7,164 research outputs found

    European guidelines for the diagnosis, treatment and follow-up of breast lesions with uncertain malignant potential (B3 lesions) developed jointly by EUSOMA, EUSOBI, ESP (BWG) and ESSO

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    Introduction: Breast lesions of uncertain malignant potential (B3) include atypical ductal and lobular hyperplasias, lobular carcinoma in situ, flat epithelial atypia, papillary lesions, radial scars and fibroepithelial lesions as well as other rare miscellaneous lesions. They are challenging to categorise histologically, requiring specialist training and multidisciplinary input. They may coexist with in situ or invasive breast cancer (BC) and increase the risk of subsequent BC development. Management should focus on adequate classification and management whilst avoiding overtreatment. The aim of these guidelines is to provide updated information regarding the diagnosis and management of B3 lesions, according to updated literature review evidence. Methods: These guidelines provide practical recommendations which can be applied in clinical practice which include recommendation grade and level of evidence. All sections were written according to an updated literature review and discussed at a consensus meeting. Critical appraisal by the expert writing committee adhered to the 23 items in the international Appraisal of Guidelines, Research and Evaluation (AGREE) tool. Results: Recommendations for further management after core-needle biopsy (CNB) or vacuum-assisted biopsy (VAB) diagnosis of a B3 lesion reported in this guideline, vary depending on the presence of atypia, size of lesion, sampling size, and patient preferences. After CNB or VAB, the option of vacuum-assisted excision or surgical excision should be evaluated by a multidisciplinary team and shared decision-making with the patient is crucial for personalizing further treatment. De-escalation of surgical intervention for B3 breast lesions is ongoing, and the inclusion of vacuum-assisted excision (VAE) will decrease the need for surgical intervention in further approaches. Communication with patients may be different according to histological diagnosis, presence or absence of atypia, or risk of upgrade due to discordant imaging. Written information resources to help patients understand these issues alongside with verbal communication is recommended. Lifestyle interventions have a significant impact on BC incidence so lifestyle interventions need to be suggested to women at increased BC risk as a result of a diagnosis of a B3 lesion. Conclusions: These guidelines provide a state-of-the-art overview of the diagnosis, management and prognosis of B3 lesions in modern multidisciplinary breast practice

    Partitioning intensity inhomogeneity colour images via Saliency-based active contour

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    Partitioning or segmenting intensity inhomogeneity colour images is a challenging problem in computer vision and image shape analysis. Given an input image, the active contour model (ACM) which is formulated in variational framework is regularly used to partition objects in the image. A selective type of variational ACM approach is better than a global approach for segmenting specific target objects, which is useful for applications such as tumor segmentation or tissue classification in medical imaging. However, the existing selective ACMs yield unsatisfactory outcomes when performing the segmentation for colour (vector-valued) with intensity variations. Therefore, our new approach incorporates both local image fitting and saliency maps into a new variational selective ACM to tackle the problem. The euler-lagrange (EL) equations were presented to solve the proposed model. Thirty combinations of synthetic and medical images were tested. The visual observation and quantitative results show that the proposed model outshines the other existing models by average, with the accuracy of 2.23% more than the compared model and the Dice and Jaccard coefficients which were around 12.78% and 19.53% higher, respectively, than the compared model

    Breast cancer detection using ensemble of convolutional neural networks

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    Early detection leading to timely treatment in the initial stages of cancer may decrease the breast cancer death rate. We propose deep learning techniques along with image processing for the detection of tumors. The availability of online datasets and advances in graphical processing units (GPU) have promoted the application of deep learning models for the detection of breast cancer. In this paper, deep learning models using convolutional neural network (CNN) have been built to automatically classify mammograms into benign and malignant. Issues like overfitting and dataset imbalance are overcome. Experimentation has been done on two publicly available datasets, namely mammographic image analysis society (MIAS) database and digital database for screening mammography (DDSM). Robustness of the models is accomplished by merging the datasets. In our experimentation, MatConvNet has achieved an accuracy of 94.2% on the merged dataset, performing the best amongst all the CNN models used individually. Hungarian optimization algorithm is employed for selection of individual CNN models to form an ensemble. Ensemble of CNN models led to an improved performance, resulting in an accuracy of 95.7%

