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  1. Extracellular Vesicles Derived from L-MYC Neural Stem Cells Mediate Neuroprotection in 3D Models of Chemotherapy- and Radiation-Induced Neurotoxicity
  2. Intranasal L-MYC Neural Stem Cells Restore Neurovascular and Neuroimmune Homeostasis After TBI via CX3CL1 Upregulation and ICAM-1 Downregulation
  3. State of the art, state of the cell: State-transition modeling of hematopoietic stem cells in CML and AML
  4. Eco-Evolutionary Dynamics of Proliferation Heterogeneity: A Phenotype-Structured Model for Tumor Growth and Treatment Response
  5. Extracellular Vesicles Derived from L-MYC Neural Stem Cells Mediate Neuroprotection in 3D Models of Chemotherapy- and Radiation-Induced Neurotoxicity
  6. Resolving Immune Lineage and Cell-State Heterogeneity in Human PBMCs via Mass Spectrometry-Based Single-Cell Proteomics
  7. Radioligand therapy in combination with CAR T cells overcomes the heterogeneous immunosuppressive prostate tumor microenvironment
  8. Mathematical modeling of neural stem cell migration within brain using multi-fiber tractography
  9. Integrating imaging and mathematical modeling to predict and optimize patient outcomes in oncology
  10. Multiomic State-Transitions Reveal Post-Treatment Transcriptome Desynchronization in Acute Myeloid Leukemia
  11. Eco-Evolutionary Dynamics of Proliferation Heterogeneity: A Phenotype-Structured Model for Tumor Growth and Treatment Response
  12. Computational codes from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  13. Supplementary Figure S.1 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  14. Supplementary Figure S.10 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  15. Supplementary Figure S.11 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  16. Supplementary Figure S.12 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  17. Supplementary Figure S.13 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  18. Supplementary Figure S.14 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  19. Supplementary Figure S.15 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  20. Supplementary Figure S.16 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  21. Supplementary Figure S.17 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  22. Supplementary Figure S.18 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  23. Supplementary Figure S.19 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  24. Supplementary Figure S.2 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  25. Supplementary Figure S.20 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  26. Supplementary Figure S.21 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  27. Supplementary Figure S.22 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  28. Supplementary Figure S.23 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  29. Supplementary Figure S.24 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  30. Supplementary Figure S.25 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  31. Supplementary Figure S.26 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  32. Supplementary Figure S.27 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  33. Supplementary Figure S.28 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  34. Supplementary Figure S.29 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  35. Supplementary Figure S.3 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  36. Supplementary Figure S.30 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  37. Supplementary Figure S.31 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  38. Supplementary Figure S.32 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  39. Supplementary Figure S.4 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  40. Supplementary Figure S.5 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  41. Supplementary Figure S.6 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  42. Supplementary Figure S.7 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  43. Supplementary Figure S.8 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  44. Supplementary Figure S.9 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  45. Supplementary Material from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  46. The future of mathematical oncology in the age of AI
  47. Mathematical modeling of combinatorial antigen targeting with multiple CAR T-cell products for glioblastoma treatment
  48. CAR T-cell and oncolytic virus dynamics and determinants of combination therapy success for glioblastoma
  49. Single‐Cell Analysis of L‐Myc Expressing Neural Stem Cells and Their Extracellular Vesicles Revealed Distinct Progenitor Populations With Neurogenic Potential
  50. Ligand discrimination in immune cells: Signal processing insights into immune dysfunction in ER+ breast cancer
  51. A Roadmap for the Future of Systems Biology in Cancer Research
  52. Interstitial fluid transport dynamics predict glioblastoma invasion and progression
  53. Study of combination CAR T-cell treatment for glioblastoma using mathematical modeling
  54. Use of AlphaFold 2 to predict stabilizing mutations for the R337H variant in the tetramerization domain of TP53.
  55. Longitudinal single cell RNA-sequencing reveals evolution of micro- and macro-states in chronic myeloid leukemia
  56. Lymphocytes and monocytes undergo swift suppression of IL-10R, IL-6R, and IL-2Rβγ signaling under high concentrations of different cytokines
  57. Computational codes from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  58. Supplementary Material from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  59. Figure 4 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  60. Data from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  61. Interstitial fluid transport dynamics predict glioblastoma invasion and progression
  62. Ligand Discrimination in Immune Cells: Signal Processing Insights into Immune Dysfunction in ER+ Breast Cancer
  63. Mathematical Modeling of Neural Stem Cell Migration within Brain using Multi-Fiber Tractography
  64. Modeling cerebral developmentin vitrowith L-MYC-immortalized human neural stem cell-derived organoids
  65. Pharmacological activity of OST-01, a natural product from baccharis coridifolia, on breast cancer cells
  66. miR-142 deficit in T cells during blast crisis promotes chronic myeloid leukemia immune escape
  67. CAR T-cell and oncolytic virus dynamics and determinants of combination therapy success for glioblastoma
  68. Validation of Clinical Dynamic Contrast-Enhanced Magnetic Resonance Imaging Perfusion Modeling and Neoadjuvant Chemotherapy Response Prediction in Breast Cancer Using 18 FDG and 64 C...
