary mask, while the discriminator learns to distinguish between ground truth and synthetic masks, driving the generator to produce masks as genuine as possible. The experimental results demonstrate that our BTS-GAN is more efficient and reliable for breast tumor segmentation and outperform other segmentation techniques in terms of the IoU and Dice coefficient on the publicly available RIDER breast cancer MRI dataset. Our proposed model achieved an average IoU and Dice scores of 77% and 85% respectively.

Purpose: The purpose of the study was to build an AI model with selected preoperative clinical features to further improve the accuracy of the assessment of benign and malignant breast nodules. Methods: Patients who underwent ultrasound, strain elastography, and SDetect before ultrasound-guided biopsy or surgical excision were enrolled. The diagnosis model was built using a logistic regression model. The diagnostic performances of different models were evaluated and compared. Results: A total of 179 lesions (101 benign and 78 malignant) were included. The whole dataset consisted of a training set (145 patients) and an independent test set (34 patients). The AI models constructed based on clinical features, ultrasound features, and strain elastography to predict and classify benign and malignant breast nodules had ROC AUCs of 0.87, 0.81, and 0.79 in the test set. The AUCs of the sonographer and S-Detect were 0.75 and 0.82, respectively, in the test set. The AUC of the combined AI model with the best performance was 0.89 in the test set. The combined AImodel showed a better specificity of 0.92 than the othermodels. The sonographer's assessment showed better sensitivity (0.97 in the test set). Conclusion: The combined AI model could improve the preoperative identification of benign and malignant breast masses and may reduce unnecessary breast biopsies. [ABSTRACT FROM AUTHOR]

Background: Micro RNA of Marsupenaeus japonicas has been known to promote apoptosis of tumor cells. However, the detailed mechanisms are not well understood. Results: Using tomographic microscope, which can detect the internal structure of cells, we observed breast tumor cells following treatment of the miRNA. Intriguingly, we found that mitochondria migrate to an adjacent tumor cells through a tunneling nanotube. To recapitulate this process, we engineered a microfluidic device through which mitochondria were transferred. We show that this mitochondrial transfer process released endonuclease G (Endo G) into tumor cells, which we referred to herein as unsealed mitochondria. Importantly, Endo G depleted mitochondria alone did not have tumoricidal effects. Moreover, unsealed mitochondria had synergistic apoptotic effects with subtoxic dose of doxorubicin thereby mitigating cardiotoxicity. Conclusions: Together, we show that the mitochondrial transfer through microfluidics can provide potential novel strategies towards tumor cell death. [ABSTRACT FROM AUTHOR]

Detailed insight into the radiation-induced changes in tumor microvasculature is crucial to maximize the efficacy of radiotherapy against breast cancer. Recent advances in imaging have enabled precise targeting of solid lesions. However, intratumoral heterogeneity makes treatment planning and monitoring more challenging. Conventional imaging cannot provide high-resolution observation and longitudinal monitoring of large-scale microvascular in response to radiotherapy directly in deep tissues. Herein, we report on an emerging noninvasive imaging assessment method of morphological and functional tumor microvasculature responses with high spatio-temporal resolution by means of optoacoustic imaging (OAI). In vivo imaging of 4T1 breast tumor response to a conventional fractionated radiotherapy at varying dose (14 × 2 Gy and 3 × 8 Gy) has been performed after 2 weeks following treatment. Remarkably, optoacoustic images can generate richful contrast for the tumor microvascular architecture. Besides, the functional status of tumor microvasculature and tumor oxygenation levels were further estimated using OAI. The results revealed the differential (sizedependent) nature of vascular responses to radiation treatments at varying doses. The vessels exhibited an decrease in their density accompanied by a decline in the number of vascular segments following irradiation, compared to the control group. The measurements further revealed an increase of tumor oxygenation levels for 14 × 2 Gy and 3 × 8 Gy irradiations. Our results suggest that OAI could be used to assess the response to radiotherapy based on changes in the functional andmorphological status of tumormicrovasculature, which are closely linked to the intratumor microenvironment. OAI assessment of the tumor microenvironment such as oxygenation status has the potential to be applied to precise radiotherapy strategy. [ABSTRACT FROM AUTHOR]

