Relevant bibliographies by topics / Breast Tumors Classification

Academic literature on the topic 'Breast Tumors Classification'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Breast Tumors Classification"

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Houseman, Eugene Andrέs, and Tan A. Ince. "Normal Cell-Type Epigenetics and Breast Cancer Classification: A Case Study of Cell Mixture–Adjusted Analysis of DNA Methylation Data from Tumors." Cancer Informatics 13s4 (January 2014): CIN.S13980. http://dx.doi.org/10.4137/cin.s13980.

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Historically, breast cancer classification has relied on prognostic subtypes. Thus, unlike hematopoietic cancers, breast tumor classification lacks phylogenetic rationale. The feasibility of phylogenetic classification of breast tumors has recently been demonstrated based on estrogen receptor (ER), androgen receptor (AR), vitamin D receptor (VDR) and Keratin 5 expression. Four hormonal states (HR0–3) comprising 11 cellular subtypes of breast cells have been proposed. This classification scheme has been shown to have relevance to clinical prognosis. We examine the implications of such phylogenetic classification on DNA methylation of both breast tumors and normal breast tissues by applying recently developed deconvolution algorithms to three DNA methylation data sets archived on Gene Expression Omnibus. We propose that breast tumors arising from a particular cell-of-origin essentially magnify the epigenetic state of their original cell type. We demonstrate that DNA methylation of tumors manifests patterns consistent with cell-specific epigenetic states, that these states correspond roughly to previously posited normal breast cell types, and that estimates of proportions of the underlying cell types are predictive of tumor phenotypes. Taken together, these findings suggest that the epigenetics of breast tumors is ultimately based on the underlying phylogeny of normal breast tissue.
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STEPONAVIČIENĖ, Laura, Daiva GUDAVIČIENĖ, and Raimundas MEŠKAUSKAS. "Rare types of breast carcinoma." Acta medica Lituanica 19, no. 2 (June 1, 2012): 81–91. http://dx.doi.org/10.6001/actamedica.v19i2.2314.

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Background. Breast cancer is a heterogeneous disease that encompasses several distinct entities with remarkably different characteristics. One of very important cancer characteristics is its histological type. Materials and methods. We used Pubmed and Medscape databases and analyzed original articles and literature reviews about rare histological types of breast cancer. Results and discussion. World Health Organization (WHO) presents a detailed classification of breast cancers. According to this classification, cancers are divided into epithelial, mesenchymal, fibroepithelial tumors. Malignant lymphoma, metastatic tumors can also be found in the breast. WHO also marks tumors of the nipple, male breast cancer and myoepithelial lesions. In this paper, only the invasive epithelial tumors are discussed. Most tumors are derived from mammary ductal epithelium, and up to 75% of the breast cancers are ductal carcinomas. The second most common epithelial tumor type is invasive lobular carcinoma which comprises 5–15% of the group. There are more than a dozen variants which are less common. They comprise less than 10% of breast tumors. Their clinical behavior can differ greatly. So, it is important to know their main characteristics in order to make the best treatment choice and to foresee prognosis. We shortly describe the epidemiology, diagnostics, clinical and immunophenotypic features, prognosis and predictive factors of rare epithelial breast tumors.
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Zhuang, Zhemin, Zengbiao Yang, Shuxin Zhuang, Alex Noel Joseph Raj, Ye Yuan, and Ruban Nersisson. "Multi-Features-Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine." Computational Intelligence and Neuroscience 2021 (May 19, 2021): 1–12. http://dx.doi.org/10.1155/2021/9980326.

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Breast ultrasound examination is a routine, fast, and safe method for clinical diagnosis of breast tumors. In this paper, a classification method based on multi-features and support vector machines was proposed for breast tumor diagnosis. Multi-features are composed of characteristic features and deep learning features of breast tumor images. Initially, an improved level set algorithm was used to segment the lesion in breast ultrasound images, which provided an accurate calculation of characteristic features, such as orientation, edge indistinctness, characteristics of posterior shadowing region, and shape complexity. Simultaneously, we used transfer learning to construct a pretrained model as a feature extractor to extract the deep learning features of breast ultrasound images. Finally, the multi-features were fused and fed to support vector machine for the further classification of breast ultrasound images. The proposed model, when tested on unknown samples, provided a classification accuracy of 92.5% for cancerous and noncancerous tumors.
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MENUT, OLIVIER, RANGARAJ M. RANGAYYAN, and J. E. LEO DESAUTELS. "PARABOLIC MODELING AND CLASSIFICATION OF BREAST TUMORS." International Journal of Shape Modeling 03, no. 03n04 (September 1997): 155–66. http://dx.doi.org/10.1142/s0218654397000124.

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Ng, Shun Leung, and Walter F. Bischof. "Automated detection and classification of breast tumors." Computers and Biomedical Research 25, no. 3 (June 1992): 218–37. http://dx.doi.org/10.1016/0010-4809(92)90040-h.

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Guseynov, Arif, T. Guseynov, and V. Odincov. "BENIGN TUMORS BREAST GLASS." Clinical Medicine and Pharmacology 7, no. 2 (November 9, 2021): 2–11. http://dx.doi.org/10.12737/2409-3750-2021-7-2-2-11.

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The lecture provides relevant information for doctors of various specialties: oncologists, surgeons, mammologists, general practitioners on the problems of diagnosis and treatment of benign breast formations. The issues of etiology and pathogenesis, classification and clinical picture of various formations are highlighted, diagnostic methods, differential diagnostics, treatment tactics and methods of surgical treatment are described in detail.
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Krasnoslobodtsev, Nikolay, Evgeny Shapiro, Tatyana Alymova, and Natalya Kuharenko. "Some etiopathogenetic features of dogs’ breast tumors." E3S Web of Conferences 203 (2020): 01014. http://dx.doi.org/10.1051/e3sconf/202020301014.

