about
Simulation of anisotropic growth of low-grade gliomas using diffusion tensor imaging.Magnetic resonance imaging characteristics of glioblastoma multiforme: implications for understanding glioma ontogeny.Complementary but distinct roles for MRI and 18F-fluoromisonidazole PET in the assessment of human glioblastomas.The evolution of mathematical modeling of glioma proliferation and invasion.Quantitative metrics of net proliferation and invasion link biological aggressiveness assessed by MRI with hypoxia assessed by FMISO-PET in newly diagnosed glioblastomasA mathematical model for brain tumor response to radiation therapy.Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach.The role of IDH1 mutated tumour cells in secondary glioblastomas: an evolutionary game theoretical viewQuantifying efficacy of chemotherapy of brain tumors with homogeneous and heterogeneous drug delivery.A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using 18F-FMISO-PET.Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model.Addendum to 'A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using 18F-FMISO-PET'.Velocity of radial expansion of contrast-enhancing gliomas and the effectiveness of radiotherapy in individual patients: a proof of principle.Prostate-specific antigen: a clinical and mathematical conundrum.A quantitative model for differential motility of gliomas in grey and white matter.From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment responsePerformance of Standardized Relative CBV for Quantifying Regional Histologic Tumor Burden in Recurrent High-Grade Glioma: Comparison against Normalized Relative CBV Using Image-Localized Stereotactic BiopsiesSpeed Switch in Glioblastoma Growth Rate due to Enhanced Hypoxia-Induced MigrationSex differences in GBM revealed by analysis of patient imaging, transcriptome, and survival dataAccurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer LearningThe 2019 mathematical oncology roadmap
P50
Q30998879-5B97A595-F12B-48EC-8BBF-988D3B41FD66Q33703639-BEFEC353-F19E-4C09-AD57-CF693E0870AFQ34133542-C43FA9BB-1E71-43B2-A098-89ADFACA2356Q36699985-044A90FA-F32A-4247-871E-88B216956AE1Q37140172-FB75493C-4259-466A-95B0-01E24FFAE8B8Q37199727-1B9AAD7C-95DC-4D59-B50E-5B0CD285CAADQ37206334-CE314A1F-040C-4E4D-92C3-8F8A9A84B177Q38304728-1B54D9B5-B2B8-437F-A4C7-5B08C5351228Q40596233-3D300DC3-4234-4B0E-8631-71AC9CFA579BQ41650486-1CBE3F50-9B20-4E5E-BCEA-C8EC8F6DDBF9Q42434135-B92D86A4-D26E-4840-915F-727ED57B31A6Q43121830-E878A6B8-DDE4-44E8-B7E7-1A53C150F906Q51892003-7C0AA1A5-EEC5-43BA-942E-76B7DF1198D9Q51943804-A1096C4B-EB22-4164-ADD6-39C55D01E9CCQ52071909-39FF9ABF-327E-4EEB-85ED-AB6F585B075CQ89892622-B4218142-7F0A-47BF-BADD-D115FA3ED3A7Q90279637-F7CFA670-894A-417E-9F12-03DC134A3EB1Q90374114-D6DDCE6D-6414-4A5E-913D-AC9E00B08C82Q90853108-4986091B-6F56-48CA-8C67-E7C2DC628BF7Q92021625-BE3988A1-831A-4EC0-B9F2-7AABF0E13705Q93131409-B8680169-C74D-475B-AFB1-172EB03E3E69
P50
description
hulumtuese
@sq
researcher
@en
wetenschapper
@nl
հետազոտող
@hy
name
Kristin R Swanson
@ast
Kristin R Swanson
@en
Kristin R Swanson
@es
Kristin R Swanson
@nl
Kristin R Swanson
@sl
type
label
Kristin R Swanson
@ast
Kristin R Swanson
@en
Kristin R Swanson
@es
Kristin R Swanson
@nl
Kristin R Swanson
@sl
prefLabel
Kristin R Swanson
@ast
Kristin R Swanson
@en
Kristin R Swanson
@es
Kristin R Swanson
@nl
Kristin R Swanson
@sl
P1053
D-7599-2011
P106
P108
P21
P2798
P31
P3829
P496
0000-0002-2464-6119