Literature DB >> 17252546

Using mathematical modeling to predict survival of low-grade gliomas.

Ellsworth C Alvord, Kristin R Swanson.   

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Year:  2007        PMID： 17252546     DOI： 10.1002/ana.21042

Source DB:  PubMed          Journal:  Ann Neurol        ISSN： 0364-5134            Impact factor:   10.422


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  4 in total

1.  Velocity of tumor spontaneous expansion predicts long-term outcomes for diffuse low-grade gliomas.

Authors:  Johan Pallud; Marie Blonski; Emmanuel Mandonnet; Etienne Audureau; Denys Fontaine; Nader Sanai; Luc Bauchet; Philippe Peruzzi; Marc Frénay; Philippe Colin; Rémy Guillevin; Valérie Bernier; Marie-Hélène Baron; Jacques Guyotat; Hugues Duffau; Luc Taillandier; Laurent Capelle
Journal:  Neuro Oncol       Date:  2013-02-07       Impact factor: 12.300

2.  A generative approach for image-based modeling of tumor growth.

Authors:  Bjoern H Menze; Koen Van Leemput; Antti Honkela; Ender Konukoglu; Marc-André Weber; Nicholas Ayache; Polina Golland
Journal:  Inf Process Med Imaging       Date:  2011

Review 3.  Advanced magnetic resonance imaging of the physical processes in human glioblastoma.

Authors:  Jayashree Kalpathy-Cramer; Elizabeth R Gerstner; Kyrre E Emblem; Ovidiu Andronesi; Bruce Rosen
Journal:  Cancer Res       Date:  2014-09-01       Impact factor: 12.701

4.  Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model.

Authors:  Christina H Wang; Jason K Rockhill; Maciej Mrugala; Danielle L Peacock; Albert Lai; Katy Jusenius; Joanna M Wardlaw; Timothy Cloughesy; Alexander M Spence; Russ Rockne; Ellsworth C Alvord; Kristin R Swanson
Journal:  Cancer Res       Date:  2009-11-24       Impact factor: 12.701
  4 in total

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