Long-term efficacy and safety profiles of iris-fixated foldable anterior chamber phakic intraocular lens implantation in eyes with more than 10-years of follow-up

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Eradication of common pathogens at days 2, 3 and 4 of moxifloxacin therapy in patients with acute bacterial sinusitis

BACKGROUND: Acute bacterial sinusitis (ABS) is a common infection in clinical practice. Data on time to bacteriologic eradication after antimicrobial therapy are lacking for most agents, but are necessary in order to optimize therapy. This was a prospective, single-arm, open-label, multicenter study to determine the time to bacteriologic eradication in ABS patients (maxillary sinusitis) treated with moxifloxacin. METHODS: Adult patients with radiologically and clinically confirmed ABS received once-daily moxifloxacin 400 mg for 10 days. Middle meatus secretion sampling was performed using nasal endoscopy pre-therapy, and repeated on 3 consecutive days during treatment. Target enrollment was 30 bacteriologically evaluable patients (pre-therapy culture positive for Streptococcus pneumoniae, Haemophilus influenzae or Moraxella catarrhalis and evaluable cultures for at least Day 2 and Day 3 during therapy visits), including at least 10 each with S. pneumoniae or H. influenzae. RESULTS: Of 192 patients enrolled, 42 were bacteriologically evaluable, with 48 pathogens isolated. Moxifloxacin was started on Day 1. Baseline bacteria were eradicated in 35/42 (83.3%) patients by day 2, 42/42 (100%) patients by day 3, and 41/42 (97.6%) patients by day 4. In terms of individual pathogens, 12/18 S. pneumoniae, 22/23 H. influenzae and 7/7 M. catarrhalis were eradicated by day 2 (total 41/48; 85.4%), and 18/18 S. pneumoniae and 23/23 H. influenzae were eradicated by day 3. On Day 4, S. pneumoniae was isolated from a patient who had negative cultures on Days 2 and 3. Thus, the Day 4 eradication rate was 47/48 (97.9%). Clinical success was achieved in 36/38 (94.7%) patients at the test of cure visit. CONCLUSION: In patients with ABS (maxillary sinusitis), moxifloxacin 400 mg once daily for 10 days resulted in eradication of baseline bacteria in 83.3% of patients by Day 2, 100% by Day 3 and 97.6% by Day 4

Resveratrol Prevents Endothelial Cells Injury in High-Dose Interleukin-2 Therapy against Melanoma

Immunotherapy with high-dose interleukin-2 (HDIL-2) is an effective treatment for patients with metastatic melanoma and renal cell carcinoma. However, it is accompanied by severe toxicity involving endothelial cell injury and induction of vascular leak syndrome (VLS). In this study, we found that resveratrol, a plant polyphenol with anti-inflammatory and anti-cancer properties, was able to prevent the endothelial cell injury and inhibit the development of VLS while improving the efficacy of HDIL-2 therapy in the killing of metastasized melanoma. Specifically, C57BL/6 mice were injected with B16F10 cells followed by resveratrol by gavage the next day and continued treatment with resveratrol once a day. On day 9, mice received HDIL-2. On day 12, mice were evaluated for VLS and tumor metastasis. We found that resveratrol significantly inhibited the development of VLS in lung and liver by protecting endothelial cell integrity and preventing endothelial cells from undergoing apoptosis. The metastasis and growth of the tumor in lung were significantly inhibited by HDIL-2 and HDIL-2 + resveratrol treatment. Notably, HDIL-2 + resveratrol co-treatment was more effective in inhibiting tumor metastasis and growth than HDIL-2 treatment alone. We also analyzed the immune status of Gr-1+CD11b+ myeloid-derived suppressor cells (MDSC) and FoxP3+CD4+ regulatory T cells (Treg). We found that resveratrol induced expansion and suppressive function of MDSC which inhibited the development of VLS after adoptive transfer. However, resveratrol suppressed the HDIL-2-induced expansion of Treg cells. We also found that resveratrol enhanced the susceptibility of melanoma to the cytotoxicity of IL-2-activated killer cells, and induced the expression of the tumor suppressor gene FoxO1. Our results suggested the potential use of resveratrol in HDIL-2 treatment against melanoma. We also demonstrated, for the first time, that MDSC is the dominant suppressor cell than regulatory T cell in the development of VLS

Multiple mechanisms mediating carbon monoxide inhibition of the voltage-gated K+ channel Kv1.5

The voltage-gated K+ channel plays key roles in the vasculature and in atrial excitability, and contributes to apoptosis in various tissues. In this study, we have explored its regulation by carbon monoxide (CO), a product of the cytoprotective heme oxygenase enzymes, and a recognized toxin. CO inhibited recombinant Kv1.5 expressed in HEK293 cells in a concentration-dependent manner which involved multiple signalling pathways. CO inhibition was partially reversed by superoxide dismutase mimetics, and by suppression of mitochondrial reactive oxygen species. CO also elevated intracellular nitric oxide (NO) levels. Prevention of NO formation also partially reversed CO inhibition of Kv1.5, as did inhibition of soluble guanylyl cyclase. CO also elevated intracellular peroxynitrite levels, and a peroxynitrite scavenger markedly attenuated the ability of CO to inhibit Kv1.5. CO caused nitrosylation of Kv1.5, an effect which was also observed in C331A and C346A mutant forms of the channel, which had previously been suggested as nitrosylation sites within Kv1.5. Augmentation of Kv1.5 via exposure to hydrogen peroxide was fully reversed by CO. Native Kv1.5 recorded in HL-1 murine atrial cells was also inhibited by CO. Action potentials recorded in HL-1 cells were increased in amplitude and duration by CO, an effect mimicked and occluded by pharmacological inhibition of Kv1.5. Our data indicate that Kv1.5 is a target for modulation by CO via multiple mechanisms. This regulation has important implications for diverse cellular functions, including excitability, contractility and apoptosis

Heme oxygenase-1 regulates cell proliferation via carbon monoxide-mediated inhibition of T-type Ca2+ channels

Induction of the antioxidant enzyme heme oxygenase-1 (HO-1) affords cellular protection and suppresses proliferation of vascular smooth muscle cells (VSMCs) associated with a variety of pathological cardiovascular conditions including myocardial infarction and vascular injury. However, the underlying mechanisms are not fully understood. Over-expression of Cav3.2 T-type Ca2+ channels in HEK293 cells raised basal [Ca2+]i and increased proliferation as compared with non-transfected cells. Proliferation and [Ca2+]i levels were reduced to levels seen in non-transfected cells either by induction of HO-1 or exposure of cells to the HO-1 product, carbon monoxide (CO) (applied as the CO releasing molecule, CORM-3). In the aortic VSMC line A7r5, proliferation was also inhibited by induction of HO-1 or by exposure of cells to CO, and patch-clamp recordings indicated that CO inhibited T-type (as well as L-type) Ca2+ currents in these cells. Finally, in human saphenous vein smooth muscle cells, proliferation was reduced by T-type channel inhibition or by HO-1 induction or CO exposure. The effects of T-type channel blockade and HO-1 induction were non-additive. Collectively, these data indicate that HO-1 regulates proliferation via CO-mediated inhibition of T-type Ca2+ channels. This signalling pathway provides a novel means by which proliferation of VSMCs (and other cells) may be regulated therapeutically

Rowing against the wind: how do times of austerity shape academic entrepreneurship in unfriendly environments?