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The article presents data on the epidemiological features of breast cancer in dogs, namely: the frequency of oncological pathologies in dogs in 2017-2019, the structure of oncological diseases, studied the age-sex characteristics of breast tumors, the frequency of certain risk factors leading to the development of breast neoplasms in dogs. From this study, it was found that neoplasms occurred in 4.8% of the dogs admitted to the clinic. In dogs, a mammary gland tumor was recorded in 153 individuals оf which, 150 females and 3 males. Breast tumors ranked first in localization (28% of all tumors), and skin tumors ranked second (8.5% of all tumors). In females, the first place is a breast tumor (45%), in males - skin tumors (13%). In most animals, the size of a breast tumor at its primary detection corresponded to the T2-T4 stage according to the TNM classification, and in some cases T4a-d. Out of 150 females with breast tumors, only 40 were castrated. This justifies the need for the formation of approaches to early diagnosis of breast tumors in domestic dogs, as well as to the study and prevention of risk factors.
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Ouyang, Yali, Po-Hsiang Tsui, Shuicai Wu, Weiwei Wu, and Zhuhuang Zhou. "Classification of Benign and Malignant Breast Tumors Using H-Scan Ultrasound Imaging." Diagnostics 9, no. 4 (November 8, 2019): 182. http://dx.doi.org/10.3390/diagnostics9040182.

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Breast cancer is one of the most common cancers among women worldwide. Ultrasound imaging has been widely used in the detection and diagnosis of breast tumors. However, due to factors such as limited spatial resolution and speckle noise, classification of benign and malignant breast tumors using conventional B-mode ultrasound still remains a challenging task. H-scan is a new ultrasound technique that images the relative size of acoustic scatterers. However, the feasibility of H-scan ultrasound imaging in the classification of benign and malignant breast tumors has not been investigated. In this paper, we proposed a new method based on H-scan ultrasound imaging to classify benign and malignant breast tumors. Backscattered ultrasound radiofrequency signals of 100 breast tumors were used (48 benign and 52 malignant cases). H-scan ultrasound images were constructed with the radiofrequency signals by matched filtering using Gaussian-weighted Hermite polynomials. Experimental results showed that benign breast tumors had more red components, while malignant breast tumors had more blue components in H-scan ultrasound images. There were significant differences between the RGB channels of H-scan ultrasound images of benign and malignant breast tumors. We conclude H-scan ultrasound imaging can be used as a new method for classifying benign and malignant breast tumors.
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Tran Thi Song, Huong, Yen Vo Thi Kim, and Quan Nguyen Phuoc Bao. "APPLICATION OF ELASTOGRAPHY FOR DIAGNOSIS BREAST TUMORS." Volume 8 Issue 6 8, no. 6 (December 2018): 8–14. http://dx.doi.org/10.34071/jmp.2018.6.1.

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Breast tumor is common in women. Benign tumors account for 80%, malignant tumors account for 20%. Breast cancer is the most common and deadly cancer among women, including Vietnam. Elastography, evaluates the stiffness of the tissue, helps to distinguish soft or hard tumors, which can help distinguish benign or malignant. Benign lesions tend to be softer than malignant lesions. There are two types of elastography: SE (Strain Elastography) and Shear Ware Elastography (SWE). In examining breast lesions, the maligne tumor tends to be stronger and the higher the velocity. Studies have shown that the SWE features should be combined with 2D ultrasound to complement the BIRADS classification. Elastography is a new technique that has emerged in the past few years, promising good diagnostic prospects, more and more research and application of elastography in diagnostics breast lesions. Breast elastogarphy, survey of hardness of breast cancer showed 4 times higher than that of benign tumor and 7 folds of normal breast tissue.
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Muhtadi, Sabiq. "Breast Tumor Classification Using Intratumoral Quantitative Ultrasound Descriptors." Computational and Mathematical Methods in Medicine 2022 (March 7, 2022): 1–18. http://dx.doi.org/10.1155/2022/1633858.

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Breast cancer is a global epidemic, responsible for one of the highest mortality rates among women. Ultrasound imaging is becoming a popular tool for breast cancer screening, and quantitative ultrasound (QUS) techniques are being increasingly applied by researchers in an attempt to characterize breast tissue. Several different quantitative descriptors for breast cancer have been explored by researchers. This study proposes a breast tumor classification system using the three major types of intratumoral QUS descriptors which can be extracted from ultrasound radiofrequency (RF) data: spectral features, envelope statistics features, and texture features. A total of 16 features were extracted from ultrasound RF data across two different datasets, of which one is balanced and the other is severely imbalanced. The balanced dataset contains RF data of 100 patients with breast tumors, of which 48 are benign and 52 are malignant. The imbalanced dataset contains RF data of 130 patients with breast tumors, of which 104 are benign and 26 are malignant. Holdout validation was used to split the balanced dataset into 60% training and 40% testing sets. Feature selection was applied on the training set to identify the most relevant subset for the classification of benign and malignant breast tumors, and the performance of the features was evaluated on the test set. A maximum classification accuracy of 95% and an area under the receiver operating characteristic curve (AUC) of 0.968 was obtained on the test set. The performance of the identified relevant features was further validated on the imbalanced dataset, where a hybrid resampling strategy was firstly utilized to create an optimal balance between benign and malignant samples. A maximum classification accuracy of 93.01%, sensitivity of 94.62%, specificity of 91.4%, and AUC of 0.966 were obtained. The results indicate that the identified features are able to distinguish between benign and malignant breast lesions very effectively, and the combination of the features identified in this research has the potential to be a significant tool in the noninvasive rapid and accurate diagnosis of breast cancer.
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Dissertations / Theses on the topic "Breast Tumors Classification"

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Chaudhury, Baishali. "The Use of Textural Kinetic Habitats to Mine Diagnostic Information from DCE MR Images of Breast Tumors." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5663.