Background: The cornerstone of management of phyllodes tumor is surgery. No standard of care exists regarding adjuvant therapy; however, local recurrence is a predominant pattern of failure. The aims of this study were to evaluate the clinical characteristics and treatment patterns of phyllodes tumor and to compare local recurrence and disease-free survival rates of patients with borderline and malignant phyllodes tumor treated with or without adjuvant radiotherapy. Materials and Methods: We analyzed the demographic data, treatment details, and recurrence patterns of all patients with nonmetastatic phyllodes tumors of the breast (n = 34) treated with a multimodality approach who presented to our institute from January 2015 to December 2020. Results: The median age at presentation was 41.2 years, and the median tumor size was 12.5 cm. All patients underwent definitive surgical procedures in the form of wide local excision or mastectomy. No recurrence was noted in the patients with a benign histology. After a median follow-up period of 38 months, the local recurrence rate was 27% for the patients with borderline and malignant histology treated with adjuvant radiotherapy (n = 11) versus 47% for those (n = 17) who did not receive adjuvant radiotherapy. The 3-year local recurrence-free survival rate was 72% in the adjuvant radiotherapy group, versus 51% in the surgery only group. Conclusion: The results of the current study confirm the excellent prognosis of patients with benign phyllodes tumor undergoing surgery alone. Local recurrence was the predominant mode of failure in the patients with borderline and malignant histology. Locoregional control was improved with the addition of postoperative radiotherapy in the patients with borderline and malignant histology irrespective of margin status. [ABSTRACT FROM AUTHOR]

Simple Summary: ROS and JAK/Stat3 cooperatively upregulate the expression of HIF-1α in bone marrow-derived mesenchymal stem cells under normoxic conditions in response to breast tumor cells. The upregulation of HIF-1α contributes in part to the increase in VEGF expression in the bone marrow-derived mesenchymal stem cells. Bone marrow-derived mesenchymal stem cells improve the angiogenic sprouting capacity of mature endothelial cells in a VEGF-dependent manner. Breast tumor cells recruit bone marrow-derived mesenchymal stem cells (BM-MSCs) and alter their cellular characteristics to establish a tumor microenvironment. BM-MSCs enhance tumor angiogenesis through various mechanisms. We investigated the mechanisms by which BM-MSCs promote angiogenesis in response to breast tumor. Conditioned media from MDA-MB-231 (MDA CM) and MCF7 (MCF7 CM) breast tumor cells were used to mimic breast tumor conditions. An in vitro spheroid sprouting assay using human umbilical vein endothelial cells (HUVECs) was conducted to assess the angiogenesis-stimulating potential of BM-MSCs in response to breast tumors. The ROS inhibitor N-acetylcysteine (NAC) and JAK inhibitor ruxolitinib attenuated increased HIF-1α in BM-MSCs in response to MDA CM and MCF7 CM. HIF-1α knockdown or HIF-1β only partially downregulated VEGF expression and, therefore, the sprouting capacity of HUVECs in response to conditioned media from BM-MSCs treated with MDA CM or MCF7 CM. Inactivation of the VEGF receptor using sorafenib completely inhibited the HUVECs' sprouting. Our results suggest that increased HIF-1α expression under normoxia in BM-MSCs in response to breast tumor cells is mediated by ROS and JAK/Stat3, and that both HIF-1α-dependent and -independent mechanisms increase VEGF expression in BM-MSCs to promote the angiogenic sprouting capacity of endothelial cells in a VEGF-dependent manner. [ABSTRACT FROM AUTHOR]