[EN] Academic spin-offs (ASOs) help universities transfer knowledge or technology through business projects developed by academic staff. This investigation aims at analyzing the critical factors for spin-off creation at universities operating in crisis-raven, entrepreneurship-unfriendly environments. Such factors revolve around four types of resources: environmental, institutional, organizational, and personal. Focusing on a Southern European context, as an example of an unfriendly environment affected by economic crisis, an entrepreneurial university (the Technical University of Valencia in Spain, UPV) is our research setting. Through a case study approach, we examine the potential of UPV as a springboard for ASOs. Our results show an adverse local environment, a rather favorable influence of institutional and organizational drivers, and a mixed role of personal factors. Our findings illustrate that UPV consistently supports spin-off creation due to a greater (rather positive) reflexivity from its institutional, organizational and personal resources than the (negative) imprinting of the unfriendly environment. This helps counter-balance the structural unfriendliness for academic entrepreneurship, and trigger a crisis-led risk-taking attitude by academic staff. Hence, UPV should continue with its current strategy of supporting academic entrepreneurship, and might transfer best practices to other universities also affected by unfavorable environmental conditions. Generally speaking, we would advise universities facing adverse circumstances to develop rules and mechanisms for academic entrepreneurship, carefully revise and improve malfunctions, and become involved throughout the whole process of spin-off development. All in all, our study advances understanding of how the different drivers for ASO creation can be revamped by universities located in unfriendly environments, having in mind the key role that universities play in fostering social and economic development through academic entrepreneurship in such environments.The authors would like to thank the Universitat Politecnica de Valencia (grant PAID-06-12-0916), and the Spanish Ministry of Economy and Competitiveness (grant ECO2011-29863), for their financial support for this research.Seguí-Mas, E.; Oltra, V.; Tormo-Carbó, G.; Sarrión Viñes, F. (2017). Rowing against the wind: how do times of austerity shape academic entrepreneurship in unfriendly environments?. International Entrepreneurship and Management Journal. 1-42. doi:10.1007/s11365-017-0478-zS142Acs, Z. J., Audretsch, D. B., & Lehmann, E. E. (2013). The knowledge spillover theory of entrepreneurship. Small Business Economics, 41, 757–774.Alemany, L. (2011). Libro blanco de la iniciativa emprendedora en España. Resource document. ISEAD. http://idl.isead.edu.es:8080/jspui/bitstream/123456789/859/1/658ALElib.pdf . Accessed 31 October 2015.Algieri, B., Aquino, A., & Succurro, M. (2013). Technology transfer offices and academic spin-off creation: the case of Italy. Journal of Technology Transfer, 38(4), 382–400.ARWU (2017). Academic Ranking of World Universities 2017. Resource document. http://www.shanghairanking.com/ARWU2017.html . Accesed 15 August 2017.Ashcroft, B., Holden, D., & Low, K. (2004). Potential entrepreneurs and the self employment choice decision. In Strathclyde Discussion papers in Economics, 4–16. Glasglow: University of Strathclyde.Autio, E., & Kauranen, I. (1994). Technologist-entrepreneurs versus nonentrepreneurial technologists: Analysis of motivational triggering factors. Entrepreneurship & Regional Development, 6, 315–328.Autio, E., Kenney, M., Mustar, P., Siegel, D., & Wright, M. (2014). Entrepreneurial innovation: The importance of context. Research Policy, 43, 1097–1108.Bonnacorsi, A., Colombo, M. G., Guerini, M., & Rossi-Lamastra, C. (2013). University specialization and new firm creation across industries. Small Business Economics, 41, 837–863.Bruneel, J., Van de Velde, E., & Clarysse, B. (2013). Impact of the type of corporate spin-off on growth. Entrepreneurship Theory and Practice, 37, 943–959.CampusHabitat5U (2017). International Campus of Excellence. Resource document. UPV. http://campushabitat5u.es/?lang=en . Accessed 5 October 2017.Chiesa, V., & Piccaluga, A. (2000). Exploitation and diffusion of public research: The chase of academic spin-offs companies in Italy. R&D Management, 30, 329–339.Clark, B. R. (1998). Creating entrepreneurial universities: Organizational pathways of transformation. New York: IAU Press.Clarysse, B., & Moray, N. (2004). A process study of entrepreneurial team formation: The case of research-based spin-off. Journal of Business Venturing, 19, 55–79.Cohen, M., Nelson, R., & Walsh, J. (2002). Links and impacts: The influence of public research on industrial R&D. Management Science, 48, 1–23.Creswell, J.W. & Clark, V. (2011). Designing and Conducting Mixed Methods Research. SAGE Publications.De Cleyn, S. H., Braet, J., & Klofsten, M. (2015). How human capital interacts with the early development of academic spin-offs. International Entrepreneurship and Management Journal, 11(3), 599–621.Doutriaux, J., & Peterman, D. (1982). Technology transfer and academic entrepreneurship. Babson Park: Frontiers of Entrepreneurship Research, Babson College Entrepreneurship Research Conference (BCERC).Eisenhardt, K. M. (1989). Building Theories from Case Study Research. Academy of Management Review, 14(4), 532–550.European Commission (2017). Erasmus 2013–14. Top 500 higher education institutions receiving Erasmus students. Resource document. EC. http://ec.europa.eu/dgs/education_culture/repository/education/library/statistics/2014/erasmus-receiving-institutions_en.pdf Accessed 5 October 2017.Eurovoc (2017). Mutilingual Thesaurus of the European Union. Resource document. http://eurovoc.europa.eu Accessed 03 February 2017.Franzoni, C. & Lissoni, F. (2006). Academic entrepreneurship, patents and spinoffs: Critical issues and lessons for Europe. CESPRI, Università Commerciale “Luigi Bocconi”. Working Paper No. 80.Fritsch, M., & Aamoucke, R. (2013). Regional public research, higher education, and innovative start-ups: An empirical investigation. Small Business Economics, 41, 865–885.Gartner, W. B. (1985). A conceptual framework for describing the phenomenon of