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Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the breast is a widely used non-invasive approach to gather information about the underlying physiology of breast tumors. Recent studies indicate that breast tumor heterogeneity may reflect the presence of different levels of cellular aggressiveness or habitats within the tumor. This heterogeneity has been correlated to the variations in the contrast enhancement patterns within the tumor apparent on gadolinium-enhanced DCE-MRI. Although pathological and qualitative (based on contrast enhancement patterns) studies suggest the presence of clini- cal and molecular predictive tumor sub-regions, this has not been fully investigated in the quantitative domain. The new era of cancer imaging emphasizes the use of Radiomics to provide in vivo quan- titative prognostic and predictive imaging biomarkers. Thus Radiomics focuses on apply- ing image analysis techniques to quantify tumor radiographic properties to create mineable databases from radiological images. In this research work, the Radiomics approach was ap- plied to develop a novel computer aided diagnosis (CAD) model for quantifying intratumor heterogeneity not only within the tumor as a whole, but also within tumor habitats with an intent to build predictive models in breast cancer. The process of building these predictive models started with 2-D tumor segmentation followed by habitat extraction (based on vari- ations in contrast patterns and geometry) and textural kinetic feature extraction to quantify habitat heterogeneity. A new correlation based random subspace ensemble framework was developed to evaluate the textural kinetics from the individual tumor habitats. This new CAD framework was applied to predict two clinical and prognostic factors: Axillary lymph node (ALN) metastases and Estrogen receptor (ER) status. An AUC of more than 0.8 was achieved for classifying breast tumors based on number of ALN involvement. The highest AUC of 0.91 was achieved for classifying tumors with no ALN metastases from tumors with 4 or more ALN metastases. For classifying tumors based on ER status the highest AUC of 0.87 was achieved. These results were acquired by utilizing the textural kinetic features from the tumor habitat with rapid delayed washout. The results presented in this work showed that the heterogeneity within the tumor habitats which showed rapid contrast washout in the delayed phase, correlated with aggressive cellular phenotypes. This work hypothesizes that successfully quantifying these prognostic factors will prove to be clinically significant as it can improve the diagnostic accuracy. This, in turn, will im- prove the breast cancer treatment paradigm by providing more tailored treatment regimens for aggressive tumors.
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Guo, Qi. "Computerised texture and shape analysis for classification of breast tumours in digital mammograms." Thesis, University of Reading, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501358.

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Computer-aided diagnosis for detection and classification of breast abnormalities on digital mammograms is an active area of. research. In the recent years, there have been many research developments in all aspects of the mammography. However, it still faces many challenges. The current detection accuracy of lesions such as mass and architectural distortion is considerably low. This research is focused on computerised texture analysis of mass and architectural distortion, and shape analysis of mass in digital mammograms. In texture analysis, we investigate fractal-based methods in texture characterisation of mammographic masses and architectural distortion.The individual ability of the different fractal-based features in the task of discriminating between abnormal lesion and normal breast parenchyma tissue are evaluated and compared using statistical analysis and receiver operating characteristics analysis.
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Purvis, Nina Louise. "Classification of breast malignancy using optimised advanced diffusion-weighted imaging, and, Surgical planning for breast tumour resection using MR-guided focused ultrasound." Thesis, University of Hull, 2016. http://hydra.hull.ac.uk/resources/hull:15193.

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Intravoxel Incoherent Motion Imaging (IVIM) is a non-invasive MR-imaging technique that enables the measurement of cellularity and vascularity using diffusion-weighted (DW)-imaging. IVIM has been applied to various cancer types including breast cancer, and is becoming more popular but lacks standardisation. The quantitative parameters; diffusion, D, perfusion fraction, f, and pseudo micro capillary diffusion, D* are thought to be correlated with tumour physiognomies such as proliferation, angiogenesis and heterogeneity. In Part 1 of this thesis, an optimised clinical b-value protocol is produced using a robust statistical method. This optimised protocol and various fitting methodologies are investigated in healthy volunteers, and then the most precise approach is applied in a clinical trial in patients following diagnosis of breast cancer, before treatment, to correlate IVIM parameters with breast cancer grade, histological type and molecular subtype with statistically significant results supporting IVIM’s potential as a non-invasive biomarker for malignancy. Monte Carlo simulations support this clinical application, where real data mean squared errors due to SNR limitations lie within simulated errors. A computed DW-imaging program is also presented to produce better quality images than acquired high b-value images as an adjunct to the optimised IVIM protocol. In Part 2 of this thesis, MR-guided Focused Ultrasound (MRgFUS) is explored as a means to create a pre-surgical template of thermally induced palpable markers to enable a surgeon to resect occult lesions and potentially reduce positive tumour margin status and local recurrence after breast conserving surgery. A surrogate animal model with pseudo lesion is presented, as well as a clinical tool to plan spot markers around a lesion as seen on MRI.
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Sundqvist, Martina. "Stability and selection of the number of groups in unsupervised clustering : application to the classification of triple negative breast cancers." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM026.

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Dans cette thèse, je traite, d'un point de vue statistique, le sujet de la classification des tumeurs du cancer du sein triple négatif (TNBC). Je me concentre principalement sur l'utilisation de la stabilité des clusters pour sélectionner le nombre de groupes dans le clustering, la méthode généralement utilisée pour la classification des TNBC. L'objectif de cette méthode est d'obtenir une classification robuste, c'est-à-dire facilement reproductible sur des données similaires.Malgré sa popularité, on sait encore peu de choses sur la façon dont cette méthode fonctionne. Pour cette raison, je propose deux contributions méthodologiques importantes : (1) un package R, clustRstab}, qui permet d'estimer, de manière flexible, la stabilité d'un clustering avec différents paramètres. Ce package est accompagné d'une étude de simulation et d'une étude d'application qui examine sous quelles conditions cette méthode fonctionne. (2) Une version modifiée de la version Ajusté du Rand Index (ARI), un score populaire pour les comparaisons de clusters, étape cruciale pour estimer la stabilité d'un clustering. Je corrige ce score en le basant sur une hypothèse de distribution multinomiale qui lui permet de prendre en compte la dépendance entre les clusters et de faire des inférences statistiques. Ce ARI modifié (M ARI) est implémenté dans le package R aricode. Ces deux méthodes sont ensuite appliquées à une large cohorte de tumeurs TNBC et les résultats sont discutés en relation avec des résultats des classification du TNBC de la littérature
In this thesis, I treat the topic of classifying Triple Negative Breast Cancer (TNBC) tumors from a statistical point of view. After proposing a classification of TNBC based on proteins, I mainly focus on the use of cluster stability for selecting the number of groups in unsupervised clustering. Indeed, this is the method generally employed when classifying TNBC. The aim of this method is to obtain a classification that is robust, that is, easily replicable on similar data. This is measured by its sensibility to small changes, such as subsamplig of the dataset.Despite the popularity of this method, little is still known about how or when it works. For this reason, I propose two important methodological contributions, increasing the usability and interpretability of this method: (1) an R-package, clustRstab, that easily enables to estimate the stability of a clustering in different parameter settings. This package is accompanied by a simulation and an application study investigating when and how this method works. (2) A Modified version of the Adjusted Rand Index (ARI), a popular score for cluster comparisons which is a crucial step for estimating the stability of a clustering. I correct this score by basing it on a multinomial distribution hypothesis which enables it to take into account dependence between clusterings and conduct statistical inference. This Modified ARI (M ARI) is implemented in the R package texttt{aricode}.These two methods are then applied to a large cohort of TNBC tumors and the results are discussed in relation to earlier classification results of TNBC
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Neudert, Marcus, Christian Fischer, Burkhard Krempien, Markus J. Seibel, and Frieder Bauss. "A Rapid Histological Score for the Semiquantitative Assessment of Bone Metastases in Experimental Models of Breast Cancer." Karger, 2008. https://tud.qucosa.de/id/qucosa%3A27606.

