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Assessing Post-Radiotherapy Treatment Involving Brain Volume Differences in Children: An Application of Adaptive Systems Methodology

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Data Mining Applications Using Artificial Adaptive Systems

Abstract

Brain volume differences from 58 children are analyzed to determine the degree of volume loss and the effect on IQ after undergoing radiotherapy treatment for tumors in an effort to identify relationships that might yield knowledge in preventing brain volume loss in future treatments. Analysis of the pre- and post-treatment data is performed first using traditional statistics and then with the assistance of a new kind of artificial adaptive system called the Activation and Competition System (ACS) and Auto-contractive Map (Auto-CM). While the result of the statistical study suggests that it is not possible to linearly classify the subjects into subsets of higher and lower IQ, the ACS clearly delineates the dataset into two IQ groups. Further, Auto-CM allows us to establish a semantic connection map among different brain segments which indicate a possible interpretation rule in the observed results. The effect of radiation treatment on the nine brain segments is addressed and future research directions are introduced.

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References

  • Buscema M (1998a) MetaNet: the theory of independent judges. In: Substance use & misuse, vol 33(2), pp 43461 (Models). Marcel Dekker, Inc., New York

    Google Scholar 

  • Buscema M (1998b) Recirculation neural networks. In: Substance use & misuse, vol 33(2), pp 383–388 (Models). Marcel Dekker, Inc., New York

    Google Scholar 

  • Buscema M (2000a) Constraints satisfaction networks. Semeion software #14, v. 12.5, Rome, 2000–2009

    Google Scholar 

  • Buscema M (2000b) Supervised ANNs and organism. Semeion software #12, v. 16, Rome, 2000–2010

    Google Scholar 

  • Buscema M (2007a) Meta Net. Semeion software no. 44, v.8.0, Rome, 2007–2010

    Google Scholar 

  • Buscema M (2007b) Squashing theory and contractive map network. Semeion technical paper #32, Rome

    Google Scholar 

  • Buscema M (2008a) MST. Software for programming graphs from artificial networks. Semeion software #38, Rome, v 6.0, 2008–2009

    Google Scholar 

  • Buscema M (2008b) Supervised auto-associative ANNs. Semeion software #50, version 3.5, 2008–2010

    Google Scholar 

  • Buscema M (2009a) Activation and competition system. Mimeo, Semeion, Rome

    Google Scholar 

  • Buscema M (2009b) Adaptive learning quantization. Mimeo, Semeion, Rome

    Google Scholar 

  • Buscema M (2009c) Modular auto-associative ANNs, Ver 10.0. Semeion software #51, Rome, 2009–2010

    Google Scholar 

  • Buscema M, Grossi E (2008) The semantic connectivity map: an adapting self-organizing knowledge discovery method in data bases. Experience in gastro-oesophageal reflux disease. Int J Data Min Bioinf 2(4):362–404

    Google Scholar 

  • Buscema M, Grossi E, Intraligi M, Garbagna N, Andriulli A, Breda M (2005) An optimized experimental protocol based on neuro-evolutionary algorithms: application to the classification of dyspeptic patients and to the prediction of the effectiveness of their treatment. Artif Intell Med 34:279–305

    Article  Google Scholar 

  • Buscema M, Breda M, Terzi S (2006a) A feed forward sine based neural network for functional approximation of a waste incinerator emissions. Proceedings of the 8th WSEAS international conference on automatic control, modeling and simulation, Praga

    Google Scholar 

  • Buscema M, Breda M, Terzi S (2006b) Using sinusoidal modulated weights improve feed-forward neural network performances in classification and functional approximation problems. WSEAS Trans Inf Sci Appl 5(3):885–893

    Google Scholar 

  • Buscema M, Terzi S, Maurelli G, Capriotti M and Carlei V (2006) The smart library architecture of an orientation portal. In: Quality & quantity. Springer, Netherland, vol 40, pp 911–933

    Google Scholar 

  • Buscema M, Grossi E, Snowdon D, Antuono P (2008a) Auto-contractive maps: an artificial adaptive system for data mining: an application to Alzheimer disease. Curr Alzheimer Res 5:481–498

    Article  Google Scholar 

  • Buscema M, Helgason C and Grossi E (2008) Auto contractive maps, H function and maximally regular graph: theory and applications. Special session on Artificial adaptive systems in medicine: applications in the real world, NAFIPS 2008 (IEEE), New York

    Google Scholar 

  • Chauvin Y, Rumelhart DE (eds) (1995) Backpropagation: theory, architectures, and applications. Lawrence Erlbaum Associates, Inc. Publishers, New Jersey

    Google Scholar 

  • Dale AM, Sereno MI (1993) Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. J Cogn Neurosci 5(2):162–176