new venture creation. The Academy of Management Review, 10, 696–706.Gartner, W. B. (1988). Who is an entrepreneur? is the wrong question. American Journal of Small Business, 12, 11–32.Geuna, A., & Nesta, L. J. J. (2006). University Patenting and its Effects on Academic Research: The merging European Evidence. Research Policy, 35, 790–807.Gibbert, M., & Ruigrok, W. (2010). The “What” and “How” of the case Study Rigor: Three Strategies based on Published Work. Organizational Research Methods, 13(4), 710–737.Gómez Gras, J. M., Galiana Lapera, D. R., Mira Solves, I., Verdú Jover, A. J., & Sancho Azuar, J. (2008). An empirical approach to the organisational determinants of spin-off creation in European universities. International Entrepreneurship and Management Journal, 4(2), 187–198.Grandi, A., & Grimaldi, R. (2005). Academics' organizational characteristics and the generation of successful business ideas. Journal of Business Venturing, 20(6), 821–845.Güemes, J.J. (2011), “Global Entrepreneurship Monitor. Informe GEM España 2010”. Resource document. GEM España. http://www.gemconsortium.org/docs/download/616. Accessed 15 January 2015 .Guerrero, M., & Urbano, D. (2012). The development of an entrepreneurial university. Journal of Technology Transfer, 37(1), 43–74.Guerrero, M., Urbano, D., Cunningham, J., & Organ, D. (2014). Entrepreneurial universities in two European regions: a case study comparison. Journal of Technology Transfer, 39(3), 415–434.Hoang, H., & Antoncic, B. (2003). Network-based research in entrepreneurship: A critical review. Journal of Business Venturing, 18(2), 165–187.Hofstede, G. (1980). Culture’s Consequences. International differences in work-related values. Beverly Hills: Sage.Hofstede, G. (2001). Culture’s consequences: Comparing values, behaviours, institutions, and organizations across nations (2nd ed.). Thousand Oaks: Sage.Hülsbeck, M., & Pickavé, E. N. (2014). Regional knowledge production as determinant of high-technology entrepreneurship: Empirical evidence for Germany. International Entrepreneurship and Management Journal, 10, 121–138.INE (2016). INEbase: Operaciones estadísticas. Instituto Nacional de Estadística (National [Spanish] Statistical Institute). Resource document. INE. http://www.ine.es/inebmenu/indice.htm . Accessed 2 July 2016.Kalar, B., & Antoncic, B. (2015). The entrepreneurial university, academic activities and technology and knowledge transfer in four European countries. Technovation, 36-37, 1–11.Kroll, H. (2009). Demonstrating the instrumentality of motivation oriented approaches for the explanation of academic spin-off formation—an application based on the Chinese case. International Entrepreneurship and Management Journal, 5, 97–116.LAEI (2013). Ley 14/2013, de 27 de septiembre, de Apoyo a Emprendedores y su Internacionalización (‘Act of Support to Entrepreneurs and their Internationalization’). Government of Spain, 27 September. Resource document: http://www.boe.es/boe/dias/2013/09/28/pdfs/BOE-A-2013-10074.pdf . Accessed 10 March 2016.Lam, A., & De Campos, A. (2015). Content to be sad’ or ‘runaway apprentice’? The psychological contract and career agency of young scientists in the entrepreneurial university. Human Relations, 68(5), 811–841.LCTI (2011). Ley 14/2011, de 1 de junio, de la Ciencia, la Tecnología y la Innovación (‘Science, Technology and Innovation Act’). Government of Spain, 1 June. Resource document: http://www.boe.es/boe/dias/2011/06/02/pdfs/BOE-A-2011-9617.pdf . Accessed 10 March 2016.León-Darder, F. (2016). La internacionalització de l’empresa valenciana. In E. Seguí-Mas (Ed.), Una nova via per a l’empresa valenciana (pp. 61–80). Catarroja: Editorial Afers & Fundació Nexe.LES (2011). Ley 2/2011, de 4 de marzo, de Economía Sostenible (‘Sustainable Economy Act’). Government of Spain, 4 March, Resource document. http://www.boe.es/boe/dias/2011/03/05/pdfs/BOE-A-2011-4117.pdf. Accessed 10 March 2016 .Leyden, D. P., & Link, A. N. (2013). Knowledge spillovers, collective entrepreneurship, and economic growth: The role of universities. Small Business Economics, 41, 797–817.Lindelöf, P., & Löfsten, H. (2006). Environmental hostility and firm behavior – An empirical examination of new technology-based firms on science parks. Journal of Small Business Management, 44(3), 386–406.Link, N., & Scott, T. (2005). Opening the ivory’s tower door: An analysis of the determinants of the formation of US university spin-off companies. Research Policy, 34, 1106–1112.Lockett, A., & Wright, M. (2005). Resources, capabilities, risk capital and the creation of university spin-out companies. Research Policy, 34, 1043–1057.LOMLOU (2007). Ley Orgánica 4/2007, de 12 de abril, por la que se modifica la Ley Orgánica 6/2011, de 21 de diciembre, de Universidades (‘Act of Modification of the University Act’). Government of Spain, 12 April. Resource document. https://www.boe.es/boe/dias/2007/04/13/pdfs/A16241-16260.pdf (accessed 11 March 2016).LOU (2001). Ley Orgánica 6/2001, de Universidades (‘University Act’). Government of Spain, 21 December. Resource document: https://www.boe.es/boe/dias/2001/12/24/pdfs/A49400-49425.pdf . Accessed 11 March 2016.Martinelli, A., Meyer, M., & Von Tunzelmann, N. (2008). Becoming an entrepreneurial university? A case study of knowledge exchange relationships and faculty attitudes in a medium-sized, research-oriented university. Journal of Technology Transfer, 33, 259–283.Martínez Carrascal, C. & Mulino Ríos, M. (2014). La evolución del crédito bancario a las empresas españolas según su tamaño. Un análisis basado en la explotación conjunta de la información de la CIR y de la CBI, Boletín Económico - Banco de España, Enero (January), pp. 117–125.Mathias, B. D., Williams, D. W., & Smith, A. R. (2015). Entrepreneurial inception: The role of imprinting in entrepreneurial action. Journal of Business Venturing, 30(1), 11–28.MIET (Spanish Ministry of Industry, Energy and Tourism) (2012). Estadísticas Pyme. Evolución e indicadores. No. 10″, Resource document. http://www.ipyme.org/Publicaciones/ESTADISTICAS_PYME_N10_2011.pdf. Accessed 2 May 2016 .Miles, M.B. & Huberman, A.M. (2008). Qualitative Data Analysis: an expanded sourcebook. Sage Publications.Morales-Gualdrón, S. Y., Gutiérrez-Gracias, & Roig Dobón, S. (2009). The entrepreneurial motivation in