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Background: Using a nude rat model of site-specific metastatic bone disease (MBD), we developed a semiquantitative histological score for rapid assessment of lytic lesions in bone. This provides additional information to conventional histological measurement by clarifying the extent and location of metastatic infiltration and the tumor growth pattern. The score can also be used to assess the action of bisphosphonates on bone metastases. Materials and Methods: Male nude rats (n = 12 per group) were inoculated with the human breast cancer cell line MDA-MB-231 via the femoral artery. Following appearance of radiographically visible osteolytic lesions on day 18, the animals received phosphate-buffered saline (PBS; controls) or ibandronate (IBN, 10 µg P/kg) daily until day 30. Whole body radiographs were obtained on days 18 and 30, and osteolytic areas (OA) were determined by radiographic computer-based analysis (CBA). On day 30, MBD was assessed in both tibias using conventional histological CBA and the new scoring system. Results: Metastatic tumor area correlated with the total sum of the new score in both PBS- (r = 0.762) and IBN-treated animals (r = 0.951; p < 0.001). OA correlated well with the total sum in both groups (r = 0.845 and 0.854, respectively; p < 0.001). Conclusion: Significant reduction of bone marrow and cortical infiltration of tumor cells with IBN suggested local control of metastases.
Hintergrund: Mit Hilfe eines etablierten Tiermodells zur Erzeugung lokalisationsspezifischer Knochenmetastasen in der Nacktratte wurde ein semiquantitatives histologisches Graduierungssystem zur schnellen Bewertung osteolytischer Knochenmetastasen entwickelt. Das Graduierungssystem liefert hinsichtlich der Metastasenlokalisation, deren Ausmaß und Infiltrationsmuster wertvolle Zusatzinformationen zu den konventionellen histologischen Untersuchungsmethoden. Damit kann beispielsweise auch die pharmakologische Wirkung von Bisphosphonaten auf die Knochenmetastasierung beurteilt werden. Material und Methoden: Männlichen Nacktratten (n = 12 pro Gruppe) wurden Zellen der humanen Brustkrebszellinie MDA-MB-231 in die Oberschenkelarterie inokuliert. Ab dem Auftreten radiologisch erkennbarer Osteolysen 18 Tage nach Inokulation erhielten die Tiere bis zum Studienende (Tag 30) täglich entweder eine subkutane Applikation einer Phosphat-Puffer-Lösung (Kontrollgruppe) oder Ibandronat (IBN, 10 µg P/kg; Behandlungsgruppe). Konventionelle Röntgenaufnahmen wurden an den Tagen 18 und 30 nach Tumorinokulation angefertigt und die Osteolysenflächen mittels Computerauswertung bestimmt. Nach Studienende wurde der Metastasenbefall in beiden Tibiae sowohl konventionell histologisch als auch mittels des neuen Graduierungssystems ausgewertet. Ergebnisse: Die Metastasenfläche korrelierte mit der kummulativen Punktsumme des Graduierungssystems sowohl in der Kontrollgruppe (r = 0,762; p < 0,001) als auch in der Ibandronat- Gruppe (r = 0,951; p < 0,001). Ebenso war die Osteolysenfläche eng mit der Punktesumme in beiden Gruppen korreliert (r = 0,845 und 0,854; p < 0,001). Schlussfolgerung: Die signifikante Reduktion von Knochenmark- und Kortikalisbefall durch IBN deuten auf eine gute lokale Kontrolle des Metastasenwachstums hin.
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
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Albuquerque, Andreia de, Sepp Kaul, Georg Breier, Petra Krabisch, and Nikos Fersis. "Multimarker Analysis of Circulating Tumor Cells in Peripheral Blood of Metastatic Breast Cancer Patients: A Step Forward in Personalized Medicine." Karger, 2012. https://tud.qucosa.de/id/qucosa%3A27718.

