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Texture analysis in perfusion images of prostate cancer—A case study

, Investigative Radiology   28 (suppl 5): S72-S77. Bradford, T., Tomlins, S., Wang, X. and Chinnaiyan, A. (2006). Molecular markers of prostate cancer, Urologic Oncology   24 (6): 538-551. Cenic, A., Nabavi, D., Craen, R., Gelb, A. and Lee, T.-Y. (2000). A CT method to measure hemodynamics in brain tumors: Validation and application of cerebral blood flow maps, American Journal of Neuroradiology   21 (3): 462-470. Charlesworth, P. and Harris, A. (2006). Mechanisms of disease: Angiogenesis in

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Data mining methods for gene selection on the basis of gene expression arrays

Mathematics and Computer Science 11(3): 565-582. Tan, P.N., Steinbach, M. and Kumar, V. (2006). Introduction to Data Mining, Pearson Education, Boston, MA. Vanderbilt (2002). Data base of prostate cancer, Vanderbilt University, Vert, J. (2007). Kernel methods in genomics and computational biology, in G. Camps-Valls, J.L. Rojo-Alvarez and M. Martinez-Ramon (Eds.), Kernel Methods in Bioengineering, Signal and Image Processing, Idea Group, London, pp. 42

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Machine learning techniques combined with dose profiles indicate radiation response biomarkers

., Delobel, J.-B., De Crevoisier, R. and Acosta, O. (2015). On feature extraction and classification in prostate cancer radiotherapy using tensor decompositions, Medical Engineering and Physics 37 (1): 126–131. Finnon, P., Kabacik, S., MacKay, A., Raffy, C., AHern, R., Owen, R., Badie, C., Yarnold, J. and Bouffler, S. (2012). Correlation of in vitro lymphocyte radiosensitivity and gene expression with late normal tissue reactions following curative radiotherapy for breast cancer, Radiotherapy and Oncology 105 (3): 329–336. Francescatto, M., Chierici, M

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Survival analysis on data streams: Analyzing temporal events in dynamically changing environments

, Spain . Yang, Y., Pierce, T. and Carbonell, J.G. (1998). A study of retrospective and on-line event detection, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1998), Melbourne, Australia , pp. 28-36. Zadeh, L. (1965). Fuzzy sets, Information and Control 8 (3): 338-353. Zupan, B., Demˇsar, J., Kattan, M.W., Beck, J.R. and Bratko, I. (2000). Machine learning for survival analysis: A case study on recurrence of prostate cancer, Artificial

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Fusion of clinical data: A case study to predict the type of treatment of bone fractures

-based data fusion and its application to protein function prediction in yeast, Pacific Symposium on Biocomputing (PSB 2004), Big Island, HI, USA , pp. 300–311. Lee, G., Doyle, S., Monaco, J., Madabhushi, A., Feldman, M.D., Master, S.R. and Tomaszewski, J.E. (2009). A knowledge representation framework for integration, classification of multi-scale imaging and non-imaging data: Preliminary results in predicting prostate cancer recurrence by fusing mass spectrometry and histology, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA

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From the slit-island method to the Ising model: Analysis of irregular grayscale objects

images of prostate cancer-A case study, International Journal of Applied Mathematics and Computer Science 20(1): 149-156, DOI: 10.2478/v10006-010-0011-9. Solomon, D. and Nayar, R. (2004). The Bethesda System for Reporting Cervical Cytology, Springer, New York, NY. Steven, I. (1993). Linear Richardson plots from non-fractal data sets, Dutch Mathematical Geology 25(6): 737-751. Styer, D. (2007). Statistical Mechanics, Oberlin College, Oberlin. Sun, W. (2006). Three new implementations of the triangular prism

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Nuclei segmentation for computer-aided diagnosis of breast cancer

marching algorithm, Signal Processing: Image Communication 16(10): 963-976. ´Smieta´nski, J., Tadeusiewicz, R. and Łuczy´nska, E. (2010). Texture analysis in perfusion images of prostate cancer- A case study, International Journal of Applied Mathematics and Computer Science 20(1): 149-156, DOI: 10.2478/v10006-010-0011-9. Steć, P. (2005). Segmentation of Colour Video Sequences Using the Fast Marching Method, University of Zielona Góra Press, Zielona Góra. Tang, X. (1998). Texture information in run-length matrices, IEEE Transactions on

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Improvement of the Fast Clustering Algorithm Improved by K-Means in the Big Data

) and contained measurements from biopsies of 30 DLBCL patients. Each sample was stained with three antibodies,: CD3, CD5, and CD19. The LunG data [ 44 ] consists of d = 1,000 dimensions with n = 197 lung cancer patients from k = 4 different factors. The Prostate data [ 45 ] consists of d = 12,600 dimensions with n = 102 prostate cancer patients from k = 2 different factors. The original data set from contained 97 men who haved prostate cancer and recorded the information of the patients. Table 3 The date information in the algorithm test

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Applied mathematics and nonlinear sciences in the war on cancer

.g. [ 55 , 56 ] for brain tumors). From the mathematical point of view it is necessary to define meaningful and robust measures of volumes, geometries, textures, etc., to be computed on images providing biomarkers of clinical relevance. Also, images are to be used as initial data for predictive mathematical algorithms able to simulate tumor evolution and provide therapy personalization. Another source of information are blood tests, which allow clinically relevant biomarkers (such as e.g. PSA levels in prostate cancer) to be directly measured. In recent years the

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