Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach.
about
Computational medicine: translating models to clinical careCurrent advances in mathematical modeling of anti-cancer drug penetration into tumor tissuesMathematical modeling of human glioma growth based on brain topological structures: study of two clinical casesA mathematical model of tumor growth and its response to single irradiationEstimating dose painting effects in radiotherapy: a mathematical modelIn silico modelling of tumour margin diffusion and infiltration: review of current status.Mathematical and computational modeling in biology at multiple scalesPatient-specific mathematical neuro-oncology: using a simple proliferation and invasion tumor model to inform clinical practiceFast and high temperature hyperthermia coupled with radiotherapy as a possible new treatment for glioblastoma.Identifying predictors of early growth response and adverse radiation effects of vestibular schwannomas to radiosurgeryAcute and fractionated irradiation differentially modulate glioma stem cell division kineticsIntegrating Imaging Data into Predictive Biomathematical and Biophysical Models of CancerImproving treatment strategies for patients with metastatic castrate resistant prostate cancer through personalized computational modeling.Invasion and proliferation kinetics in enhancing gliomas predict IDH1 mutation status.Post-Surgery Glioma Growth Modeling from Magnetic Resonance Images for Patients with TreatmentA Predictive Mathematical Modeling Approach for the Study of Doxorubicin Treatment in Triple Negative Breast CancerGene therapy enhances chemotherapy tolerance and efficacy in glioblastoma patientsPatient-specific metrics of invasiveness reveal significant prognostic benefit of resection in a predictable subset of gliomasDiscriminating survival outcomes in patients with glioblastoma using a simulation-based, patient-specific response metricToward patient-specific, biologically optimized radiation therapy plans for the treatment of glioblastoma.A computational model incorporating neural stem cell dynamics reproduces glioma incidence across the lifespan in the human populationSpatial Metrics of Tumour Vascular Organisation Predict Radiation Efficacy in a Computational Model.Increased re-entry into cell cycle mitigates age-related neurogenic decline in the murine subventricular zone.Multispecies model of cell lineages and feedback control in solid tumors.Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction-Diffusion Model.Simulating radiotherapy effect in high-grade glioma by using diffusive modeling and brain atlasesNon-standard radiotherapy fractionations delay the time to malignant transformation of low-grade gliomas.From patient-specific mathematical neuro-oncology to precision medicineModeling Tumor-Associated Edema in Gliomas during Anti-Angiogenic Therapy and Its Impact on Imageable TumorResponse classification based on a minimal model of glioblastoma growth is prognostic for clinical outcomes and distinguishes progression from pseudoprogression.A mechanically coupled reaction-diffusion model for predicting the response of breast tumors to neoadjuvant chemotherapy.Mathematical modeling links Wnt signaling to emergent patterns of metabolism in colon cancer.A Patient-Specific Anisotropic Diffusion Model for Brain Tumour Spread.A mathematical model describes the malignant transformation of low grade gliomas: Prognostic implications.Predicting Patient-Specific Radiotherapy Protocols Based on Mathematical Model Choice for Proliferation Saturation Index.Biophysical modeling of brain tumor progression: From unconditionally stable explicit time integration to an inverse problem with parabolic PDE constraints for model calibration.Homeostasis Back and Forth: An Ecoevolutionary Perspective of Cancer.A mathematical approach to virus therapy of glioblastomas.Image based modeling of tumor growth.Kinetic Models for Predicting Cervical Cancer Response to Radiation Therapy on Individual Basis Using Tumor Regression Measured In Vivo With Volumetric Imaging.