Breast cancer treatment failure is related to low response rates, high costs, and long-term toxicities. Thus, it is necessary to find less toxic, cheaper, and more effective treatments. In situ administration ensures drug delivery to tumor cells and decreases systemic toxic effects. The androstene-3β, 17α-diol (α-AED) reduces breast tumor cell proliferation and is an ideal candidate to treat mammary tumors. This study aims to identify the in vitro and in vivo effects of α-AED on a triple-negative mammary tumor model. An in vitro biphasic steroid effect was observed in mouse and human mammary tumor cells treated with α-AED. In this sense, cells treated with higher doses (100 and 200 μM) showed an antiproliferative effect. The α-AED administrated intratumorally reduced average tumor weight and increased the percentage of natural killer cells (NK), plasmatic, and plasmablast cells in mice tumors. Of note, VEGF levels in all α-AED-treated tumors was lower than in the control and vehicle groups. The tumor in situ increased response was reflected systemically by higher anti-4T1 IgG concentration in serum from α-AED-treated mice, but no other associated systemic changes were detected. The reduction in tumor size for the local injection of α-AED is associated with the anti-proliferative effect of this steroid, and the lower local levels of VEGF may be related to the imperceptible macroscopic metastasis in α-AED-treated mice. The above suggests that α-AED may be used in clinical studies to prove its efficacy as an alternative breast tumor treatment or in conjunction with already established therapies. [ABSTRACT FROM AUTHOR]

Automatic segmentation of breast cancer lesions in dynamic contrast-enhanced magnetic resonance imaging is challenged by low accuracy of delineation of the infiltration area, variable structure and shapes, large intensity heterogeneity changes, and low boundary contrast. This study constructed a two-stage breast cancer image segmentation framework and proposes a novel breast cancer lesion segmentation model (TR-IMUnet). The benchmark U-Net network model enables a rough delineation of the breast area in the acquired images and eliminates the influence of unrelated tissues (chest muscle, fat, and heart) on breast tumor segmentation. Based on the extracted results of the region of interest, the rectified linear unit (ReLU) function of the encoding–decoding structure in the model was replaced by an improved ReLU function to reserve and adjust the data dynamically according to input information. The segmentation accuracy of breast cancer lesions was improved by embedding a multi-scale fusion block and a transformer module in the coding path of the model, thereby obtaining multi-scale and global attention information. The experimental results showed that the breast tumor segmentation indexes Dice coefficient (Dice), Intersection over Union (IoU), Sensitivity (SEN), and Positive Predictive Value (PPV) increased by 4.27, 5.21, 3.37, and 3.68%, respectively, relative to the U-Net reference model. The proposed model improves the segmentation results of breast cancer lesions and reduces small area mis-segmentation and calcification segmentation. [ABSTRACT FROM AUTHOR]

Purpose: Breast cancer is the most commonly occurring cancer worldwide. The ultrasound reflectivity imaging technique can be used to obtain breast ultrasound (BUS) images, which can be used to classify benign and malignant tumors. However, the classification is subjective and dependent on the experience and skill of operators and doctors. The automatic classification method can assist doctors and improve the objectivity, but current convolution neural network (CNN) is not good at learning global features and vision transformer (ViT) is not good at extraction local features. In this study, we proposed a visual geometry group attention ViT (VGGA‐ViT) network to overcome their disadvantages. Methods: In the proposed method, we used a CNN module to extract the local features and employed a ViT module to learn the global relationship among different regions and enhance the relevant local features. The CNN module was named the VGGA module. It was composed of a VGG backbone, a feature extraction fully connected layer, and a squeeze‐and‐excitation block. Both the VGG backbone and the ViT module were pretrained on the ImageNet dataset and retrained using BUS samples in this study. Two BUS datasets were employed for validation. Results: Cross‐validation was conducted on two BUS datasets. For the Dataset A, the proposed VGGA‐ViT network achieved high accuracy (88.71±$\ \pm \ $1.55%), recall (90.73±$\ \pm \ $1.57%), specificity (85.58±$\ \pm \ $3.35%), precision (90.77±$\ \pm \ $1.98%), F1 score (90.73±$\ \pm \ $1.24%), and Matthews correlation coefficient (MCC) (76.34±7$\ \pm \ 7$3.29%), which were better than those of all compared previous networks in this study. The Dataset B was used as a separate test set, the test results showed that the VGGA‐ViT had highest accuracy (81.72±$\ \pm \ $2.99%), recall (64.45±$\ \pm \ $2.96%), specificity (90.28±$\ \pm \ $3.51%), precision (77.08±$\ \pm \ $7.21%), F1 score (70.11±$\ \pm \ $4.25%), and MCC (57.64±$\ \pm \ $6.88%). Conclusions: In this study, we proposed the VGGA‐ViT for the BUS classification, which was good at learning both local and global features. The proposed network achieved higher accuracy than the compared previous methods. [ABSTRACT FROM AUTHOR]

