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