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