  69. Supplementary Figure S.32 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  70. Supplementary Material from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  71. Supplementary Figure S.9 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  72. Supplementary Figure S.31 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  73. Supplementary Figure S.30 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  74. Supplementary Figure S.20 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  75. Supplementary Figure S.14 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  76. Supplementary Figure S.13 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  77. Figure 3 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  78. Supplementary Figure S.8 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  79. Figure 2 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  80. Supplementary Figure S.26 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  81. Supplementary Figure S.7 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  82. Supplementary Figure S.2 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  83. Supplementary Figure S.19 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  84. Supplementary Figure S.12 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  85. Supplementary Figure S.25 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  86. Supplementary Figure S.24 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  87. Supplementary Figure S.18 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  88. Supplementary Figure S.6 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  89. Figure 1 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  90. Supplementary Figure S.11 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  91. Supplementary Figure S.10 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  92. Supplementary Figure S.5 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  93. Computational codes from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  94. Supplementary Figure S.4 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  95. Supplementary Figure S.23 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  96. Supplementary Figure S.17 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  97. Supplementary Figure S.16 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  98. Supplementary Figure S.3 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  99. Supplementary Figure S.1 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  100. Table 1 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  101. Supplementary Figure S.29 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  102. Figure 5 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  103. Supplementary Figure S.22 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  104. Supplementary Figure S.21 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  105. Supplementary Figure S.15 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  106. Supplementary Figure S.28 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  107. Supplementary Figure S.27 from Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  108. CNSC-54. CENTRAL AND BOUNDARY-DRIVEN GROWTH PATTERNS DOMINATE RESPECTIVELY IDH WILD-TYPE AND MUTANT GLIOMAS
  109. Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  110. Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients’ blood
  111. Challenges with sirolimus experimental data to inform QSP model of post‐transplantation cyclophosphamide regimens
  112. Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers
  113. Structural and practical identifiability of contrast transport models for DCE-MRI
  114. Model discovery approach enables noninvasive measurement of intra-tumoral fluid transport in dynamic MRI
  115. Transcriptome free energy can serve as a dynamic patient-specific biomarker in acute myeloid leukemia
  116. Locoregional delivery of IL-13Rα2-targeting CAR-T cells in recurrent high-grade glioma: a phase 1 trial
  117. Targeting Wnt signaling for improved glioma immunotherapy
  118. A novel class of inhibitors that disrupts the stability of integrin heterodimers identified by CRISPR-tiling-instructed genetic screens
  119. State-transition modeling of blood transcriptome predicts disease evolution and treatment response in chronic myeloid leukemia
  120. Structural and practical identifiability of contrast transport models for DCE-MRI
  121. Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients’ blood
  122. Neuroprotective potential of intranasally delivered L-myc immortalized human neural stem cells in female rats after a controlled cortical impact injury
  123. State-transition Modeling of Blood Transcriptome Predicts Disease Evolution and Treatment Response in Chronic Myeloid Leukemia
  124. Enhancing Brain Flow Visualization with Automated 3D Data Processing: A Study on DCE-MRI Data from Mice with Tumors.