    Article  Google Scholar 

  • Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis I: segmentation and surface reconstruction. Neuroimage 9:179–194

    Article  Google Scholar 

  • Day WHE (1988) Consensus methods as tools for data analysis. In: Bock HH (ed) Classification and related methods for data analysis. North-Holland, Amsterdam, pp 312–324

    Google Scholar 

  • Diappi L, Bolchim P, Buscema M (2004) Improved understanding of urban sprawl using neural networks. In: Van Leeuwen JP, Timmermans HJP (eds) Recent advances in design and decision support systems in architecture and urban planning. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  • Fischl B, Salat D, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM (2002) Whole brain segmentation. Automated labeling of neuroanatomical structures in the human brain. Neuron 33(3):341–355

    Article  Google Scholar 

  • Fischl B, Salat DH, van der Kouwe AJ, Makris N, Segonne F, Quinn BT, Dale AM (2004) Sequence-independent segmentation of magnetic resonance images. Neuroimage 23(Suppl 1):S69–S84

    Article  Google Scholar 

  • Grossberg S (1976) Adaptive pattern classification and universal recording: Part I. Parallel development and coding of neural feature detectors. Biol Cybern 23:121–134

    Article  MathSciNet  MATH  Google Scholar 

  • Grossberg S (1978) A theory of visual coding, memory, and development. In: Leeuwenberg J, Buffart HFJ (eds) Formal theories of visual perception. Wiley, New York

    Google Scholar 

  • Grossberg S (1981) How does the brain build a cognitive code? Psychol Rev 87:1–51

    Article  Google Scholar 

  • Harila-Saari AH, Paakko EL, Vainionpaa LK, Pyhtinen J, Lanning BM (1998) A longitudinal magnetic resonance imaging study of the brain in survivors in childhood acute lymphoblastic leukemia. Cancer 83(12):2608–2617

    Article  Google Scholar 

  • Hertzberg H, Huk WJ, Ueberall MA, Langer T, Meier W, Dopfer R, Skalej M, Lackner H, Bode U, Janssen G, Zintl F, Beck JD (1997) CNS late effects after ALL therapy in childhood. Part I: Neuroradiological findings in long-term survivors of childhood ALL – an evaluation of the interferences between morphology and neuropsychological performance. The German Late Effects Working Group. Med Pediatr Oncol 28(6):387–400

    Article  Google Scholar 

  • Hinton GE, Anderson A (eds) (1981) Parallel models of associative memory. Erlbaum, Hillsdale, NJ

    Google Scholar 

  • Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79:2554–2558

    Article  MathSciNet  Google Scholar 

  • Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two-state neurons. Proc Natl Acad Sci USA 81:3088–3092

    Article  Google Scholar 

  • Ishikawa N, Tajima G, Yofune N, Nishimura S, Kobayashi M (2006) Moyamoya syndrome after cranial irradiation for bone marrow transplantation in a patient with acute leukemia. Neuropediatrics 37(6):364–366

    Article  Google Scholar 

  • Khong PL, Leung LH, Fung AS, Fong DYT, Qiu D, Kwong DLW, Ooi GC, McAlanon G, Cao G, Chan GCF (2006) White matter anisotropy in post-treatment childhood cancer survivors: preliminary evidence of association with neurocognitive function. J Clin Oncol 24(6):884–890

    Article  Google Scholar 

  • Kikuchi A, Maeda M, Hanada R, Okimoto Y, Ishimoto K, Kaneko T, Ikuta K, Tsuchida M (2007) Moyamoya syndrome following childhood acute lymphoblastic leukemia. Pediatr Blood Cancer 48(3):268–272

    Article  Google Scholar 

  • Kohonen T (1995) Self-organizing maps. Springer Verlag, Berlin

    Book  Google Scholar 

  • Kosko B (1992) Neural networks for signal processing. Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  • Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley, Hoboken, NJ

    Book  MATH  Google Scholar 

  • Laitt RD, Chambers EJ, Goddard PR, Wakeley CJ, Duncan AW, Foreman NK (1995) Magnetic resonance imaging and magnetic resonance angiography in long term survivors of acute lymphoblastic leukemia treated with cranial irradiation. Cancer 76(10):1846–1852

    Article  Google Scholar 

  • Leung LH, Ooi GC, Kwong DL, Chan GC, Cao G, Khong PL (2004) White-matter diffusion anisotropy after chemo-irradiation: a statistical parametric mapping study and histogram analysis. Neuroimage 21(1):261–268

    Article  Google Scholar 

  • Licastro F, Porcellini E, Chiappelli M, Forti P, Buscema M, Ravaglia G, Grossi E (2010) Multivariable network associated with cognitive decline and dementia. Int Neurobiol Aging 1(2):257–269

