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