    Evaluation of a novel wafer-scale CMOS APS X-ray detector for use in mammography

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    The most important factors that affect the image quality are contrast, spatial resolution and noise. These factors and their relationship are quantitatively described by the Contrast-to-Noise Ratio (CNR), Signal-to-Noise Ratio (SNR), Modulation Transfer Function (MTF), Noise Power Spectrum (NPS) and Detective Quantum Efficiency (DQE) parameters. The combination of SNR, MTF and NPS determines the DQE, which represents the ability to visualize object details of a certain size and contrast at a given dose. In this study the performance of a novel large area Complementary Metal-Oxide-Semiconductor (CMOS) Active Pixel Sensor (APS) X-ray detector, called DynAMITe (Dynamic range Adjustable for Medical Imaging Technology), was investigated and compared to other three digital mammography systems (namely a) Large Area Sensor (LAS), b) Hamamatsu C9732DK, and c) Anrad SMAM), in terms of physical characteristics and evaluation of the image quality. DynAMITe detector consists of two geometrically superimposed grids: a) 2560 � 2624 pixels at 50 μm pitch, named Sub-Pixels (SP camera) and b) 1280 � 1312 pixels at 100 μm pitch, named Pixels (P camera). The X-ray performance evaluation of DynAMITe SP detector demonstrated high DQE results (0.58 to 0.64 at 0.5 lp/mm). Image simulation based on the X-ray performance of the detectors was used to predict and compare the mammographic image quality using ideal software phantoms: a) one representing two three dimensional (3-D) breasts of various thickness and glandularity to estimate the CNR between simulated microcalcifications and the background, and b) the CDMAM 3.4 test tool for a contrast-detail analysis of small thickness and low contrast objects. The results show that DynAMITe SP detector results in high CNR and contrast-detail performance. © 2012 IEEE.</p

    Radiological Features of Male Breast Neoplasms: How to Improve the Management of a Rare Disease

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    The primary aim of our study was to assess the main mammographic and ultrasonographic features of invasive male breast malignancies. The secondary aim was to evaluate whether a specific radiological presentation would be associated with a worse receptor profile. Radiological images (mammography and/or ultrasound) of all patients who underwent surgery for male invasive breast cancer in our institution between 2008 and 2023 were retrospectively analyzed by two breast radiologists in consensus. All significant features of radiological presentation known in the literature were re-evaluated. Fifty-six patients were selected. The mean age at surgery of patients was 69 years (range: 35-81); in 82% of cases (46 patients), the histologic outcome was invasive ductal carcinoma. A total of 28 out of 56 (50%) patients had preoperative mammography; in 9/28 cases (32%), we found a mass with microcalcifications on mammography. The mass presented high density in 25 out of 28 patients (89%); the mass showed irregular margins in 15/28 (54%) cases. A total of 46 out of 56 patients had preoperative ultrasounds. The lesion showed a solid mass in 41/46 (89%) cases. In 5/46 patients (11%), the lesion was a mass with a mixed (partly liquid-partly solid) structure. We did not find any statistically significant correlation between major types of radiological presentation and tumor receptor arrangement. Knowledge of the main radiologic presentation patterns of malignant male breast neoplasm can help better manage this type of disease, which is rare but whose incidence is increasing

    Analysis of mathematical methods for diagnosis of breast diseases

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    This paper analyzes the diagnosis of breast diseases is a critical aspect of modern healthcare, as early detection can greatly improve patient outcomes. Mathematical methods have increasingly been utilized in recent years as a means of aiding in the diagnosis of breast diseases. This abstract provides an analysis of the various mathematical methods that have been developed and applied to this important area of medical research. The methods include but are not limited to artificial intelligence (AI), machine learning, and statistical modeling. The strengths and limitations of these approaches are examined, as well as their potential impact on clinical practice. Furthermore, the abstract will discuss the current state of research in this field and offer insights into future directions for the development and application of mathematical methods in the diagnosis of breast diseases. Keywords: mathematical methods, breast diseases (BD), mastopathy, fibroadenoma, risk factors, X-ray devices, ultrasound examination (USE)

