Abstract
Early prediction of cancer reoccurrence constitutes a challenge for oncologists and surgeons. This chapter describes one ongoing experience, the EU-Project NeoMark, where scientists from different medical and biology research fields joined efforts with Information Technology experts to identify methods and algorithms that are able to early predict the reoccurrence risk for one of the most devastating tumors, the oral cavity squamous cell carcinoma (OSCC). The challenge of NeoMark is to develop algorithms able to identify a “signature” or bio-profile of the disease, by integrating multiscale and multivariate data from medical images, genomic profile from tissue and circulating cells RNA, and other medical parameters collected from patients before and after treatment. A limited number of relevant biomarkers will be identified and used in a real-time PCR device for early detection of disease reoccurrence.
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B. Boyle, P. Levin, editor. World Cancer Report. International Agency for Research on Cancer, 2008.
S. Steger, M. Erdt, G. Chiari, and G. Sakas. Feature extraction from medical images for an oral cancer reoccurrence prediction environment. In World Congress on Medical Physics and Biomedical Engineering, 2009.
F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens. Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging, 16(2):187–198, 1997.
W. M. Wells, P. Viola, H. Atsumi, S. Nakajima, and R. Kikinis. Multi-modal volume registration by maximization of mutual information. Med Image Anal, 1(1):35–51, Mar 1996.
D. M. Honea, G. Yaorong, W. E. Snyder, P. F. Hemler, and D. J. Vining. Lymph node segmentation using active contours. volume 3034, pages 265–273. SPIE, 1997.
D. Maleike, M. Fabel, R. Tetzlaff, H. von Tengg-Kobligk, T. Heimann, H-P. Meinzer, and I. Wolf. Lymph node segmentation on CT images by a shape model guided deformable surface method. volume 6914, page 69141S. SPIE, 2008.
J. Rogowska, K. Batchelder, G. S. Gazelle, E. F. Halpern, W. Connor, and G. L. Wolf. Evaluation of selected two-dimensional segmentation techniques for computed tomography quantitation of lymph nodes. Invest Radiol, 31(3):138–145, Mar 1996.
G. Unal, G. Slabaugh, A. Ess, A. Yezzi, T. Fang, J. Tyan, M. Requardt, R. Krieg, R. Seethamraju, M. Harisinghani, and R. Weissleder. Semi-automatic lymph node segmentation in ln-mri. In Proc. IEEE International Conference on Image Processing, pages 77–80, 8–11 Oct. 2006.
J. Dornheim, H. Seim, B. Preim, I. Hertel, and G. Strauss. Segmentation of neck lymph nodes in CT datasets with stable 3d mass-spring models segmentation of neck lymph nodes. Acad Radiol, 14(11):1389–1399, Nov 2007.
G. Barequet and S. Har-peled. Efficiently approximating the minimum-volume bounding box of a point set in three dimensions. J Algorithms, 38:82–91.
R. V. P. Hutter, M. Klimpfinger, L. H. Sobin, C. Wittekind, F. L. Greene, editor. TNM Atlas. Springer, Berlin, 5th edition, 2007.
G. H. John and R. Kohavi. Wrappers for feature subset selection. Artif Intell, 97:273–324, 1997.
V. Kumar, P.-N. Tan, M. Steinbach. Introduction to data mining. Pearson Addison Wesley, Boston, 1st edition, 2006.
K. P. Murphy. Dynamic bayesian netoworks: Representation, inference and learning. University of California, 2002.
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Picone, M. et al. (2011). Enabling Heterogeneous Data Integration and Biomedical Event Prediction Through ICT: The Test Case of Cancer Reoccurrence. In: Arabnia, H., Tran, QN. (eds) Software Tools and Algorithms for Biological Systems. Advances in Experimental Medicine and Biology, vol 696. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7046-6_37
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