    Article  Google Scholar 

  • Liu AK, Marcus KJ, Fischl B, Grant PE, Poussaint TY, Rivkin MY, Davis P, Tarbell NJ, Yock TI (2007) Changes in cerebral cortex of children treated for medulloblastoma. Int J Radiat Oncol Biol Phys 68(4):992–998

    Article  Google Scholar 

  • Massini G (2007) Semantic connection map, Ver 2.0. Semeion software #45, Rome, 2007–2009

    Google Scholar 

  • McClelland JL, Rumelhart DE (1988) Explorations in parallel distributed processing: a handbook of models, programs, and exercises. Bradford Books, Cambridge, MA

    Google Scholar 

  • Mulhern RK, Reddick WE, Palmer SL, Glass JO, Elkin TD, Kun LE, Taylor J, Langston J, Gajjar A (1999) Neurocognitive deficits in medulloblastoma survivors and white matter loss. Ann Neurol 46(6):834–841

    Article  Google Scholar 

  • Nagel BJ, Palmer SL, Reddick WE, Glass JO, Helton KJ, Wu S, Xiong X, Kun LE, Gajjar A, Mulhern RK (2004) Abnormal hippocampal development in children with medulloblastoma treated with risk-adapted irradiation. AJNR Am J Neuroradiol 25(9):1575–1582

    Google Scholar 

  • Paakko E, Talvensaari K, Pyhtinen J, Lanning M (1994) Late cranial MRI after cranial irradiation in survivors of childhood cancer. Neuroradiology 36(8):652–655

    Article  Google Scholar 

  • Paakko E, Harila-Saari A, Vanionpaa L, Himanen S, Pyhtinen J, Lanning M (2000) White matter changes on MRI during treatment in children with acute lymphoblastic leukemia: correlation with neuropsychological findings. Med Pediatr Oncol 35(5):456–461

    Article  Google Scholar 

  • Poussaint TY, Siffert J, Barnes PD, Pomeroy SL, Goumnerova LC, Anthony DC, Sallan SE, Tarbell NJ (1995) Hemorrhagic vasculopathy after treatment of central nervous system neoplasia in childhood: diagnosis and follow-up. AJNR Am J Neuroradiol 16(4):693–699

    Google Scholar 

  • Reddick WE, White HA, Glass JO, Wheeler GC, Thompson SJ, Gajjar A, Leigh L, Mulhern RK (2003) Developmental model relating white matter volume to neurocognitive deficits in pediatric brain tumor survivors. Cancer 97(10):2512–2519

    Article  Google Scholar 

  • Reddick WE, Glass JO, Palmer SL, Wu S, Gajjar A, Langston LW, Kun LE, Xiong X, Mulhern RK (2005) Atypical white matter volume development in children following craniospinal irradiation. Neuro Oncol 7(1):12–19

    Article  Google Scholar 

  • Reddick WE, Shan ZY, Glass JO, Helton S, Xiong X, Wu S, Bonner MJ, Howard SC, Christensen R, Khan RB, Pui CH, Mulhern RK (2006) Smaller white-matter volumes are associated with larger deficits in attention and learning among long-term survivors of acute lymphoblastic leukemia. Cancer 106(4):941–949

    Article  Google Scholar 

  • Rumelhart DE, McClelland JL (eds) (1986) Parallel distributed processing, Vol. 1. Foundations, explorations in the microstructure of cognition, Vol. 2. Psychological and biological models. The MIT Press, Cambridge, MA

    Google Scholar 

  • Rumelhart DE, Smolensky P, McClelland JL, Hinton GE (1986) Schemata and sequential thought processes in PDP models. In: McClelland JL, Rumelhart DE (eds) PDP, exploration in the microstructure of cognition, vol II. The MIT Press, Cambridge, MA

    Google Scholar 

  • Segonne F, Dale AM, Busa E, Glessner M, Salat D, Kahn HK, Fischl B (2004) A hybrid approach to the skull stripping problem in MRI. Neuroimage 22(3):1060–1075

    Article  Google Scholar 

  • Ullrich NJ, Robertson R, Kinnamon DD, Scott RM, Kieran MW, Turner CD, Chi SN, Goumnerova L, Proctor M, Tarbell NJ, Marcus KJ, Pomeroy SL (2007) Moyamoya following cranial irradiation for primary brain tumors in children. Neurology 68(12):932–938

    Article  Google Scholar 

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Buscema, M., Newman, F., Massini, G., Grossi, E., Tastle, W.J., Liu, A.K. (2013). Assessing Post-Radiotherapy Treatment Involving Brain Volume Differences in Children: An Application of Adaptive Systems Methodology. In: Tastle, W. (eds) Data Mining Applications Using Artificial Adaptive Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4223-3_1

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  • DOI: https://doi.org/10.1007/978-1-4614-4223-3_1

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