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Aim: To develop an immunomagnetic assay for the isolation of circulating tumor cells (CTCs) followed by the analysis of a multimarker panel, which will enable the characterization of these malignant cells with high accuracy. Patients and Methods: Peripheral blood (PB) was collected from 32 metastatic breast cancer patients and 42 negative controls. The antibodies BM7 and VU1D9 were used for immunomagnetic tumor cell enrichment. A real-time reverse transcription-polymerase chain reaction (RT-PCR) approach for the markers KRT19, SCGB2A2, MUC1, EPCAM, BIRC5 and ERBB2 was used for CTC detection and characterization. Results: The positivity rates for each marker were as follows: 46.9% for KRT19, 25.0% for SCGB2A2, 28.1% for MUC1, 28.1% for EPCAM, 21.9% for BIRC5, and 15.6% for ERBB2. After the creation of individualized cutoffs, the sensitivity and specificity of the combined marker gene panel increased to 56.3% and 100%, respectively. Interestingly, 27.0% of the HER2-negative tumor patients showed ERBB2 mRNA-positive CTCs. Conclusions: The described technique can be used to measure CTCs with great accuracy. The use of a multimarker panel for the characterization of CTCs may provide real-time information and be of great value in therapy monitoring.
Ziel: Entwicklung eines immunomagnetischen Verfahrens zur Isolierung zirkulierender Tumorzellen (CTCs) in Kombination mit einer molekularen Multimarkeranalyse für die hochspezifische Identifizierung maligner Zellen. Patientinnen und Methoden: Peripheres Blut (PB) von 32 Patientinnen mit metastasiertem Mammakarzinom und von 42 gesunden Kontrollen wurde für die immunomagnetische Tumorzellanreicherung mit den Antikörpern BM7 und VU1D9 genutzt. Eine Real-Time Reverse Transkription Polymerase-Kettenreaktion (RT-PCR)-Methodik mit den Markern KRT19, SCGB2A2, MUC1, EPCAM, BIRC5 und ERBB2 wurde für den CTC-Nachweis und die Tumorzellcharakterisierung entwickelt. Ergebnisse: Für die einzelnen Marker wurden die folgenden Positivitätsraten ermittelt: 46,9% für KRT19, 25,0% für SCGB2A2, 28,1% für MUC1, 28,1% für EPCAM, 21,9% für BIRC5 und 15,6% für ERBB2. Nach der Bestimmung individualisierter Cut-off-Werte ergab sich für den kombinierten Multimarkernachweis eine Sensitivität und Spezifität von 56,3% bzw. 100%. Bemerkenswert war der Befund, dass 27,0% der HER2-tumornegativen Patientinnen ERBB2-mRNA-positive CTCs aufwiesen. Schlussfolgerung: Die hier beschriebene Methodik bestimmt CTCs mit hoher Spezifität. Die molekulare Multimarkeranalyse liefert wertvolle Real-Time-Informationen für personalisierte Behandlungsmodalitäten.
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
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Makdissi, Fabiana Baroni Alves. "Influência do microambiente no prognóstico do câncer da mama." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/5/5155/tde-01042014-112230/.

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Introdução: Os cânceres de mama subtipos Luminal A e B (HER2 negativo) podem apresentar prognóstico variável, a depender do índice de proliferação, avaliado pelo Ki67. As células malignas e as células estromais adjacentes (fibroblastos e células de resposta imune ) podem interagir tanto pelo contato célula a célula como por fatores secretados por elas, ambas influenciando no comportamento tumoral. Já foi demonstrado que as células estromais podem aumentar a proliferação das células do câncer da mama. Objetivo: Nosso objetivo foi avaliar o perfil de expressão gênica de células do estroma em câncer de mama luminal A e luminal B e analisar se este se correlaciona com o prognóstico da doença. Pacientes e Métodos/ Resultados: Amostras de tumores de 11 pacientes na pós menopausa foram analisadas, todas elas HER2 negativas. A expressão de Ki67 foi <= 10 % em 5 pacientes (luminal A) e >= 30 % em outras 6 amostras(Luminal B ). Células estromais foram microdissecadas para a extração de RNA, que posteriormente foi hibridizado na plataforma de microarray Agilent G485 -1A GE 8x60K. Após a normalização, 50 % dos genes com a maior variância foram selecionados para análise por SAM duas classes desemparelhado (software TMEV ) e aceitando FDR 14.1%, 35 sequências foram identificadas como diferencialmente expressas, incluindo 16 genes conhecidos, entre as células estromais das amostras de Luminal A versos Luminal B, todos mais expressos nas amostras B. Dentre as funções biológicas enriquecidas em genes diferencialmente expressos encontram-se regulação positiva do sistema imune, incluindo genes como ZAP70 (proteína quinase 70kDa associada a cadeia zeta (TCR)), CD38 (molécula CD38); UBASH3A (ubiquitina associada e SH3 domínio que contém A); PLA2G7 (fosfolipase A2, grupo VII (fator acetil ativador de plaquetas no plasma)); NCR3 (citotoxicidade natural, provocando receptor 3). Nosso próximo passo foi avaliar se a expressão de alguns genes selecionados estava associada com prognóstico de tumores luminais. Para tal selecionamos amostras de outro grupo de 89 pacientes com seguimento de pelo menos 5 anos, cujos tumores eram ER(+), HER2(-), para análise de expressão proteica em Tissue microarray. Caracterizamos os fibroblastos destas amostras com 3 marcadores de fibroblastos: actina de músculo liso (AML), S100A4 e caveolina-1 (CAV1) e analisamos a marcação da proteína ZAP70. Correlacionamos a expressão proteica de todos os marcadores com as características anatomopatológicas da amostra. Observamos que fibroblastos de todas as amostras de tumor de mama expressam AML, S100A4 e CAV1, em diferentes proporções, entretanto não detectamos diferença entre os tumores luminais A e B. Também não obsevamos diferença de expressão de AML, S100A4 e CAV1 em relação a grau histológico, comprometimento linfonodal e estadiamento clínico. Nestas amostras não detectamos expressão proteica de ZAP70 em fibroblastos tumorais. Conclusão: Houve expressão diferencial de 16 genes relacionados a processos imunes, todos eles mais expressos em células estromais de tumores Luminal B em relação a luminal A
Introduction: Luminal breast cancer subtypes A and B (HER2 negative) may present a variable prognosis, depending on tumor proliferation index, evaluated by Ki67 expression. Malignant cells and adjacent stromal cells (fibroblasts and immune response cells) may interact by both cell contact and secreted factors and influence tumor behavior. It was shown that stromal cells may enhance breast cancer cells proliferation. Objective: Our aim was to evaluate stromal cells gene expression profile in luminal A and luminal B tumors and to evaluate whether selected transcripts expressed in stromal cells may be associated with prognosis in breast cancer. Material/ Methods and Results: Hormone receptor positive tumor samples from 11 post menopausal patients were analyzed, all of them Her2 negative. Ki67 expression <= 10% (luminal A) was observed in five and Ki67 >= 30% (luminal B) in six samples. Stromal cells were microdissected for RNA extraction, which was hybridized in Agilent G485-1A GE 8x60K microarray platform. After normalization, 50% of the genes with the highest variance were selected for further analysis by two class unpaired SAM (TMEV software) and accepting FDR 14,1%, 35 sequences, including 16 known genes, were found differentially expressed between stromal cells from luminal A vs luminal B breast cancer samples, all of them more expressed in luminal B. Among biological functions enriched in genes found differentially expressed were positive regulation of immune system process, including genes as: ZAP70 (zeta-chain (TCR) associated protein kinase 70kDa); CD38 (CD38 molecule); UBASH3A (ubiquitin associated and SH3 domain containing A); PLA2G7 (phospholipase A2, group VII (platelet-activating factor acetylhydrolase, plasma); NCR3 (natural cytotoxicity triggering receptor 3). Our next step was evaluate whether expression of selected genes was associated with prognosis in another group of patients. Tumor samples from 89 patients with at least 5 years of follow up, all of them estrogen receptor positive and HER2 negative, were selected. Tissue microarray was prepared with stromal tumor compartment from paraffin embedded tumor samples. Fibroblasts were characterized for the expression of 3 fibroblasts markers (alfa-SMA, alpha smooth muscel actin; S100A4 and CAV1, caveolin 1), and ZAP70. Correlation of expression of these markers with prognostic variables was determined. Expression of alfa-SMA, S100A4 and CAV1 was detected in fibroblasts from all tumor samples in different proportions, however no differential expression was observed between luminal A and B tumors. Neither difference was detected on the expression of these proteins in relation with histological grade, lymph node involvement and clinical stage. Conclusion: A differential expression of 16 genes involved in immune process was found, all of them more expressed in fibroblasts from luminal B as compared with luminal A tumors
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Seifert, Michael, Khalil Abou-El-Ardat, Betty Friedrich, Barbara Klink, and Andreas Deutsch. "Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles." Public Library of Science, 2014. https://tud.qucosa.de/id/qucosa%3A28671.