P2860
Q24594842-14D65006-C678-4AB6-A6EB-DDD3293A0245Q26827129-D15AC5F1-9DBE-4A02-BEC9-F3E368B0C43BQ27300749-88F212B3-CC1B-4BD0-A736-3A486E6683D0Q27303022-6280A13F-0E86-4041-8BF8-14ABC5BDE61FQ27321048-54B05F9B-B243-4770-A507-8C24AF11BE2DQ27691795-7A1560C6-F71F-4DF3-A725-BF967081BC79Q28082151-11013AF7-3D34-4A06-9189-E485783ED09DQ28083949-A1669603-8006-4658-A887-B6B7EFE01951Q30367908-27C04E4B-3B3D-436E-9CA1-E6F1CD1F8015Q30427640-3275AD52-5F78-4076-AA4C-B3F143DBDA3EQ30537413-2A6F1ED6-40D1-43C0-8C5F-6B21111DF37CQ30659261-947A85C7-74FC-45C3-9802-D3C547C82654Q33589156-34F46181-6B6E-4C58-870F-F154A44F06BDQ33616627-EDE2AF56-7DD2-4911-8982-D1D338AEB09CQ33683150-8EC750EC-E26C-4E2C-BD1F-A1C1713F658EQ33916210-DBE53A30-89EC-4E38-AC62-65665A5B060DQ34117487-94FB5486-F59D-46B6-9907-641B29F90F75Q34414528-AB8B58B2-E177-405A-A267-5006A955A56CQ34570841-BF428A60-0CC0-4F2D-A66A-63BF8D702F31Q35050427-7522412A-0A62-454C-A58D-DD03F3F3F976Q35436721-D6E51229-D9FC-4C20-8B6D-3E257E1B0F7CQ35901811-7A8EE8AB-4FED-41AD-8724-58DC1B4ABDFEQ36005970-7150D39A-6052-4FEA-AFDF-003D7D5F8321Q36216894-B8E420A6-F549-4DD6-8423-966CB610E94FQ36296828-CB851007-3BBA-45BC-9E1F-0223CBC93466Q36316397-76E3A01F-5C66-4BA6-A096-FD06D32C21C8Q36389363-4214D8AB-8F63-4B49-B3EE-FE6E2D314ACDQ36733380-AF05CB14-CAB5-4F77-8830-AC3FA2BDC008Q36739473-1490717F-7B52-49AE-A460-E47851E54101Q37088426-FC339B28-A52A-4FF5-909A-A577AD87085BQ37218930-CC021A38-BA69-48E4-8DC8-264F7E018EC9Q37668798-C519C09E-23F8-4A71-976F-36D7E583AAF5Q38401094-A2AE5E92-A3C1-45DF-8442-2D1E1195F9DBQ38649268-A47B4E36-3B57-4F5F-9B63-F1B74D322EB4Q38691830-9F7B973A-2878-4EE5-A78C-C4708A2E9A1EQ38779205-AC4C2BC5-B4D4-48A6-A959-02C47FD550E5Q38910371-57B6EA09-7DCA-4275-BF71-DF5F00113EB0Q38923793-AF944EDC-681F-4482-BE63-D2D2EBF8A8F4Q38946715-B9C59680-FEE6-4E65-8E6A-5E0665DD9EF7Q39030618-A6D62ECA-42A1-4D0C-89CD-91879714BD51
P2860
Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach.
description
article científic
@ca
article scientifique
@fr
articolo scientifico
@it
artigo científico
@pt
bilimsel makale
@tr
scientific article published on 18 May 2010
@en
vedecký článok
@sk
vetenskaplig artikel
@sv
videnskabelig artikel
@da
vědecký článek
@cs
name
Predicting the efficacy of rad ...... athematical modeling approach.
@en
Predicting the efficacy of rad ...... athematical modeling approach.
@nl
type
label
Predicting the efficacy of rad ...... athematical modeling approach.
@en
Predicting the efficacy of rad ...... athematical modeling approach.
@nl
prefLabel
Predicting the efficacy of rad ...... athematical modeling approach.
@en
Predicting the efficacy of rad ...... athematical modeling approach.
@nl
P2093
P2860
P356
P1476
Predicting the efficacy of rad ...... mathematical modeling approach
@en
P2093
A M Spence
E C Alvord
J K Rockhill
K Hendrickson
T Cloughesy
P2860
P304
P356
10.1088/0031-9155/55/12/001
P577
2010-05-18T00:00:00Z