Добірка наукової літератури з теми "Breast Tumors Classification"
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Статті в журналах з теми "Breast Tumors Classification"
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.
Повний текст джерелаSTEPONAVIČIENĖ, 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.
Повний текст джерелаZhuang, 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.
Повний текст джерелаMENUT, 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.
Повний текст джерелаNg, 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.
Повний текст джерелаGuseynov, 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.
Повний текст джерелаKrasnoslobodtsev, 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.
Повний текст джерелаOuyang, 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.
Повний текст джерелаTran 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.
Повний текст джерелаMuhtadi, 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.
Повний текст джерелаДисертації з теми "Breast Tumors Classification"
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.
Повний текст джерелаGuo, 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.
Повний текст джерелаPurvis, 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.
Повний текст джерелаSundqvist, 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.
Повний текст джерелаIn 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
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.
Повний текст джерелаHintergrund: 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.
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.
Повний текст джерелаZiel: 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.
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/.
Повний текст джерелаIntroduction: 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
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.
Повний текст джерелаKennedy, 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.
Повний текст джерелаHabla, 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.
Повний текст джерелаКниги з теми "Breast Tumors Classification"
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.
Знайти повний текст джерелаLeong, Stanley P. L. Atlas of Selective Sentinel Lymphadenectomy for Melanoma, Breast Cancer and Colon Cancer. Cleveland: Kluwer Academic Publishers, 2003.
Знайти повний текст джерелаSobin, Leslie H., and Christian Wittekind. TNM Classification of Malignant Tumours: Breast and Gynaecological Tumours. Wiley & Sons, Incorporated, John, 2008.
Знайти повний текст джерелаPrati, 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.
Повний текст джерелаWHO Classification of Tumours Editorial Board. DEFAULT_SET : Breast Tumours: WHO Classification of Tumours. World Health Organization, 2019.
Знайти повний текст джерелаL, Leong Stanley P., ed. Atlas of selective sentinel lymphadenectomy for melanoma, breast cancer, and colon cancer. Boston: Kluwer Academic Publishers, 2002.
Знайти повний текст джерелаLeong, Stanley P. L. Atlas of Selective Sentinel Lymphadenectomy for Melanoma, Breast Cancer and Colon Cancer. Springer London, Limited, 2006.
Знайти повний текст джерелаLeong, Stanley P. L. Atlas of Selective Sentinel Lymphadenectomy for Melanoma, Breast Cancer and Colon Cancer. Springer, 2013.
Знайти повний текст джерелаSpringer-Verlag. Histological Typing of Breast Tumours (International histological classification of tumours). 2nd ed. Springer-Verlag, 1998.
Знайти повний текст джерелаLeong, Stanley P. L. Atlas of Selective Sentinel Lymphadenectomy for Melanoma, Breast Cancer and Colon Cancer (Cancer Treatment and Research). Springer, 2002.
Знайти повний текст джерелаЧастини книг з теми "Breast Tumors Classification"
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.
Повний текст джерелаCavalli, 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.
Повний текст джерелаCavalli, 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.
Повний текст джерелаMo, 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.
Повний текст джерелаVarela, 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.
Повний текст джерелаGlaßer, 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.
Повний текст джерелаGlaßer, 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.
Повний текст джерелаZhang, 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.
Повний текст джерелаTzalavra, 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.
Повний текст джерелаHermanek, 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.
Повний текст джерелаТези доповідей конференцій з теми "Breast Tumors Classification"
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.
Повний текст джерелаSouto, 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.
Повний текст джерелаDa 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.
Повний текст джерелаMansani, 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.
Повний текст джерелаHettich, 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.
Повний текст джерелаSyed 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.
Повний текст джерелаAbdaheer, 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.
Повний текст джерелаXiao, 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.
Повний текст джерелаSong, 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.
Повний текст джерелаAcevedo, 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.
Повний текст джерела