Objectives: To explore added value of diffusion-weighted imaging (DWI) as an adjunct to Kaiser score (KS) for differentiation of benign from malignant lesions on breast magnetic resonance imaging (MRI). Methods: Two hundred forty-six patients with 273 lesions (155 malignancies) were included in this retrospective study from January 2015 to December 2019. All lesions were proved by pathology. Two radiologists blind to pathological results evaluated lesions according to KS. Lesions with score > 4 were considered malignant. Four thresholds of ADC values −1.3 × 10−3mm2/s, 1.4 × 10−3mm2/s, 1.53 × 10−3mm2/s, and 1.6 × 10−3mm2/s were used to distinguish benign from malignant lesions. For combined diagnosis, a lesion with KS > 4 and ADC values below the preset cutoffs was considered as malignant; otherwise, it was benign. Sensitivity, specificity, and area under the curve (AUC) were compared between KS, DWI, and combined diagnosis. Results: The AUC of KS was significantly higher than that of DWI alone (0.941 vs 0.901, p = 0.04). The sensitivity of KS (96.8%) and DWI (97.4 – 99.4%) was comparable (p > 0.05) while the specificity of KS (83.9%) was significantly higher than that of DWI (19.5–56.8%) (p < 0.05). Adding DWI as an adjunct to KS resulted in a 0–2.5% increase of specificity and a 0.1–1.3% decrease of sensitivity; however, the difference did not reach statistical significance (p > 0.05). Conclusion: KS showed higher diagnostic performance than DWI alone for discrimination of breast benign and malignant lesions. DWI showed no additional value to KS for characterizing breast lesions. Key Points: • KS showed higher diagnostic performance than DWI alone for differentiation of benign from breast malignant lesions. • DWI alone showed a high sensitivity but a low specificity for characterizing breast lesions. • Diagnostic performance did not improve using DWI as an adjunct to KS. [ABSTRACT FROM AUTHOR]

Objective: Elastography images tissue mechanical responses and infers the underlying properties to aid diagnosis and treatment response monitoring. The estimation of absolute or relative tumor properties may vary with dimensions even when the mechanical properties remain constant. Harmonic motion imaging (HMI) uses amplitude-modulated (AM) focused ultrasound to interrogate the targeted tissue's viscoelastic properties. In this study, effects of AM frequencies on HMI were investigated in terms of inclusion relative stiffness and size estimation., Methods: AM frequencies from 200 to 600 Hz in steps of 100 Hz were considered using a 5.3-kPa phantom with cylindrical inclusions (Young's modulus: 22, 31, 44, 56 kPa, and diameter: 4.8, 8.1, 13.6, 19.8 mm) to optimize the performance of HMI in characterizing tumors with the same mechanical properties and of different dimensions., Results: Consistent displacement ratios (DRs) (17.5% variation) of the inclusion to background were obtained with 200-Hz AM for breast-tumor-mimicking inclusions albeit a suboptimal inclusion size estimation obtained. 400-Hz was otherwise used for small and low-contrast inclusions (4.8 mm, 22 or 31 kPa). A linear relationship (R 2 = 0.9043) was found between the inverse DR at these frequencies and the Young's modulus ratio. 400 Hz obtained the most accurate inclusion size estimation with an overall estimation error on the lateral dimension of 0.5 mm. In vivo imaging of breast cancer patients (n = 5) was performed at 200 or 400 Hz., Conclusion: The results presented herein indicate that the HMI AM frequency could be optimized adaptively in cases of different applications, i.e., at 200 or 400 Hz, depending on whether aimed for consistent DR measurement for tumor response assessment or tumor margin delineation for surgical planning. HMI may thus be capable of predicting the pathologic endpoint of tumors in response to neoadjuvant chemotherapy (NACT) as early as 3 weeks into treatment., Competing Interests: Conflict of interest The authors declare no conflicts of interest., (Copyright © 2024. Published by Elsevier Inc.)