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Changes in gene expression programs play a central role in cancer. Chromosomal aberrations such as deletions, duplications and translocations of DNA segments can lead to highly significant positive correlations of gene expression levels of neighboring genes. This should be utilized to improve the analysis of tumor expression profiles. Here, we develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in tumor. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the simultaneous usage of higher-order state-transitions and autoregressive emissions as novel model features. We apply autoregressive higher-order HMMs to the analysis of breast cancer and glioma gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other related methods. The performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions. This benefit could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumors independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas.
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Kennedy, Brian Michael Kennedy. "Leveraging Multimodal Tumor mRNA Expression Data from Colon Cancer: Prospective Observational Studies for Hypothesis Generating and Predictive Modeling." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1498742562364379.

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Habla, Christiane. "Der Einfluss von Relaxin auf das Wachstum von Mammakarzinomen." Doctoral thesis, Universitätsbibliothek Leipzig, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-37922.

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Brustkrebs ist die häufigste Krebstodesursache bei Frauen in den Industrienationen mit einer jährlich ansteigenden Neuerkrankungsrate (Senn und Niederberger 2002). Durch vorangegangene Untersuchungen wurde bereits deutlich, dass das Peptidhormon Relaxin unter in vitro Bedingungen maßgeblich zur Tumorprogression von Mammakarzinomen beiträgt (Binder et al. 2002). Die vorliegende Arbeit hat untersucht, ob Relaxin diese Wirkung auch in vivo auf Mammakarzinome ausübt. Relaxin ist ein multifunktionales Hormon. Es ist ein Aktivator verschiedenerWachstumsund Transkriptionsfaktoren (Samuel et al. 2007a) und nimmt eine Schlüsselfunktion im Bindegewebsstoffwechsel ein, indem es durch eine Steigerung der MMP-Expression zur bindegewebigen Erweichung führt (Unemori et al. 1996). Im Krebsgeschehen schafft das Peptidhormon damit die Voraussetzungen für Tumorwachstum und Metastasierung (Bingle et al. 2002). Für die Fragestellung der vorliegenden Arbeit wurde das Brustkrebsmodell der BalbneuT- Maus eingesetzt, die aufgrund der transgenen HER2-Überexpression spontan Mammakarzinome entwickelt. Es wurden 45 weibliche Tiere mit beginnendem Wachstum von Mammatumoren auf eine Relaxin- (n=22) und eine Kontrollgruppe (n=23) aufgeteilt. Den Tieren wurde über eine unter das Nackenfell implantierte osmotische Minipumpe (Fa. Alzet, Modell 2004; Kupertura, Kanada) im Falle der Relaxin-Gruppe Relaxin und im Falle der Kontrollgruppe isotone Natriumchloridlösung verabreicht. Danach wurden die Tiere 10-49 Tage beobachtet und daraufhin eingeschläfert. Es wurden die Tumoren, Biopsien von Leber, Lunge und Nieren sowie Blutproben entnommen. Um beurteilen zu können, ob die Tumoren der Relaxin-behandelten Tiere ein schnelleres Wachstum zeigten, wurden Tumorvolumina und -gewichte zu den unterschiedlichen Tötungszeitpunkten erfasst. Weiterhin wurden im Tumorgewebe immunhistochemisch der Proliferationsmarker Ki67, der Makrophagenmarker MAC 387, der Relaxinrezeptor RXFP1 sowie die Steroidhormonrezeptoren für 17!-Östradiol (ER) und Progesteron (PR) bestimmt. Zusätzlich wurde die RXFP1-spezifische mRNA molekularbiologisch im Tumorgewebe dargestellt. Außerdem wurden die peripheren Hormonkonzentrationen von Relaxin, 17!-Östradiol (E2) und Progesteron (P4) ermittelt. Die Ergebnisse der vorliegenden Arbeit konnten den Beweis erbringen, dass Relaxin auch in vivo dasWachstum von Mammakarzinomen unterstützt. Relaxin bewirkte im vorliegenden Experiment eine Rekrutierung von Tumor-assoziierten Makrophagen (TAMs) ins tumorumgebenden Bindegewebe. Dadurch erfolgte dort die Synthese verschiedener Faktoren und Enzyme, welche zur bindegewebigen Erweichung, Apoptosehemmung und zu einer gesteigerten Zellproliferation führten (Bingle et al. 2002; Devetzi et al. 2008). Weiterhin induzierte die exogene Relaxingabe eine vermehrte E2-Synthese, was sich ebenfalls wachstumsfördernd und apoptosehemmend auswirkte und somit die Tumorproliferation unterstützt hat (Catalano et al. 2009; Lewis-Wambi und Jordan 2009). Die Expression des RXFP1 im Tumorgewebe wurde durch Relaxin über eine gesteigerte E2- Synthese (Wilson et al. 2008) gefördert, ebenso wie die Expression des ER. Weiterhin führte Relaxin zu einer gesteigerten P4-Synthese und zur gesteigerten Expression des PR im Tumorgewebe über einen derzeit noch unbekannten Mechanismus. Aufgrund der maßgeblichen Bedeutung des Peptidhormons für das Progressionsverhalten von Mammakarzinomen kann die Bestimmung der Relaxinblutspiegel bei Brustkrebspatientinnen deshalb in Zukunft ein wichtiges Hilfsmittel bei der Wahl der richtigen Therapie und bei der Prognosebeurteilung werden.
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Books on the topic "Breast Tumors Classification"

