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dc.contributor.author | Martínez Díaz, Rafael | es_ES |
dc.contributor.author | Balaguer, J. | es_ES |
dc.contributor.author | Sánchez Ruiz, Luis Manuel | es_ES |
dc.contributor.author | Bello, P. | es_ES |
dc.contributor.author | Castel, V. | es_ES |
dc.contributor.author | Peris Fajarnes, Guillermo | es_ES |
dc.date.accessioned | 2014-06-04T10:13:44Z | |
dc.date.available | 2014-06-04T10:13:44Z | |
dc.date.issued | 2013 | |
dc.identifier.issn | 1085-3375 | |
dc.identifier.uri | http://hdl.handle.net/10251/37913 | |
dc.description.abstract | Nonlinear dynamics of cancer recurrence are known to be governed by several factors as initial tumour size, number of metastatic sites, or quantity of drug resistant cells. The precise extent and location of tumours are very important factors so quantitative and consistent methods of evaluation are needed to assess reponse to patient therapy.Whole-body 123I-metaiodobenzylguanidine (mIBG) scintigraphy is used as primary medical image modality to detect neuroblastoma tumours due to its high specificity and sensitivity.However, current oncological guidelines are based on qualitative observer-dependent analysis. This fact makes it difficult to compare results of scintigraphies taken at different moments during therapy or at different institutions. In this paper, we review analytical methods used in neuroblastoma detection and propose an observer-independent method to quantitatively analyse a 123I-mIBG scintigraphy. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Hindawi Publishing Corporation | es_ES |
dc.relation.ispartof | Abstract and Applied Analysis | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | CITG | es_ES |
dc.subject | Neuroblastoma Detection | es_ES |
dc.subject | Non-linear methods | es_ES |
dc.subject.classification | EXPRESION GRAFICA EN LA INGENIERIA | es_ES |
dc.subject.classification | MATEMATICA APLICADA | es_ES |
dc.title | On Analytical Methods in Neuroblastoma Detection | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1155/2013/341346 | |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica | es_ES |
dc.description.bibliographicCitation | Martínez Díaz, R.; Balaguer, J.; Sánchez Ruiz, LM.; Bello, P.; Castel, V.; Peris Fajarnes, G. (2013). On Analytical Methods in Neuroblastoma Detection. Abstract and Applied Analysis. 2013:1-5. doi:10.1155/2013/341346 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 5 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 2013 | es_ES |
dc.relation.senia | 252266 | |
dc.description.references | Foo, J., & Leder, K. (2013). Dynamics of cancer recurrence. The Annals of Applied Probability, 23(4), 1437-1468. doi:10.1214/12-aap876 | es_ES |
dc.description.references | Bellomo, N., & Delitala, M. (2008). From the mathematical kinetic, and stochastic game theory to modelling mutations, onset, progression and immune competition of cancer cells. Physics of Life Reviews, 5(4), 183-206. doi:10.1016/j.plrev.2008.07.001 | es_ES |
dc.description.references | Bianca, C., & Delitala, M. (2011). On the modelling of genetic mutations and immune system competition. Computers & Mathematics with Applications, 61(9), 2362-2375. doi:10.1016/j.camwa.2011.01.024 | es_ES |
dc.description.references | Cattani, C., & Ciancio, A. (2012). Separable Transition Density in the Hybrid Model for Tumor-Immune System Competition. Computational and Mathematical Methods in Medicine, 2012, 1-6. doi:10.1155/2012/610124 | es_ES |
dc.description.references | Cattani, C., Ciancio, A., & d’ Onofrio, A. (2010). Metamodeling the learning–hiding competition between tumours and the immune system: A kinematic approach. Mathematical and Computer Modelling, 52(1-2), 62-69. doi:10.1016/j.mcm.2010.01.012 | es_ES |
dc.description.references | Mueller, W. P., Coppenrath, E., & Pfluger, T. (2012). Nuclear medicine and multimodality imaging of pediatric neuroblastoma. Pediatric Radiology, 43(4), 418-427. doi:10.1007/s00247-012-2512-1 | es_ES |
dc.description.references | Bombardieri, E., Giammarile, F., Aktolun, C., Baum, R. P., Bischof Delaloye, A., Maffioli, L., … Chiti, A. (2010). 131I/123I-Metaiodobenzylguanidine (mIBG) scintigraphy: procedure guidelines for tumour imaging. European Journal of Nuclear Medicine and Molecular Imaging, 37(12), 2436-2446. doi:10.1007/s00259-010-1545-7 | es_ES |
dc.description.references | Matthay, K. K., Shulkin, B., Ladenstein, R., Michon, J., Giammarile, F., Lewington, V., … Cohn, S. L. (2010). Criteria for evaluation of disease extent by 123I-metaiodobenzylguanidine scans in neuroblastoma: a report for the International Neuroblastoma Risk Group (INRG) Task Force. British Journal of Cancer, 102(9), 1319-1326. doi:10.1038/sj.bjc.6605621 | es_ES |
dc.description.references | MAISEY, M. N., NATARAJAN, T. K., HURLEY, P. J., & WAGNER, H. N. (1973). Validation of a Rapid Computerized Method of Measuring99mTc Pertechnetate Uptake for Routine Assessment of Thyroid Structure and Function1. The Journal of Clinical Endocrinology & Metabolism, 36(2), 317-322. doi:10.1210/jcem-36-2-317 | es_ES |
dc.description.references | Chen, W., Cao, Q., & Dilsizian, V. (2011). Variation of Heart-to-Mediastinal Ratio in 123I-mIBG Cardiac Sympathetic Imaging: Its Affecting Factors and Potential Corrections. Current Cardiology Reports, 13(2), 132-137. doi:10.1007/s11886-010-0157-y | es_ES |