The majority of breast malignancies are either ductal or lobular tumors. A rare benign neoplasm of adnexal origin, cylindroma of the breast, was first described 23 years ago, and 21 cases have been documented in the literature since then. We report a case of a breast cylindroma on a 62-year-old woman who presented with a nodule first detected by a national mammographic screening program. We review the radiological and histological characteristics, diagnosis, and clinical course of this entity and discuss the previous cases in the literature. In addition, we emphasize on the challenges of differential diagnosis of this rare form of breast tumor from the solid variant of adenoid cystic carcinomas and the management of similar conditions., Competing Interests: Human subjects: All authors have confirmed that this study did not involve human participants or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Papaoikonomou et al.)

Core needle biopsy is a common diagnostic procedure in breast cancer patients, but it can occasionally lead to serious complications. We report a rare case of pseudoaneurysm formation following a core needle biopsy in a 54-year-old female patient diagnosed with breast cancer. Despite the routine nature of the procedure, the patient developed a palpable mass at the biopsy site, which prompted further diagnostic imaging and interventions. The pseudoaneurysm was effectively treated using a percutaneous approach with ultrasound-guided thrombin injection, demonstrating a minimally invasive solution that promptly addressed the complication without the need for surgical intervention. This case highlights the critical importance of detecting complications early in the biopsy process, as they have significant implications for disease staging and treatment initiation. It also underscores the importance of being prepared for immediate intervention in case of biopsy-related complications like pseudoaneurysms, to prevent severe consequences., (© 2024 The Authors. Published by Elsevier Inc. on behalf of University of Washington.)

One of the malignant tumors in women that has involved both developed and developing countries is breast cancer. Similar to other types of tumors, breast cancer cells demonstrate high metastatic nature. Besides, breast tumor cells have ability of developing drug resistance. EMT is the related mechanism to cancer metastasis and focus of current manuscript is highlighting function of EMT in breast tumor malignancy and drug resistance. Breast tumor cells increase their migration by EMT induction During EMT, N-cadherin and vimentin levels increase, and E-cadherin levels decrease to mediate EMT-induced breast tumor invasion. Different kinds of anti-cancer agents such as tamoxifen, cisplatin and paclitaxel that EMT induction mediates chemoresistance feature of breast tumor cells. Furthermore, EMT induction correlates with radio-resistance in breast tumor. Clinical aspect is reversing EMT in preventing chemotherapy or radiotherapy failure in breast cancer patients and improving their survival time. The anti-tumor agents that suppress EMT can be used for decreasing breast cancer invasion and increasing chemosensitivity of tumor cells. Furthermore, lncRNAs, miRNAs and other factors can modulate EMT in breast tumor progression that are discussed here.

Abstract Background Ductal carcinoma in situ (DCIS) is an established precursor to invasive ductal carcinoma (IDC) and its coexistence with IDC appear to favor reduced biological aggressiveness. Its prognostic implication and ability to affect clinical outcome has been understudied in Asia. This study aims to explore if concomitant DCIS affects the clinical behavior and outcomes among Asians. Aim Stages I to III breast cancer patients with histological proven IDC, diagnosed and treated in a single institution from June 1, 2004 to June 30, 2014 were included in this study. Statistical analyses were conducted using Χ2 test, independent t test, multivariate logistic regression and Kaplan–Meier test. Methods and Results A total of 818 patients were identified, including 224 and 594 patients with isolated IDC (No‐DCIS) and IDC with coexisting DCIS (IDC‐DCIS) respectively. Patients with IDC‐DCIS were found to have smaller tumors (median: 22 mm, p ≤ .01), estrogen receptor positivity (p = .001), progesterone receptor positivity (p

Human colostrum and milk contain diverse cells and soluble components that have the potential to act against tumors. In breast cancer, macrophages play a significant role in immune infiltration and contribute to the progression and spread of tumors. However, studies suggest that these cells can be reprogrammed to act as an antitumor immune response. This study aimed to evaluate the levels of melatonin and its receptors, MT1 (melatonin receptor 1) and MT2 (melatonin receptor 2), in colostrum and assess the differentiation and polarization of the colostrum macrophages modulated by melatonin in the presence of breast tumor cells. Colostrum samples were collected from 116 mothers and tested for their melatonin and receptor levels. The colostrum cells were treated with or without melatonin and then cultured for 24 h in the presence or absence of breast tumor cells. The results showed that melatonin treatment increased the expression of MT1 and MT2 in the colostrum cells. Furthermore, melatonin treatment increased the percentage of M1 macrophages and decreased the percentage of M2 macrophages. When the colostrum macrophages were cocultured with breast tumor cells, melatonin reduced the percentage of both macrophage phenotypes and the cytokines tumor necrosis factor-alpha (TNF-α) and interleukin 8 (IL-8). These data suggest that melatonin can regulate the inflammatory process via M1 macrophages in the tumor microenvironment and, simultaneously, the progression of M2 macrophages that favor tumorigenesis.