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M.J. van de Vijver. WHO Classification of Tumours of the Breast: IARC WHO Classification of Tumours, No 4. LYON, FRANCE: International Agency for Research on Cancer, 2012.

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Leong, Stanley P. L. Atlas of Selective Sentinel Lymphadenectomy for Melanoma, Breast Cancer and Colon Cancer. Cleveland: Kluwer Academic Publishers, 2003.

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Sobin, Leslie H., and Christian Wittekind. TNM Classification of Malignant Tumours: Breast and Gynaecological Tumours. Wiley & Sons, Incorporated, John, 2008.

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Prati, Raquel, and Olga Olevsky. Breast Cancer Staging and Treatment. Edited by Christoph I. Lee, Constance D. Lehman, and Lawrence W. Bassett. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190270261.003.0012.

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Breast carcinomas are a heterogeneous group of diseases that can be further characterized based on their histology, biomarkers, and molecular profiles. These characteristics, gathered during disease staging, provide crucial information with regard to treatment decisions. Staging has evolved from informing the operability of breast tumors to providing prognostic information, and consequently helping establish local and systemic treatment guidelines. This chapter provides a succinct overview of breast cancer staging and treatment. Topics covered include the histological classification of breast cancers, as well as classification by tumor size and location, lymph node involvement, and metastatic involvement. The topic of molecular assays for prognostic information is reviewed. Finally, current treatment paradigms, including surgery, radiation, and chemotherapy regimens for different types of breast cancer, are discussed.
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WHO Classification of Tumours Editorial Board. DEFAULT_SET : Breast Tumours: WHO Classification of Tumours. World Health Organization, 2019.

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L, Leong Stanley P., ed. Atlas of selective sentinel lymphadenectomy for melanoma, breast cancer, and colon cancer. Boston: Kluwer Academic Publishers, 2002.

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Leong, Stanley P. L. Atlas of Selective Sentinel Lymphadenectomy for Melanoma, Breast Cancer and Colon Cancer. Springer London, Limited, 2006.

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Leong, Stanley P. L. Atlas of Selective Sentinel Lymphadenectomy for Melanoma, Breast Cancer and Colon Cancer. Springer, 2013.

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Springer-Verlag. Histological Typing of Breast Tumours (International histological classification of tumours). 2nd ed. Springer-Verlag, 1998.

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Leong, Stanley P. L. Atlas of Selective Sentinel Lymphadenectomy for Melanoma, Breast Cancer and Colon Cancer (Cancer Treatment and Research). Springer, 2002.

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Book chapters on the topic "Breast Tumors Classification"

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Varela, C., N. Karssemeijer, J. M. Muller, and P. G. Tahoces. "Classification of Breast Tumors in Digitized Mammograms." In Digital Mammography, 382–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-59327-7_89.

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Cavalli, Luciane R., and Iglenir J. Cavalli. "Molecular Classification and Prognostic Signatures of Breast Tumors." In Oncoplastic and Reconstructive Breast Surgery, 129–38. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-62927-8_8.

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Cavalli, Luciane R., and Iglenir J. Cavalli. "Molecular Classification and Prognostic Signatures of Breast Tumors." In Oncoplastic and Reconstructive Breast Surgery, 55–62. Milano: Springer Milan, 2013. http://dx.doi.org/10.1007/978-88-470-2652-0_5.

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Mo, Wanying, Yuntao Zhu, and Chaoyun Wang. "A Method for Localization and Classification of Breast Ultrasound Tumors." In Lecture Notes in Computer Science, 564–74. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53956-6_52.

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Varela, Celia, Nico Karssemeijer, and Pablo G. Tahoces. "Classification of Breast Tumors on Digital Mammograms Using Laws’ Texture Features." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001, 1391–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45468-3_241.

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Glaßer, Sylvia, Sophie Roscher, and Bernhard Preim. "Adapted Spectral Clustering for Evaluation and Classification of DCE-MRI Breast Tumors." In Informatik aktuell, 198–203. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54111-7_39.

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Glaßer, Sylvia, Uli Niemann, Uta Preim, Bernhard Preim, and Myra Spiliopoulou. "Classification of Benign and Malignant DCE-MRI Breast Tumors by Analyzing the Most Suspect Region." In Bildverarbeitung für die Medizin 2013, 45–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36480-8_10.

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Zhang, Qiangzhi, Huali Chang, Longzhong Liu, Anhua Li, and Qinghua Huang. "A Computer-Aided System for Classification of Breast Tumors in Ultrasound Images via Biclustering Learning." In Communications in Computer and Information Science, 24–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45652-1_3.

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Tzalavra, Alexia, Kalliopi Dalakleidi, Evangelia I. Zacharaki, Nikolaos Tsiaparas, Fotios Constantinidis, Nikos Paragios, and Konstantina S. Nikita. "Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based on DCE-MRI." In Machine Learning in Medical Imaging, 296–304. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47157-0_36.

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Hermanek, P., and L. H. Sobin. "Breast Tumours (ICD-O 174)." In TNM Classification of Malignant Tumours, 93–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-82982-6_7.

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Conference papers on the topic "Breast Tumors Classification"

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Anparasy, S. "Classification of Breast cancer tumors using Feature Selection and CNN." In ERU Symposium 2021. Engineering Research Unit (ERU), University of Moratuwa, 2021. http://dx.doi.org/10.31705/eru.2021.11.