academia: A multidimensional construct. International Entrepreneurship and Management Journal, 6, 301–317.Mosey, S., & Wright, M. (2007). From human capital to social capital: A longitudinal study of technology-based academic entrepreneurs. Entrepreneur, 31, 909–936.Mosey, S., Lockett, A., & Westhead, P. (2006). Creating network bridges for university technology transfer: The Medici fellowship programme. Technology Analysis and Strategic Management, 18, 71–91.Mosey, S., Wright, M., & Clarysse, B. (2012a). Transforming traditional university structures for the knowledge economy through multidisciplinary institutes. Cambridge Journal of Economics, 36, 587–607.Mosey, S., Noke, H., & Binks, M. (2012b). The influence of human and social capital upon the entrepreneurial intentions and destinations of academics. Technology Analysis and Strategic Management, 24, 893–910.Moutinho, R., Au-Yong-Oliveira, M., Coelho, A., & Manso, J. P. (2016). Determinants of knowledge-based entrepreneurship: an exploratory approach. International Entrepreneurship and Management Journal, 12(1), 171–197.Mowery, D. C., Nelson, R. R., Sampat, B. N., & Ziedonis, A. A. (2001a). The growth of patenting and licensing by US universities: an assessment of the effects of Bayle-Dole Act of 1980. Research Policy, 30(1), 99–119.Mowery, D. C., Sampat, B. N., & Ziedonis, A. A. (2001b). Learning to patent: institutional experience, learning, and the characyeristics of US university Patents after the Bayle-Dole Act, 1981-1992. Management Science, 48(1), 73–89.O’Shea, R., Allen, J., Chevalier, A., & Roche, F. (2005). Entrepreneurial orientation, technology transfer and spinoff performance of US universities. Research Policy, 34, 994–1009.O’Shea, R., Allen, T., Morse, K., O’Gorman, C., & Roche, F. (2007). Delineating the anatomy of an entrepreneurial university: the Massachusetts Institute of Technology Experience. R&D Management, 37(1), 1–16.O’Shea, R., Chugh, H., & Allen, T. (2008). Determinants and consequences of university spinoff activity: A conceptual framework. Journal of Technology Transfer, 33, 653–666.Ortín, P., Salas, V., Trujillo, M.V., & Vendrell, F. (2007). El spin-off universitario en España como modelo de creación de empresas intensivas en tecnología. Ministerio de Industria, Turismo y Comercio. Secretaría General de Industria. Dirección General de Política de la Pyme. Resource document. http://www.ipyme.org/Publicaciones/Informe spinnoff.pdf . Accessed 2 October 2016.Papaoikonomou, E., Segarra, P., & Li, X. (2012). Entrepreneurship in the context of crisis: Identifying barriers and proposing strategies. International Advances in Economic Research, 18, 111–119.Piperopoulos, P., & Piperopoulos, G. (2010). Is Greece finally on the right path toward entrepreneurship, innovation, and business clusters? International Journal of Public Administration, 33(1), 55–59.Powers, B., & McDougall, P. (2005). University startup formation and technology licensing with firms that go public: A resource-based view of academic entrepreneurship. Journal of Business Venturing, 20, 291–311.Red OTRI (2016). Informe de la Encuesta de Investigación y Transferencia 2014 de las universidades españolas. Resource document. http://www.redotriuniversidades.net/index.php/informa-encuesta/6-encuesta-redotri/informa-encuesta-2014/download . Accessed 22 June 2016.Redero San-Román, M. (2002). Origen y desarrollo de la universidad franquista. Studia Zamorensia, 6, 337–352.Rodríguez-Gulías, M. J., Rodeiro-Pazos, D., & Fernández-López, S. (2017). The effect of university and regional knowledge spillovers on firms’ performance: an analysis of the Spanish USOs. International Entrepreneurship and Management Journal, 13(1), 191–209.Rodríguez-San Pedro, L.E. (2014). Las universidades españolas en su contexto historic. Resource document. Universia. http://universidades.universia.es/universidades-de-pais/historia-de-universidades/historia-universidad-espanola/pasado-reciente/pasado-reciente-multiplicidad-regimen-autonomico.html . Accessed 28 July 2015.Samsom, K., & Gurdon, M. (1990). Entrepreneurial scientist: Organizational performance in scientist-started high technology firms. Forest Park: Frontiers of Entrepreneurship Research, Babson College Entrepreneurship Research Conference (BCERC).Schmitz, A., Urbano, D., Dandolini, G. A., de Souza, J. A., & Guerrero, M. (2017). Innovation and entrepreneurship in the academic setting: A systematic literature review. International Entrepreneurship and Management Journal, 13(2), 369–395.Shane, S., & Khurana, R. (2003). Bringing individuals back in: The effects of career experience on new firm founding. Industrial and Corporate Change, 12, 519–543.Shapero, A., & Sokol, L. (1982). The social dimensions of entrepreneurship. In C. A. Kent, D. L. Sexton, & K. H. Vesper (Eds.), Encyclopaedia of entrepreneurship (pp. 72–90). Englewood Cliffs: Prentice Hall.Smilor, R. W., Gibson, D. V., & Dietrich, G. B. (1990). University spin-out companies: technology start-ups from UT-Austin. Journal of Business Venturing, 5(1), 63–76.Soler i Marco, V. (2009). Creixement i canvi estructural. In V. Soler (Ed.), Economia espanyola i del País Valencià. Valencia: Publicacions de la Universitat de València.Suddaby, R., Bruton, G. D., & Si, S. X. (2015). Entrepreneurship through a qualitative lens: Insights on the construction and/or discovery of entrepreneurial opportunity. Journal of Business Venturing, 30(1), 1–10.Tech Transfer UPV FCR (2016). Air Nostrum, Caixa Popular e IVI entran en el fondo de la UPV. Resource document. TTUPV FCR. http://www.techtransferupv.com/noticias/air-nostrum-caixa-popular-e-ivi-entran-en-el-fondo-de-la-upv/ (4 April) Accessed 10 July 2016.The Times Higher Education (2017). 100 Under 50 Ranking 2017. Resource document. THE. https://www.timeshighereducation.com/world-university-rankings/2017/young-university-rankings#!