Background: The use of archived formalin-fixed paraffin-embedded (FFPE) tumor tissues has become a common practice in clinical and epidemiologic genetic research. Simultaneous extraction of DNA and RNA from FFPE tissues is appealing but can be practically challenging. Here we report our results and lessons learned from processing FFPE breast tumor tissues for a large epidemiologic study. Methods: Qiagen AllPrep DNA/RNA FFPE kit was adapted for dual extraction using tissue punches or sections from breast tumor tissues. The yield was quantified using Qubit and fragmentation analysis by Agilent Bioanalyzer. A subset of the DNA samples were used for genome-wide DNA methylation assays and RNA samples for sequencing. The QC metrices and performance of the assays were analyzed with pre-analytical variables. Results: A total of 1859 FFPE breast tumor tissues were processed. We found it critical to adjust proteinase K digestion time based on tissue volume to achieve balanced yields of DNA and RNA. Tissue punches taken from tumor-enriched regions provided the most reliable output. A median of 1475 ng DNA and 1786 ng RNA per sample was generated. The median DNA integrity number (DIN) was 3.8 and median DV200 for RNA was 33.2. Of 1294 DNA samples used in DNA methylation assays, 97% passed quality check by qPCR and 92% generated data deemed high quality. Of the 130 RNA samples with DV200 ≥ 20% used in RNA-sequencing, all but 5 generated usable transcriptomic data with a mapping rate ≥ 60%. Conclusions: Dual DNA/RNA purification using Qiagen AllPrep FFPE extraction protocol is feasible for clinical and epidemiologic studies. We recommend tissue punches as a reliable source material and fine tuning of proteinase K digestion time based on tissue volume. Impact: Our protocol and recommendations may be adapted by future studies for successful extraction of archived tumor tissues. [ABSTRACT FROM AUTHOR]

Background: Selective activation of Delta-like 1 (DLL1)-Notch signaling is a new approach to activate CD8+ T cell and suppress tumor growth, while the efficacy remains modest. Lentinan (LNT) is a clinically used immunomodulation agent. Thus, we hypothesized that LNT could improve the efficacy of DLL1.Methods: The effects of LNT combined with DLL1 on tumor growth were evaluated by growth curve and tumor weight in EO771 breast and LAP0297 lung tumor models. The impacts on immune cells and gene expression in tumor tissues were determined by flow cytometry, qPCR. Neutrophil depletion was used to investigate the mechanism of the combination therapy on tumor growth. The data sets were compared using unpaired student's t-test or ordinary one-way ANOVA.Results:  LNT treatments additively improved the antitumor effects of DLL1 in EO771 breast tumor growth. Remarkably, LNT treatments synergistically enhanced the suppression of DLL1 on LAP0297 lung tumor growth, resulting in tumor regression. Mechanically, the combination of LNT and DLL1 interventions not only promoted the accumulation and activation of CD8+ T cells, but also increased intratumoral CD45+CD11b+Ly6G+ neutrophils. Reduced neutrophils by anti-Gr1 antibody administrations reversed the improved antitumor effects by LNT treatments in LAP0297 lung tumor. These results suggest that LNT treatments improve the inhibition of DLL1 on tumor growth via neutrophils.Conclusions: Our findings indicates that LNT and DLL1 may induce synergistical antitumor immunity via simultaneous modulating lymphoid and myeloid cell populations regardless of the type of tumor, providing a potential new strategy to potentiate cancer immunotherapy. [ABSTRACT FROM AUTHOR]