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Breast cancer is one of the most dangerous diseases in the world and almost two million new cases are diagnosed every year. It starts from the breasts tissue and then spreads to other parts of the body. Early detection of breast cancer is important to save the life of a woman as it is related with a risen number of available treatment options. Benign and malignant are the major types of tumors and they are cancerous and non-cancerous, respectively. Benign is not dangerous since it does not destroy the nearby tissues and cannot spread or grow. Malignant tumor invades neighbouring tissues, blood vessels and spreads to other parts of the body by metastasis. Therefore, differentiating malignant from benign will help to detect breast cancer in its early stage. Nowadays, machine learning techniques are used to classify the tumor types hence the quality of lift is increased.
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Souto, Lizianne P. Marques, Thiago K. L. Dos Santos, and Marcelino Pereira S. Silva. "Classification of Breast Tumors Through Image Mining Techniques." In XVIII Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/sbcas.2018.3667.

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Da Silva, Valesca J. S., Mateus M. R. Da Silva, Marcelino P. S. Silva, and Joana R. C. Nogueira. "BI-RADS Breast Tumor Classification Through Image Mining." In VII Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/kdmile.2019.8791.

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In this article, a computer aided diagnostic system for BI-RADS classification of breast cancer is proposed. The approach involves image processing capabilities to extract features from tumors in mammography and image mining to classify them as BI-RADS 2, BI-RADS 3, BI-RADS 4C or BI-RADS 5. Images from the BCDR repository were used for the experiments. The results showed the efficacy of the proposed method, which classified tumors with considerable accuracy in four BI-RADS categories.
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Mansani, Fabio Postiglione, Mariane Marcelino Fernandes, Mario Rodrigues Montemor Netto, and Cristiane da Costa Bandeira Abrahão Nimir. "COMPARATIVE ANALYSIS BETWEEN IMMUNOHISTOCHEMISTRY PATHOLOGICAL SUBTYPING AND MAMMAPRINT® GENETIC SIGNATURE IN PATIENTS WITH BREAST CANCER IN BRAZIL: A PILOT STUDY." In Abstracts from the Brazilian Breast Cancer Symposium - BBCS 2021. Mastology, 2021. http://dx.doi.org/10.29289/259453942021v31s2098.

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Introduction: Immunohistochemistry, in breast cancer samples, measures the expression of biomarkers such as estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki67. Using the positivity or negativity of the receptors and the Ki67 value, this method, along with the histological results, allows the doctors to classify the tumors into four types as follows: Luminal A, Luminal B, HER2, and basal/triple negative. Genetic signature is a tool involving in numerous studies in this area; however owing to the difficulty of access to the tests, its usefulness is still limited. MammaPrint® was the first test approved by the Food and Drugs Administration (FDA) in 2007 to measure prognostic value associated with breast cancer recurrence and classify patients with breast cancer into “low risk” or “high risk” of developing metastases within the first 10 years after diagnosis and elucidates the patient’s need for adjuvant chemotherapy. It categorizes tumors into subtypes based on biological homogeneity. This study aims to analyze the concordance between the results of immunohistochemistry pathological subtyping and MammaPrint®, which is accompanied by BluePrint®, for the classification and stratification of luminal breast cancer. Material and Methods: Data were collected from the medical records of 19 patients in the Instituto Sul Paranaense de Oncologia (ISPON) who presented immunohistochemistry and genetic test compatible with luminal tumors. Immunohistochemistry was evaluated through hormone receptors, HER2 and mainly Ki67, as defined by the 2013 St. Gallen guidelines (50% of the sample were centrally assessed). For classification by the genetic test, BluePrint® provided the molecular subtype data and MammaPrint® stratified the risk, establishing Luminal A tumors as low risk and Luminal B as high risk. The concordance between the immunohistochemical classification and the genetic test was evaluated with the nonparametric McNemar-Bowker test. The Ki67 cutoff value predictive for recurrence risk compared with MammaPrint® was accessed by the ROC curve. Results: The results showed that, on one side, only 33.3% of patients classified as Luminal A by immunohistochemistry were also classified by the genetic signature as Luminal A. On the other side, on the tumors classified as Luminal B, 60% presented agreement between the classifications. Overall agreement among the tests was 47.3%. The cutoff value found for Ki67 predictive of tumor recurrence risk was ≤5, with a sensitivity of 100% and a specificity of 33%. The agreement between hormonal receptors and HER2 with BluePrint® was 100%. Conclusion: This study provides preliminary data regarding the prognostic and predictive value of genetic and molecular tests — represented by MammaPrint®/BluePrint® and immunohistochemistry—in a sample of Brazilian population, evidencing a discrepancy between the methods. The cutoff value of Ki67 predictive for recurrence risk remains under discussion, since there is no standardization of its measurement methodology. As a result, new studies could be developed, with larger and multicentric samples.
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Hettich, David, Megan Olson, Andie Jackson, and Naima Kaabouch. "Breast Cancer: Classification of Tumors Using Machine Learning Algorithms." In 2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). IEEE, 2021. http://dx.doi.org/10.1109/civemsa52099.2021.9493583.

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Syed Abdaheer, M., and E. Khan. "Shape based classification of breast tumors using fractal analysis." In 2009 International Multimedia, Signal Processing and Communication Technologies (IMPACT-2009). IEEE, 2009. http://dx.doi.org/10.1109/mspct.2009.5164228.

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Abdaheer, M. S., and Ekram Khan. "Automatic classification of breast tumors using circularly approximated contour." In 2011 International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT). IEEE, 2011. http://dx.doi.org/10.1109/mspct.2011.6150498.

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Xiao, Yi, Kuan Huang, Sihua Niu, and Jianhua Huang. "Interpretable Fine-grained BI-RADS Classification of Breast Tumors." In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021. http://dx.doi.org/10.1109/embc46164.2021.9630131.

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Song, Mingue, and Yanggon Kim. "Deep Representation for the Classification of Ultrasound Breast Tumors." In 2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM). IEEE, 2022. http://dx.doi.org/10.1109/imcom53663.2022.9721796.

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Acevedo, Pedro, and Monica Vazquez. "Classification of Tumors in Breast Echography Using a SVM Algorithm." In 2019 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2019. http://dx.doi.org/10.1109/csci49370.2019.00128.

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