    Targeting immune and desmoplastic tumor microenvironment to sensitize gynecological cancer cells to therapy

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    Cancer is a pervasive global threat that manifests with diverse clinical attributes and notable mortality rates, particularly attributable to its metastatic potential in solid cancers. These tumours encompass various types including epithelial cancers like high-grade serous ovarian cancer (HGSC) and mesenchymal cancers like uterine sarcomas (USs). Despite the differing origins of USs and HGSCs, the pivotal concept of the transition between epithelial and mesenchymal states remains remarkably plastic, occurring frequently in these cancers. This plasticity holds immense significance in understanding tumour invasiveness and metastasis. The TME emerges as a crucial influencer as exerting its impact on cancer progression, epithelial-mesenchymal transition (EMT), metastasis, and even chemoresistance. The TME comprises various elements, with the extracellular matrix (ECM) containing structural proteins like collagens, standing out as a key constituent. Moreover, immune cells within the TME, such as lymphocytes and macrophages, actively engage in interactions with both the ECM and cancer cells shaping local responses to kill the cancer cells or support their growth. Understanding the intricate tumour-TME interactions become imperative in formulating effective strategies aimed at modulating the immune response and halting cancer progression. Therefore, a nuanced comprehension of these complexities is crucial in developing strategies to combat cancer effectively. This thesis focuses on identifying TME factors, including ECM components and immune cell interactions in gynaecological cancers for improved precision medicine including immunotherapies and other novel treatments. In Paper I, Uterine sarcomas present distinct immune signatures with prognostic value, independent of tumour type. FOXP3+ cell density and CD8+/FOXP3+ ratio (CFR) correlated with favourable survival in endometrial stromal sarcomas (ESS) and undifferentiated uterine sarcomas (USS). The CFR also highlighted the correlation between CFR high and upregulation of ECM organization pathways. In Paper II conversely, uterine leiomyosarcomas (uLMS) showed distinct behaviours, with lower collagen density and upregulated ECM remodelling enzymes correlating with aggressiveness. MMP-14 and yes-associated protein 1 (YAP) were required for uLMS growth and invasion. In Paper Ⅲ, shifting to HGSC, matrisome, a group of proteins encoded by genes for core ECM proteins 4 (collagens, proteoglycans, and ECM glycoproteins) and ECM-associated proteins (proteins structurally resembling ECM proteins, ECM remodelling enzymes, and secreted factors) in the ECM, showed changes in expression depending on the type of tumour host tissues and after chemotherapy. Collagen VI, among scrutinized proteins, exhibited elevated expression linked to shortened survival in ovarian cancer patients. Mechanistically, collagen VI promoted platinum resistance via the stiffness-dependent β1 integrin-pMLC and YAP/TAZ pathways in HGSC cell lines In summary, this integrated exploration of uterine sarcomas and ovarian cancer provides a comprehensive understating of their TME. The study elucidates diverse immune and molecular features, offering potential prognostic markers and therapeutic targets. The findings underscore the complexity of these gynaecological malignancies, emphasizing the need for tailored approaches in understanding and combating these diseases

    Effectiveness and perspectives of women in the Dutch breast cancer screening programme

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    This thesis provides an overview of the evaluation of the Dutch breast cancer screening programme. The first part evaluates the effectiveness of the current programme and whether it can be improved. The second part focusses on the perspectives of the women eligible for breast cancer screening and their quality of life

    An Ensemble Deep Learning Model for the Detection and Classification of Breast Cancer