Granular cell tumor is an infrequent, predominantly benign tumor originating from Schwann cells. Granular cell tumor of the breast (GCTB) can simulate breast malignant carcinoma on the clinical assessment. We herein present a rare case of GCTB which recurred in the contralateral breast. We believe the contrast-enhanced ultrasound (CEUS) findings of GCTB have never been described. The high similarity of breast malignant carcinoma makes its differential diagnosis difficult on the clinical and radiological features. In this report, we present the CEUS findings from a rare case of GCTB, explore the possible value of CEUS in differential diagnosis between benign breast lesions and malignant ones, and briefly review the literature. [ABSTRACT FROM AUTHOR]

The present study aimed to explore the involved lncRNA-miRNA-mRNA network in the cell cycle and proliferation after conventional treatments in Luminal A breast cancer patients.The candidate miRNAs (miRs), lncRNAs, and mRNAs were first taken from the Gene Expression Omnibus and TCGA databases. The lncRNA–miR–mRNA network was then constructed using the high-throughput sequencing data. The expression levels of selected targets were measured in the breast cancer and healthy samples by the Real-Time PCR technique and compared with the clinical outcomes by the Kaplan-Meier method.Our analysis revealed a group of differentially expressed 3 lncRNAs, 9 miRs, and 14 mRNAs in breast cancer patients. A significant expression decrease of the selected tumor suppressor lncRNAs, miRs, and genes and a substantial expression increase of the selected onco-lncRNAs, oncomiRs, and oncogenes were obtained in the patients compared to the healthy group. The plasma levels of the lncRNAs, miRs, and mRNAs were more significant after the operation, chemotherapy, and radiotherapy than the pre-treatment. The Kaplan-Meier analysis indicated that the patients with a high expression of miR-21, miR-20b, IGF1R, and E2F2 and a low expression of miR-125a, PDCD4, and PTEN had exhibited a shorter overall survival rate.Our results suggested that the underlying mechanisms of the lncRNA, miRs, and mRNAs and relevant signaling pathways may be considered predictive and therapeutic targets for breast cancer. [ABSTRACT FROM AUTHOR]

Breast cancer as the most common cancer in women has become the leading cause of cancer death for women. Although many inflammatory factors increase the risk of breast cancer, there are very few studies on the mechanisms by which inflammation affects the initiation and progression of breast cancer. Here, we profiled and compared the transcriptome of normal tissues, inflammatory breast tissues, benign breast tumors, and malignant breast tumors. To find key regulatory factors, a protein interaction network between characteristic modules in inflammatory lesions and ER-negative (ER−) breast cancer was constructed and inflammation-cancer interface genes were identified. We found that the transcriptional profile of inflammatory breast tissues was similar with ER− malignant tumors, featured with low ER expression levels and similar immune signaling pathway activation. Through comprehensive protein network analysis, we identified the interface genes and chemokine signaling pathway that have the potential to promote inflammatory cancer transformation. These interface genes could be used as a risk factor to provide a certain basis for the clinical early detection and treatment of breast cancer. This is the first study to explore the association between breast inflammatory lesions and breast cancer at the transcriptome level. Our inflammation data and research results provide a basis for future inflammation-cancer transformation analysis. [ABSTRACT FROM AUTHOR]

Retraction phenomenon is a unique sign on an automated breast volume scanner coronal plane image and has high specificity in differentiating benign lesions from malignant breast cancer. The purpose of this study was to quantify the retraction phenomenon by setting different rules to describe connected regions from different dimensions. In total, six quantitative indicators (FΩ1,FΓ,FS,FΩ2,FΩ3and FL) were obtained. FΩ1, FΩ2 and FΩ3 represent the relative areas of the connected region under different rules. FΓandFS represent the number ratio and absolute area of the connected region, respectively. FL represents the ratio of edge numbers. Two hundred fourteen patients with 214 lesions (90 benign and 124 malignant) were enrolled in this study. All quantitative indicators in the malignant group were significantly higher than those in the benign group (all p values <0.001). The indicator FΓ achieved the highest area under the receiver operating characteristic curve (AUC) (0.701, 95% confidence interval: 0.631-0.771). Both FΓ and FS had significant associations with axillary lymph node metastasis (p = 0.023 and 0.049). Compared with the classic texture feature gray-level co-occurrence matrix, retraction phenomenon quantization improved the AUC by 8.3%. The results indicate that retraction phenomenon quantitative indicators have certain value in distinguishing benign and malignant breast lesions and seem to be associated with axillary lymph node metastasis. [ABSTRACT FROM AUTHOR]