  125. Acquired miR-142 deficit in leukemic stem cells suffices to drive chronic myeloid leukemia into blast crisis
  126. Proteomics and mathematical modeling of longitudinal CSF differentiates fast versus slow ALS progression
  127. Model discovery approach enables non-invasive measurement of intra-tumoral fluid transport in dynamic MRI
  128. Differential Distribution of Brain Metastases from Non-Small Cell Lung Cancer Based on Mutation Status
  129. Sequential CAR T cell and targeted alpha immunotherapy in disseminated multiple myeloma
  130. Data driven model discovery and interpretation for CAR T-cell killing using sparse identification and latent variables
  131. Supplementary Methods from State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia
  132. Data from State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia
  133. Supplementary Data Figures S1-S14 from State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia
  134. Supplementary Data Tables S1-S15 from State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia
  135. Data from State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia
  136. Supplementary Data Figures S1-S14 from State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia
  137. Supplementary Data Tables S1-S15 from State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia
  138. Supplementary Methods from State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia
  139. Modeling interaction of Glioma cells and CAR T-cells considering multiple CAR T-cells bindings
  140. Bow-tie architectures in biological and artificial neural networks: Implications for network evolution and assay design
  141. Integration of single-cell transcriptomes and biological function reveals distinct behavioral patterns in bone marrow endothelium
  142. Cancer Genomics and Evolution
  143. Data driven model discovery and interpretation for CAR T-cell killing using sparse identification and latent variables
  144. Spatial organization of heterogeneous immunotherapy target antigen expression in high-grade glioma
  145. Regulation of chromatin accessibility by the histone chaperone CAF-1 sustains lineage fidelity
  146. Dynamic patterns of microRNA expression during acute myeloid leukemia state-transition
  147. Roadmap on plasticity and epigenetics in cancer
  148. MicroRNA networks in FLT3-ITD acute myeloid leukemia
  149. Editorial: Advances in Mathematical and Computational Oncology
  150. Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy
  151. Mathematical modeling of therapeutic neural stem cell migration in mouse brain with and without brain tumors
  152. Comparison of cell state models derived from single-cell RNA sequencing data: graph versus multi-dimensional space
  153. Delivery strategies for cell-based therapies in the brain: overcoming multiple barriers
  154. Targeting miR-126 in inv(16) acute myeloid leukemia inhibits leukemia development and leukemia stem cell maintenance
  155. A Mathematical Modeling Approach for Targeted Radionuclide and Chimeric Antigen Receptor T Cell Combination Therapy
  156. Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy
  157. A Mathematical Modeling Approach for Targeted Radionuclide and Chimeric Antigen Receptor-T Cell Combination Therapy
  158. Treatment-induced arteriolar revascularization and miR-126 enhancement in bone marrow niche protect leukemic stem cells in AML
  159. Intranasally Administered L-Myc-Immortalized Human Neural Stem Cells Migrate to Primary and Distal Sites of Damage after Cortical Impact and Enhance Spatial Learning
  160. Effect of chemotherapy on default mode network connectivity in older women with breast cancer
  161. Concepts and Applications of Information Theory to Immuno-Oncology
  162. Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer
  163. State-Transition Analysis of Time-Sequential microRNA Expression Predicts Development of Acute Myeloid Leukemia
  164. Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data
  165. Cytoplasmic DROSHA and non-canonical mechanisms of MiR-155 biogenesis in FLT3-ITD acute myeloid leukemia
  166. RAMP2-AS1 Regulates Endothelial Homeostasis and Aging
  167. Utilizing Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to Analyze Interstitial Fluid Flow and Transport in Glioblastoma and the Surrounding Parenchyma in Human Patients
  168. Repeatability of tumor perfusion kinetics from dynamic contrast-enhanced MRI in glioblastoma
  169. Interstitial Fluid Flow and Transport in Glioblastoma and Surrounding Parenchyma in Patients
  170. Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer
  171. Comparison of CD38-Targeted α- Versus β-Radionuclide Therapy of Disseminated Multiple Myeloma in an Animal Model
  172. Identifying CD38+ cells in patients with multiple myeloma: first-in-human imaging using copper-64–labeled daratumumab
  173. The Histone Chaperone CAF-1 Sustains Myeloid Lineage Identity
  174. Spatiotemporal strategies to identify aggressive biology in precancerous breast biopsies
  175. State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia
  176. TAG-72–Targeted α-Radionuclide Therapy of Ovarian Cancer Using 225Ac-Labeled DOTAylated-huCC49 Antibody
  177. Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data
  178. Radiomic prediction of mutation status based on MR imaging of lung cancer brain metastases
  179. Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach
  180. P855 High-resolution maps of heterogeneous antigen expression in glioblastoma and implications for immunotherapy