/page/0/length/-1/sort_by/rank/sort_order/asc/cols/stats . Accessed 15 august 2017.UPV (2007). Instituto IDEAS 15 aniversario (1992–2007). Resource document. UPV. http://www.upv.es/entidades/IDEAS/menu_urlv.html?http://www.upv.es/entidades/IDEAS/info/memoria15a%F1os.pdf . Accessed 10 April 2016.UPV (2011). Corporación empresarial. Resource document. UPV. http://www.upv.es/noticias-upv/noticia-4904-corporacion-emp-es.html . Accessed 10 April 2016.UPV (2014). Plan de emprendimiento global. Resource document. UPV. https://www.upv.es/noticias-upv/noticia-6846-plan-de-emprend-es.html . Accessed 10 April 2016.UPV (2015). Jornadas de Puertas Abiertas 2015–16. Resource document. UPV. www.upv.es/contenidos/ORIENTA/info/jpa_ciclos_2015-16.ppt . Accessed 10 April 2016.UPV (2017a). Spin-Off UPV. Resource document. UPV. http://www.upv.es/entidades/I2T/info/891434normalc.html . Accessed 5 October 2017.UPV (2017b). Ciudad Politécnica de la Innovación. Parque Científico en Red de la Universidad Politécnica de Valencia. Quienes Somos. Presentación. Resource document. UPV. http://cpi.upv.es/quienes-somos/presentacion . Accessed 5 October 2017.UPV (2017c). Servicio de Promoción y Apoyo a la Investigación, la Innovación y la Transferencia. Presentación. Resource document. UPV. http://i2t.webs.upv.es/i2t/presentacion.php. Accessed 5 October 2017 .UPV. (2017d). Tech Transfer UPV. UPV: Resource document http://www.upv.es/noticias-upv/noticia-8355-tech-transfer-u-es.html. Accessed 5 October 2017 .UPV (2017e). Mission statement, vision and values. Resource document. UPV. https://www.upv.es/organizacion/la-institucion/misionvisionvalores-plan-upv-en.html Accessed 17 October 2017.Vargas Vasserot, C. (2012). Las spin-offs académicas y su posible configuración como empresas de economía social. REVESCO. Revista de Estudios Cooperativos, 107, 186–205.VLC/Campus (2017). VLC/Campus. Valencia, International Campus of Excellence. Resource document. UPV. http://www.vlc-campus.com/en . Accessed 5 October 2017.Walter, A., Auer, M., & Ritter, T. (2006). The impact of network capabilities and entrepreneurial orientation on university spin-off performance. Journal of Business Venturing, 21(4), 541–567.Weatherston, J. (1995). Academic Entrepreneurs: Is a spin-off Company too risky. Proceedings of the 40th International Council on Small Business, Sydney, 18–21.Willoughby, M., Talon, J., Millet, J., & Ayats, C. (2013). University services for fostering creativity in hi-tech firms. The Service Industries Journal, 33, 1103–1116.Wright, M., & Mosey, S. (2012). Strategic entrepreneurship, resource orchestration and growing spin-offs from universities. Technology Analysis and Strategic Management, 24, 911–927.Wright, M., Clarysse, B., Mustar, P., & Lockett, A. (2007). Academic Entrepreneurship in Europe. Cheltenham: Edward Elgar.Yin, R. K. (1994). Case study research: Design and methods (2nd ed.). Sage: Thousand Oaks.Yusof, M., & Jain, K. J. (2010). Categories of university-level entrepreneurship: A literature survey. International Entrepreneurship and Management Journal, 6(1), 81–86

Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review

[EN] Purpose: To systematically review evidence regarding the association of multi-parametric biomarkers with clinical outcomes and their capacity to explain relevant subcompartments of gliomas. Materials and Methods: Scopus database was searched for original journal papers from January 1st, 2007 to February 20th , 2017 according to PRISMA. Four hundred forty-nine abstracts of papers were reviewed and scored independently by two out of six authors. Based on those papers we analyzed associations between biomarkers, subcompartments within the tumor lesion, and clinical outcomes. From all the articles analyzed, the twenty-seven papers with the highest scores were highlighted to represent the evidence about MR imaging biomarkers associated with clinical outcomes. Similarly, eighteen studies defining subcompartments within the tumor region were also highlighted to represent the evidence of MR imaging biomarkers. Their reports were critically appraised according to the QUADAS-2 criteria. Results: It has been demonstrated that multi-parametric biomarkers are prepared for surrogating diagnosis, grading, segmentation, overall survival, progression-free survival, recurrence, molecular profiling and response to treatment in gliomas. Quantifications and radiomics features obtained from morphological exams (T1, T2, FLAIR, T1c), PWI (including DSC and DCE), diffusion (DWI, DTI) and chemical shift imaging (CSI) are the preferred MR biomarkers associated to clinical outcomes. Subcompartments relative to the peritumoral region, invasion, infiltration, proliferation, mass effect and pseudo flush, relapse compartments, gross tumor volumes, and high-risk regions have been defined to characterize the heterogeneity. For the majority of pairwise cooccurrences, we found no evidence to assert that observed co-occurrences were significantly different from their expected co-occurrences (Binomial test with False Discovery Rate correction, alpha=0.05). The co-occurrence among terms in the studied papers was found to be driven by their individual prevalence and trends in the literature. Conclusion: Combinations of MR imaging biomarkers from morphological, PWI, DWI and CSI exams have demonstrated their capability to predict clinical outcomes in different management moments of gliomas. Whereas morphologic-derived compartments have been mostly studied during the last ten years, new multi-parametric MRI approaches have also been proposed to discover specific subcompartments of the tumors. MR biomarkers from those subcompartments show the local behavior within the heterogeneous tumor and may quantify the prognosis and response to treatment of gliomas.This work was supported by the Spanish Ministry for Investigation, Development and Innovation project with identification number DPI2016-80054-R.Oltra-Sastre, M.; Fuster García, E.; Juan -Albarracín, J.; Sáez Silvestre, C.; Perez-Girbes, A.; Sanz-Requena, R.; Revert-Ventura, A.... (2019). Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review. Current Medical Imaging Reviews. 15(10):933-947. https://doi.org/10.2174/1573405615666190109100503S9339471510Louis D.N.; Perry A.; Reifenberger G.; The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 2016,131(6),803-820Ostrom Q.T.; Gittleman H.; Fulop J.; CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008-2012. Neuro-oncol 2015,17(Suppl. 