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    Background: Detecting breast cancer in its early stages remains a significant challenge in the present context and is a leading cause of death among women, primarily due to delayed identification. This paper presents a practical and accurate approach based on deep learning to identify breast cancer in cytology images.Method: The analytical approach leverages knowledge from a related problem through a technique known as transfer learning. Convolutional neural networks (CNNs) are employed due to their remarkable performance on large datasets. Image classification architectures such as Google network (GoogleNet), Visual geographical group network (VGGNet), residual network (ResNet), and dense convolution network (DenseNet) are utilized in this approach. By applying transfer learning, the images are classified into two categories: those containing cancer cells and those without them. The performance of the proposed ensemble method is evaluated using a breast cytology image dataset.Results: The results of our proposed ensemble framework outperform conventional CNN models in terms of precision, recall, and F1 measures, achieving an impressive 86% prediction accuracy. Visual representations of validation graphs for each classifier demonstrate that the ensemble framework surpasses the performance of pre-trained CNN architectures.Conclusion: Combining the outcomes of conventional CNN architectures into an ensemble framework enhances early breast cancer detection, leading to a reduction in mortality through timely medical interventions

    Raman Spectroscopy Techniques for the Detection and Management of Breast Cancer

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    Breast cancer has recently become the most common cancer worldwide, and with increased incidence, there is increased pressure on health services to diagnose and treat many more patients. Mortality and survival rates for this particular disease are better than other cancer types, and part of this is due to the facilitation of early diagnosis provided by screening programmes, including the National Health Service breast screening programme in the UK. Despite the benefits of the programme, some patients undergo negative experiences in the form of false negative mammograms, overdiagnosis and subsequent overtreatment, and even a small number of cancers are induced by the use of ionising radiation. In addition to this, false positive mammograms cause a large number of unnecessary biopsies, which means significant costs, both financially and in terms of clinicians' time, and discourages patients from attending further screening. Improvement in areas of the treatment pathway is also needed. Surgery is usually the first line of treatment for early breast cancer, with breast conserving surgery being the preferred option compared to mastectomy. This type of operation achieves the same outcome as mastectomy - removal of the tumour - while allowing the patient to retain the majority of their normal breast tissue for improved aesthetic and psychological results. Yet, re-excision operations are often required when clear margins are not achieved, i.e. not all of the tumour is removed. This again has implications on cost and time, and increases the risk to the patient through additional surgery. Currently lacking in both the screening and surgical contexts is the ability to discern specific chemicals present in the breast tissue being assessed/removed. Specifically relevant to mammography is the presence of calcifications, the chemistry of which holds information indicative of pathology that cannot be accessed through x-rays. In addition, the chemical composition of breast tumour tissue has been shown to be different to normal tissue in a variety of ways, with one particular difference being a significant increase in water content. Raman spectroscopy is a rapid, non-ionising, non-destructive technique based on light scattering. It has been proven to discern between chemical types of calcification and subtleties within their spectra that indicate the malignancy status of the surrounding tissue, and differentiate between cancerous and normal breast tissue based on the relative water contents. Furthermore, this thesis presents work aimed at exploring deep Raman techniques to probe breast calcifications at depth within tissue, and using a high wavenumber Raman probe to discriminate tumour from normal tissue predominantly via changes in tissue water content. The ability of transmission Raman spectroscopy to detect different masses and distributions of calcified powder inclusions within tissue phantoms was tested, as well as elucidating a signal profile of a similar inclusion through a tissue phantom of clinically relevant thickness. The technique was then applied to the measurement of clinically active samples of bulk breast tissue from informed and consented patients to try to measure calcifications. Ex vivo specimens were also measured with a high wavenumber Raman probe, which found significant differences between tumour and normal tissue, largely due to water content, resulting in a classification model that achieved 77.1% sensitivity and 90.8% specificity. While calcifications were harder to detect in the ex vivo specimens, promising results were still achieved, potentially indicating a much more widespread influence of calcification in breast tissue, and to obtain useful signal from bulk human tissue is encouraging in itself. Consequently, this work demonstrates the potential value of both deep Raman techniques and high wavenumber Raman for future breast screening and tumour margin assessment methods
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