  181. Circulating tumor DNA as an early cancer detection tool
  182. From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response
  183. Synthetic Apparent Diffusion Coefficient for High b-Value Diffusion-Weighted MRI in Prostate
  184. Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data
  185. Introduction to Mathematical Oncology
  186. Glioblastoma Recurrence and the Role of O6-Methylguanine–DNA Methyltransferase Promoter Methylation
  187. Change in Apparent Diffusion Coefficient Is Associated With Local Failure After Stereotactic Body Radiation Therapy for Non-Small Cell Lung Cancer: A Prospective Clinical Trial
  188. Synthetic apparent diffusion coefficient for high b-value diffusion weighted MRI in Prostate
  189. Mathematical modeling with single-cell sequencing data
  190. The 2019 mathematical oncology roadmap
  191. Improved model prediction of glioma growth utilizing tissue-specific boundary effects
  192. From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response
  193. Intrinsic brain activity changes associated with adjuvant chemotherapy in older women with breast cancer: a pilot longitudinal study
  194. Quantitative Evaluation of Intraventricular Delivery of Therapeutic Neural Stem Cells to Orthotopic Glioma
  195. Premature Aging in Young Cancer Survivors
  196. New Developments on Computational Methods and Imaging in Biomechanics and Biomedical Engineering
  197. Towards Model-Based Characterization of Biomechanical Tumor Growth Phenotypes
  198. Subcortical brain iron deposition and cognitive performance in older women with breast cancer receiving adjuvant chemotherapy: A pilot MRI study
  199. Distinct Phenotypic Clusters of Glioblastoma Growth and Response Kinetics Predict Survival
  200. Gray matter density reduction associated with adjuvant chemotherapy in older women with breast cancer
  201. Long-term stability and computational analysis of migration patterns of L-MYC immortalized neural stem cells in the brain
  202. MRI analysis to map interstitial flow in the brain tumor microenvironment
  203. Modelling acute myeloid leukaemia in a continuum of differentiation states
  204. Assessing brain volume changes in older women with breast cancer receiving adjuvant chemotherapy: a brain magnetic resonance imaging pilot study
  205. Comparative dynamics of microglial and glioma cell motility at the infiltrative margin of brain tumours
  206. Early Changes in Tumor Perfusion from T1-Weighted Dynamic Contrast-Enhanced MRI following Neural Stem Cell-Mediated Therapy of Recurrent High-Grade Glioma Correlate with Overall Survival
  207. State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Leukemia Development
  208. Aging in a relativistic biological space-time
  209. Tumor Uptake of 64Cu-DOTA-Trastuzumab in Patients with Metastatic Breast Cancer
  210. Exploiting Homeostatic Repopulation to Increase DC Vaccine Efficacy in Multiple Myeloma
  211. Addendum to ‘A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using18F-FMISO-PET’
  212. A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using 18F-FMISO-PET
  213. Patient-Specific Metrics of Invasiveness Reveal Significant Prognostic Benefit of Resection in a Predictable Subset of Gliomas
  214. Gene therapy enhances chemotherapy tolerance and efficacy in glioblastoma patients
  215. Invasion and proliferation kinetics in enhancing gliomas predict IDH1 mutation status
  216. Toward Patient-Specific, Biologically Optimized Radiation Therapy Plans for the Treatment of Glioblastoma
  217. A digital reference object for the 3D Hoffman brain phantom for characterization of PET neuroimaging quality
  218. Response Classification Based on a Minimal Model of Glioblastoma Growth Is Prognostic for Clinical Outcomes and Distinguishes Progression from Pseudoprogression
  219. Discriminating Survival Outcomes in Patients with Glioblastoma Using a Simulation-Based, Patient-Specific Response Metric
  220. From Patient-Specific Mathematical Neuro-Oncology to Precision Medicine
  221. Modeling Tumor-Associated Edema in Gliomas during Anti-Angiogenic Therapy and Its Impact on Imageable Tumor
  222. Adaptive IMRT using a multiobjective evolutionary algorithm integrated with a diffusion–invasion model of glioblastoma
  223. Quantifying the Role of Angiogenesis in Malignant Progression of Gliomas: In Silico Modeling Integrates Imaging and Histology
  224. Applying a patient-specific bio-mathematical model of glioma growth to develop virtual [18F]-FMISO-PET images
  225. The role of IDH1 mutated tumour cells in secondary glioblastomas: an evolutionary game theoretical view
  226. Magnetic Resonance Imaging Characteristics of Glioblastoma Multiforme: Implications for Understanding Glioma Ontogeny
  227. Predicting the efficacy of radiotherapy in individual glioblastoma patientsin vivo:a mathematical modeling approach
  228. Prognostic Significance of Growth Kinetics in Newly Diagnosed Glioblastomas Revealed by Combining Serial Imaging with a Novel Biomathematical Model
  229. Quantitative Metrics of Net Proliferation and Invasion Link Biological Aggressiveness Assessed by MRI with Hypoxia Assessed by FMISO-PET in Newly Diagnosed Glioblastomas
  230. Complementary but Distinct Roles for MRI and18F-Fluoromisonidazole PET in the Assessment of Human Glioblastomas
  231. A mathematical model for brain tumor response to radiation therapy
  232. Velocity of Radial Expansion of Contrast-enhancing Gliomas and the Effectiveness of Radiotherapy in Individual Patients: a Proof of Principle