4),iv1-iv62Yachida S.; Jones S.; Bozic I.; Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 2010,467(7319),1114-1117Gerlinger M.; Rowan A.J.; Horswell S.; Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012,366(10),883-892Sottoriva A.; Spiteri I.; Piccirillo S.G.M.; Intratumor heterogeneityin human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci USA 2013,110(10),4009-4014Whiting P.F.; Rutjes A.W.; Westwood M.E.; QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011,155(8),529-536Stupp R.; Mason W.P.; van den Bent M.J.; Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 2005,352(10),987-996Ponte K.F.; Berro D.H.; Collet S.; In vivo relationship between hypoxia and angiogenesis in human glioblastoma: a multimodal imaging study. J Nucl Med 2017,58(10),1574-1579Pope W.B.; Kim H.J.; Huo J.; Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology 2009,252(1),182-189Mörén L.; Bergenheim A.T.; Ghasimi S.; Brännström T.; Johansson M.; Antti H.; Metabolomic screening of tumor tissue and serum in glioma patients reveals diagnostic and prognostic information. Metabolites 2015,5(3),502-520Prager A.J.; Martinez N.; Beal K.; Omuro A.; Zhang Z.; Young R.J.; Diffusion and perfusion MRI to differentiate treatment-related changes including pseudoprogression from recurrent tumors in high-grade gliomas with histopathologic evidence. AJNR Am J Neuroradiol 2015,36(5),877-885Kickingereder P.; Burth S.; Wick A.; Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 2016,280(3),880-889Yoo R-E.; Choi S.H.; Cho H.R.; Tumor blood flow from arterial spin labeling perfusion MRI: a key parameter in distinguishing high-grade gliomas from primary cerebral lymphomas, and in predicting genetic biomarkers in high-grade gliomas. J Magn Reson Imaging 2013,38(4),852-860Liberman G.; Louzoun Y.; Aizenstein O.; Automatic multi-modal MR tissue classification for the assessment of response to bevacizumab in patients with glioblastoma. Eur J Radiol 2013,82(2),e87-e94Ramadan S.; Andronesi O.C.; Stanwell P.; Lin A.P.; Sorensen A.G.; Mountford C.E.; Use of in vivo two-dimensional MR spectroscopy to compare the biochemistry of the human brain to that of glioblastoma. Radiology 2011,259(2),540-549Xintao H.; Wong K.K.; Young G.S.; Guo L.; Wong S.T.; Support vector machine multi-parametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma. J Magn Reson Imaging 2011,33(2),296Ingrisch M.; Schneider M.J.; Nörenberg D.; Radiomic Analysis reveals prognostic information in T1-weighted baseline magnetic resonance imaging in patients with glioblastoma. Invest Radiol 2017,52(6),360-366Ulyte A.; Katsaros V.K.; Liouta E.; Prognostic value of preoperative dynamic contrast-enhanced MRI perfusion parameters for high-grade glioma patients. Neuroradiology 2016,58(12),1197-1208O’Neill A.F.; Qin L.; Wen P.Y.; de Groot J.F.; Van den Abbeele A.D.; Yap J.T.; Demonstration of DCE-MRI as an early pharmacodynamic biomarker of response to VEGF Trap in glioblastoma. J Neurooncol 2016,130(3),495-503Kickingereder P.; Bonekamp D.; Nowosielski M.; Radiogenomics of glioblastoma: machine learning-based classification of molecular characteristics by using multiparametric and multiregional mr imaging features. Radiology 2016,281(3),907-918Roberto S-R.; Antonio R-V.; Luis M-B.; Angel A-B.; Gracián G-M.; Quantitative mr perfusion parameters related to survival time in high-grade gliomas. European Radiology 2013,23(12),3456-3465Jain R.; Poisson L.; Narang J.; Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers. Radiology 2013,267(1),212-220Fathi K.A.; Mohseni M.; Rezaei S.; Bakhshandehpour G.; Saligheh R.H.; Multi-parametric (ADC/PWI/T2-W) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme. MAGMA 2015,28(1),13-22Caulo M.; Panara V.; Tortora D.; Data-driven grading of brain gliomas: a multiparametric MR imaging study. Radiology 2014,272(2),494-503Alexiou G.A.; Zikou A.; Tsiouris S.; Comparison of diffusion tensor, dynamic susceptibility contrast MRI and (99m)Tc-Tetrofosmin brain SPECT for the detection of recurrent high-grade glioma. Magn Reson Imaging 2014,32(7),854-859Van Cauter S.; De Keyzer F.; Sima D.M.; Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas. Neuro-oncol 2014,16(7),1010-1021Seeger A.; Braun C.; Skardelly M.; Comparison of three different MR perfusion techniques and MR spectroscopy for multiparametric assessment in distinguishing recurrent high-grade gliomas from stable disease. Acad Radiol 2013,20(12),1557-1565Chawalparit O.; Sangruchi T.; Witthiwej T.; Diagnostic performance of advanced mri in differentiating high-grade from low-grade gliomas in a setting of routine service. J Med Assoc Thai 2013,96(10),1365-1373Li Y.; Lupo J.M.; Parvataneni R.; Survival analysis in patients with newly diagnosed glioblastoma using pre- and postradiotherapy MR spectroscopic imaging. Neuro-oncol 2013,15(5),607-617Shankar J.J.S.; Woulfe J.; Silva V.D.; Nguyen T.B.; Evaluation of perfusion CT in grading and prognostication of high-grade gliomas at diagnosis: a pilot study. AJR Am J Roentgenol 2013,200(5)Zinn P.O.; Mahajan B.; Sathyan P.; Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme. PLoS One 2011,6(10)Matsusue E.; Fink J.R.; Rockhill J.K.; Ogawa T.; Maravilla K.R.; Distinction between glioma progression and post-radiation change by combined physiologic MR imaging. Neuroradiology 2010,52(4),297-306Juan-Albarracín J.; Fuster-Garcia E.; Manjón J.V.; Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification. PLoS One 2015,10(5)Itakura H.; Achrol A.S.; Mitchell L.A.; Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Sci Transl Med 2015,7(303)Ion-Margineanu A.; Van Cauter S.; Sima D.M.; Tumour relapse prediction using multiparametric MR data recorded during follow-up of GBM patients. BioMed Res Int 2015,2015Durst C.R.; Raghavan P.; Shaffrey M.E.; Multimodal MR imaging model to predict tumor infiltration in patients with gliomas. Neuroradiology 2014,56(2),107-115Yoon J.H.; Kim J.H.; Kang W.J.; Grading of cerebral glioma with multi-parametric MR Imaging and 18F-FDG-PET: concordance and accuracy. European Radiol 2014,24(2),380-389Demerath T.; Simon-Gabriel C.P.; Kellner E.; Mesoscopic imaging of glioblastomas: are diffusion, perfusion and spectroscopic measures influenced by the radiogenetic phenotype? Neuroradiol J 2017,30(1),36-47Qin L.; Li X.; Stroiney A.; Advanced MRI assessment to predict benefit of anti-programmed cell death 1 protein immunotherapy response in patients with recurrent glioblastoma. Neuroradiology 2017,59(2),135-145Boult J.K.R.; Borri M.; Jury