Breast cancer is increasing rapidly in women these days. It is diagnosed after the symptoms appear. The detection at an initial stage and the procedure of the treatment are the most significant strategies curing it effectively. For the determination of breast cancer, breast mammography is required. It can be one of the methods to recognize tumor in women's breasts because it lessens the risk of death by recognizing the tumor at an early level. This paper shows a combination of selection methods for step by step evaluation for Efficient Seeded Region Growing with enhanced performance to spot breast cancer. The suggested method has been divided into the following parts: First, preprocessing of breast image is performed to estimate the automatic extraction of Region of Interest. Second, an automatic threshold is calculated for the binarization process. Third, the numbers of seed points are determined automatically and their positions in the breast image are identified using the density of pixels value. Fourth, a method calculating the threshold value is proposed for the purpose of region creation in seed region growing. The given method is applied and tested on the two publicly available mammogram data sets; Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM). It was observed that the proposed algorithm gives 94.6% and 95.3% True Positive Fraction (TPF) in case of MIAS and DDSM images, respectively. The parameters demonstrate that the given algorithm gives better results than the previously available methods. [ABSTRACT FROM AUTHOR]

Positron Emission Tomography (PET) has become a preferred imaging modality for cancer diagnosis, radiotherapy planning, and treatment responses monitoring. Accurate and automatic tumor segmentation is the fundamental requirement for these clinical applications. Deep convolutional neural networks have become the state-of-the-art in PET tumor segmentation. The normalization process is one of the key components for accelerating network training and improving the performance of the network. However, existing normalization methods either introduce batch noise into the instance PET image by calculating statistics on batch level or introduce background noise into every single pixel by sharing the same learnable parameters spatially. In this paper, we proposed an attentive transformation (AT)-based normalization method for PET tumor segmentation. We exploit the distinguishability of breast tumor in PET images and dynamically generate dedicated and pixel-dependent learnable parameters in normalization via the transformation on a combination of channel-wise and spatial-wise attentive responses. The attentive learnable parameters allow to re-calibrate features pixel-by-pixel to focus on the high-uptake area while attenuating the background noise of PET images. Our experimental results on two real clinical datasets show that the AT-based normalization method improves breast tumor segmentation performance when compared with the existing normalization methods. [ABSTRACT FROM AUTHOR]

Abstract Objective To retrospectively analyze the safety and long-term clinical efficacy of gelatin sponge microparticles combined with the chemotherapy drug pirarubicin for hepatic transcatheter arterial chemoembolization (GSMs-TACE) in order to treat breast cancer liver metastasis (BCLM). Methods Twenty-seven BCLM patients who underwent GSMs-TACE from July 2010 to July 2016 were enrolled. Tumor target blood vessels were slowly and regionally embolized with absorbable gelatin sponge particles and pirarubicin injections. Plain computed tomography (CT) scans and biochemical indexes were re-examined at 4 days after treatment, and enhanced CT scans or magnetic resonance images and biochemical indexes, 1 month later. For patients with stable tumors, the follow-up period was 2 to 3 months, and the tumor response was evaluated using Modified Response Evaluation Criteria in Solid Tumors. Adverse reactions, survival time, and prognostic factors were assessed. Results By October 2019, 27 patients with BCLM had undergone GSMs-TACE, with an average of 2.44 ± 1.58 treatments. The 1-, 3-, and 5-year survival rates were 62.96%, 22.22%, and 14.81%, respectively, and the mOS was 22.0 months. No serious complications, such as acute liver failure and liver abscess, had occurred. There were two cases of acute cholecystitis that recovered after symptomatic treatment. Multivariate analysis of the prognosis showed that the primary tumor size, number of metastatic lymph nodes, estrogen receptor/progesterone receptor (ER/PR) status, and time to postoperative liver metastasis and combination therapy were statistically significant. Conclusions The overall prognosis of BCLM was poor. GSMs-TACE was safe and effective for BCLM treatment and could prolong the median survival time of patients. Therefore, it is worthy of widespread clinical application.