A.; Investigating intracranial tumour growth patterns with multiparametric MRI incorporating Gd-DTPA and USPIO-enhanced imaging. NMR Biomed 2016,29(11),1608-1617Server A.; Kulle B.; Gadmar Ø.B.; Josefsen R.; Kumar T.; Nakstad P.H.; Measurements of diagnostic examination performance using quantitative apparent diffusion coefficient and proton MR spectroscopic imaging in the preoperative evaluation of tumor grade in cerebral gliomas. Eur J Radiol 2011,80(2),462-470Chang P.D.; Chow D.S.; Yang P.H.; Filippi C.G.; Lignelli A.; Predicting glioblastoma recurrence by early changes in the apparent diffusion coefficient value and signal intensity on FLAIR images. AJR Am J Roentgenol 2017,208(1),57-65Yi C.; Shangjie R.; Volume of high-risk intratumoralsubregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma. Eur Radiol 2017,27,3583-3592Khalifa J.; Tensaouti F.; Chaltiel L.; Identification of a candidate biomarker from perfusion MRI to anticipate glioblastoma progression after chemoradiation. Eur Radiol 2016,26(11),4194-4203Prateek P.; Jay P.; Partovi S.; Madabhushi A.; Tiwari P.; Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastomamultiforme: preliminary findings. Eur Radiol 2017,27(10),4188-4197Lemasson B.; Chenevert T.L.; Lawrence T.S.; Impact of perfusion map analysis on early survival prediction accuracy in glioma patients. Transl Oncol 2013,6(6),766-774Inano R.; Oishi N.; Kunieda T.; Visualization of heterogeneity and regional grading of gliomas by multiple features using magnetic resonance-based clustered images. Sci Rep 2016,6,30344Delgado-Goñi T.; Ortega-Martorell S.; Ciezka M.; MRSI-based molecular imaging of therapy response to temozolomide in preclinical glioblastoma using source analysis. NMR Biomed 2016,29(6),732-743Cui Y.; Tha K.K.; Terasaka S.; Prognostic imaging biomarkers in glioblastoma: development and independent validation on the basis of multiregion and quantitative analysis of MR images. Radiology 2016,278(2),546-553Price S.J.; Young A.M.H.; Scotton W.J.; Multimodal MRI can identify perfusion and metabolic changes in the invasive margin of glioblastomas. J Magn Reson Imaging 2016,43(2),487-494Sauwen N.; Acou M.; Van Cauter S.; Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI. Neuroimage Clin 2016,12,753-764Jena A.; Taneja S.; Gambhir A.; Glioma recurrence versus radiation necrosis: single-session multiparametric approach using simultaneous O-(2-18F-Fluoroethyl)-L-Tyrosine PET/MRI. Clin Nucl Med 2016,41(5),e228-e236Kim H.S.; Goh M.J.; Kim N.; Choi C.G.; Kim S.J.; Kim J.H.; Which combination of MR imaging modalities is best for predicting recurrent glioblastoma? Study of diagnostic accuracy and reproducibility. Radiology 2014,273(3),831-843Christoforidis G.A.; Yang M.; Abduljalil A.; “Tumoral pseudoblush” identified within gliomas at high-spatial-resolution ultrahigh-field-strength gradient-echo MR imaging corresponds to microvascularity at stereotactic biopsy. Radiology 2012,264(1),210-217Wang S.; Kim S.; Chawla S.; Differentiation between glioblastomas, solitary brain metastases, and primary cerebral lymphomas using diffusion tensor and dynamic susceptibility contrast-enhanced MR imaging. AJNR Am J Neuroradiol 2011,32(3),507-514Hanahan D.; Weinberg R.A.; Hallmarks of cancer: the next generation. Cell 2011,144(5),646-674Macdonald D.R.; Cascino T.L.; Schold S.C.; Cairncross J.G.; Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol 1990,8(7),1277-1280Wen P.Y.; Macdonald D.R.; Reardon D.A.; Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 2010,28(11),1963-1972Sorensen A.G.; Batchelor T.T.; Wen P.Y.; Zhang W-T.; Jain R.K.; Response criteria for glioma. Nat Clin Pract Oncol 2008,5(11),634-644Rosenkrantz A.B.; Friedman K.; Chandarana H.; Current status of hybrid PET/MRI in oncologic imaging. AJR Am J Roentgenol 2016,206(1),162-172Castiglioni I.; Gallivanone F.; Canevari C.; Hybrid PET/MRI for In vivo imaging of cancer: current clinical experiences and recent advances. Curr Med Imaging 2016,12,106Mainta I.C.; Perani D.; Delattre B.M.A.; FDG PET/MR imaging in major neurocognitive disorders. Curr Alzheimer Res 2017,14,186-197Marner L.; Henriksen O.M.; Lundemann M.; Larsen V.A.; Law I.; Clinical PET/MRI in neurooncology: opportunities and challenges from a single-institution perspective. Clin Transl Imaging 2017,5(2),135-149R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2015. Available from: https://www.R-project.org

Multiproject–multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy

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Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.EFG was supported by Programa Torres Quevedo, Ministerio de Educacion y Ciencia, co-funded by the European Social Fund (PTQ-1205693). EFG, JMGG, and JVM were supported by Red Tematica de Investigacion Cooperativa en Cancer, (RTICC) 2013-2016 (RD12/0036/0020). JMGG was supported by Project TIN2013-43457-R: Caracterizacion de firmas biologicas de glioblastomas mediante modelos no-supervisados de prediccion estructurada basados en biomarcadores de imagen, co-funded by the Ministerio de Economia y Competitividad of Spain; CON2014001 UPV-IISLaFe: Unsupervised glioblastoma tumor components segmentation based on perfusion multiparametric MRI and spatio/temporal constraints; and CON2014002 UPV-IISLaFe: Empleo de segmentacion no supervisada multiparametrica basada en perfusion RM para la caracterizacion del edema peritumoral de gliomas y metastasis cerebrales unicas, funded by Instituto de Investigacion Sanitaria H. Universitario y Politecnico La Fe. This work was partially supported by the Instituto de Aplicaciones de las Tecnologias de la Informacion y las Comunicaciones Avanzadas (ITACA). Veratech for Health S.L. provided support in the form of salaries for author EF-G, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author is articulated in the "author contributions" section. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.Juan Albarracín, J.; Fuster García, E.; Manjón Herrera, JV.; Robles Viejo, M.; Aparici, F.; Marti-Bonmati, L.; García Gómez, JM. (2015). Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification. PLoS ONE. 10(5):1-20. https://doi.org/10.1371/journal.pone.0125143S120105Wen, P. Y., Macdonald, D. R., Reardon, D. A., Cloughesy, T. F., Sorensen, A. G., Galanis, E., … Chang, S. M. (2010). Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group. Journal of Clinical Oncology, 28(11), 1963-1972. doi:10.1200/jco.2009.26.3541Bauer, S., Wiest, R., Nolte, L.-P., & Reyes, M. (2013). A survey of MRI-based medical image analysis for brain tumor studies. Physics in Medicine and Biology, 58(13), R97-R129. doi:10.1088/0031-9155/58/13/r97Dolecek, T. A., Propp, J. M., Stroup, N. E., & Kruchko, C. (2012). CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2005-2009. Neuro-Oncology, 14(suppl 5), v1-v49. doi:10.1093/neuonc/nos218Gordillo, N., Montseny, E., & Sobrevilla, P. (2013). State of the art survey on MRI brain tumor segmentation. Magnetic Resonance Imaging, 31(8), 1426-1438. doi:10.1016/j.mri.2013.05.002Verma, R., Zacharaki, E. I., Ou, Y., Cai, H., Chawla, S., Lee, S.-K., … Davatzikos, C. (2008). Multiparametric Tissue Characterization of Brain Neoplasms and Their Recurrence Using Pattern Classification of MR Images. Academic Radiology, 15(8), 966-977. doi:10.1016/j.acra.2008.01.029Jensen, T. R., & Schmainda, K. M. (2009). Computer-aided detection of brain tumor invasion using multiparametric MRI. Journal of Magnetic Resonance Imaging, 30(3), 481-489. doi:10.1002/jmri.21878Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324Wagstaff KL. Intelligent Clustering with instance-level constraints. PhD Thesis, Cornell University. 2002.Fletcher-Heath, L. M., Hall, L. O., Goldgof, D. B., & Murtagh, F. R. (2001). Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artificial Intelligence in Medicine, 21(1-3), 43-63. doi:10.1016/s0933-3657(00)00073-7Nie, J., Xue, Z., Liu, T., Young, G. S., Setayesh, K., Guo, L., & Wong, S. T. C. (2009). Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field. Computerized Medical Imaging and Graphics, 33(6), 431-441. doi:10.1016/j.compmedimag.2009.04.006Zhu, Y., Young, G. S., Xue, Z., Huang, R. Y., You, H., Setayesh, K., … Wong, S. T. (2012). Semi-Automatic Segmentation Software for Quantitative Clinical Brain Glioblastoma Evaluation. Academic Radiology, 19(8), 977-985. doi:10.1016/j.acra.2012.03.026Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20(1), 45-57. doi:10.1109/42.906424Vijayakumar, C., Damayanti, G., Pant, R., & Sreedhar, C. M. (2007). Segmentation and grading of brain tumors on apparent diffusion coefficient images using self-organizing maps. Computerized Medical Imaging and Graphics, 31(7), 473-484. doi:10.1016/j.compmedimag.2007.04.004Prastawa, M., Bullitt, E., Moon, N., Van Leemput, K., & Gerig, G. (2003). Automatic brain tumor segmentation by subject specific modification of atlas priors1. Academic Radiology, 10(12), 1341-1348. doi:10.1016/s1076-6332(03)00506-3Gudbjartsson, H., & Patz, S. (1995). The rician distribution of noisy mri data. Magnetic Resonance in Medicine, 34(6), 910-914. doi:10.1002/mrm.1910340618Buades, A., Coll, B., & Morel, J. M. (2005). A Review of Image Denoising Algorithms, with a New One. Multiscale Modeling & Simulation, 4(2), 490-530. doi:10.1137/040616024Manjón, J. V., Coupé, P., Martí-Bonmatí, L., Collins, D. L., & Robles, M. (2009). Adaptive non-local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging, 31(1), 192-203. doi:10.1002/jmri.22003Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging, 17(1), 87-97. doi:10.1109/42.668698Tustison, N. J., Avants, B. B., Cook, P. A., Yuanjie Zheng, Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging, 29(6), 1310-1320. doi:10.1109/tmi.2010.2046908Manjón, J. V., Coupé, P., Buades, A., Collins, D. L., & Robles, M. (2010). MRI Superresolution Using Self-Similarity and Image Priors. International Journal of Biomedical Imaging, 2010, 1-11. doi:10.1155/2010/425891Rousseau, F. (2010). A non-local approach for image super-resolution using intermodality priors☆. Medical Image Analysis, 14(4), 594-605. doi:10.1016/j.media.2010.04.005Protter, M., Elad, M., Takeda, H., & Milanfar, P. (2009). Generalizing the Nonlocal-Means to Super-Resolution Reconstruction. IEEE Transactions on Image Processing, 18(1), 36-51. doi:10.1109/tip.2008.2008067Manjón, J. V., Coupé, P., Buades, A., Fonov, V., Louis Collins, D., & Robles, M. (2010). Non-local MRI upsampling. Medical Image Analysis, 14(6), 784-792. doi:10.1016/j.media.2010.05.010Kassner, A., & Thornhill, R. E. (2010). Texture Analysis: A Review of Neurologic MR Imaging Applications. American Journal of Neuroradiology, 31(5), 809-816. doi:10.3174/ajnr.a2061Ahmed, S., Iftekharuddin, K. M., & Vossough, A. (2011). Efficacy of Texture, Shape, and Intensity Feature Fusion for Posterior-Fossa Tumor Segmentation in MRI. IEEE Transactions on Information Technology in Biomedicine, 15(2), 206-213. doi:10.1109/titb.2011.2104376Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. doi:10.1109/tit.1982.1056489Dunn, J. C. (1973). A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics, 3(3), 32-57. doi:10.1080/01969727308546046Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. doi:10.1007/978-1-4757-0450-1Hammersley, JM, Clifford, P. Markov fields on finite graphs and lattices. 1971.Komodakis, N., & Tziritas, G. (2007). Approximate Labeling via Graph Cuts Based on Linear Programming. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(8), 1436-1453. doi:10.1109/tpami.2007.1061Komodakis, N., Tziritas, G., & Paragios, N. (2008). Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies. Computer Vision and Image Understanding, 112(1), 14-29. doi:10.1016/j.cviu.2008.06.007Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., & Collins, D. L. (2011). Unbiased average age-appropriate atlases for pediatric studies. NeuroImage, 54(1), 313-327. doi:10.1016/j.neuroimage.2010.07.033Fonov, V., Evans, A., McKinstry, R., Almli, C., & Collins, D. (2009). Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage, 47, S102. doi:10.1016/s1053-8119(09)70884-5Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., … Parsey, R. V. (2009). Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage, 46(3), 786-802. doi:10.1016/j.neuroimage.2008.12.037AVANTS, B., EPSTEIN, C., GROSSMAN, M., & GEE, J. (2008). Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1), 26-41. doi:10.1016/j.media.2007.06.004Saez C, Robles M, Garcia-Gomez JM. Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances. Statistical Methods in Medical Research 2014; In press