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R. L. Siegel, K. D. Miller and A. Jemal, (2016), Cancer Statistics, 2016, CA: A Cancer Journal for Clinicians, 66, No 1, 7-30. 10.3322/caac.2133226742998SiegelR. L.MillerK. D.JemalA.2016Cancer Statistics, 2016CA: A Cancer Journal for Clinicians66173010.3322/caac.2133226742998Open DOISearch in Google Scholar

G. Gigerenzer et al., (2007), Helping Doctors and Patients Make Sense of Health Statistics, Psychological Science in the Public Interest, 8, No 2, 53-96. 10.1111/j.1539-6053.2008.00033.xGigerenzerG.2007Helping Doctors and Patients Make Sense of Health StatisticsPsychological Science in the Public Interest82539610.1111/j.1539-6053.2008.00033.x26161749Open DOISearch in Google Scholar

H. M. Byrne, (2010), Dissecting cancer through mathematics: from the cell to the animal model, Nature Reviews Cancer, 10, 221-230. 10.1038/nrc280820179714ByrneH. M.2010Dissecting cancer through mathematics: from the cell to the animal modelNature Reviews Cancer1022123010.1038/nrc280820179714Open DOISearch in Google Scholar

P. M. Altrock, L. L. Liu and F. Michor, (2015), The mathematics of cancer: integrating quantitative models, Nature Reviews Cancer, 15, 730-745. 10.1038/nrc402926597528AltrockP. M.LiuL. L.MichorF.2015The mathematics of cancer: integrating quantitative modelsNature Reviews Cancer1573074510.1038/nrc402926597528Open DOISearch in Google Scholar

Alexander R. A. Anderson and V. Quaranta, (2008), Integrative mathematical oncology, Nature Reviews Cancer, 8, 227-234. 10.1038/nrc232918273038AndersonAlexander R. A.QuarantaV.2008Integrative mathematical oncologyNature Reviews Cancer822723410.1038/nrc232918273038Open DOISearch in Google Scholar

L. B. Edelman, J. A. Eddy and N. D. Price, (2010), In silico models of cancer, Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 2, No 4, 438-459. 10.1002/wsbm.7520836040EdelmanL. B.EddyJ. A.PriceN. D.2010In silico models of cancerWiley Interdisciplinary Reviews: Systems Biology and Medicine2443845910.1002/wsbm.75315728720836040Open DOISearch in Google Scholar

Thomas S. Deisboeck et al., (2011), Multiscale Cancer Modeling, Annual Review of Biomedical Engineering, 13, 127-155. doi https://dx.doi.org/10.1146%2Fannurev-bioeng-071910-12472910.1146/annurev-bioeng-071910-12472921529163DeisboeckThomas S.2011Multiscale Cancer ModelingAnnual Review of Biomedical Engineering13127155https://dx.doi.org/10.1146%2Fannurev-bioeng-071910-124729388335921529163Open DOISearch in Google Scholar

J. Clairambault, (2011), Optimizing cancer pharmacotherapeutics using mathematical modeling and a systems biology approach, Personalized Medicine, 8, No 3, 271-286. 10.2217/pme.11.20ClairambaultJ.2011Optimizing cancer pharmacotherapeutics using mathematical modeling and a systems biology approachPersonalized Medicine8327128610.2217/pme.11.2029783516Open DOISearch in Google Scholar

T. Jackson, N. Komarova and K. Swanson, (2014), Mathematical Oncology: Using Mathematics to Enable Cancer Discoveries, American Mathematical Monthly, 121, No 9, 840-856. 10.4169/amer.math.monthly.121.09.840JacksonT.KomarovaN.SwansonK.2014Mathematical Oncology: Using Mathematics to Enable Cancer DiscoveriesAmerican Mathematical Monthly121984085610.4169/amer.math.monthly.121.09.840Open DOISearch in Google Scholar

X. L. Li et al., (2015), Integrating Multiscale Modeling with Drug Effects for Cancer Treatment, Cancer Informatics, 14 (Suppl 5), 21-31. 10.4137%2FCIN.S3079726792977LiX. L.2015Integrating Multiscale Modeling with Drug Effects for Cancer TreatmentCancer Informatics14(Suppl 5)213110.4137%2FCIN.S30797Open DOISearch in Google Scholar

F. Azuaje, (2016), Computational models for predicting drug responses in cancer research, Briefings in Bioinformatics, 1-10. 10.1093/bib/bbw065AzuajeF.2016Computational models for predicting drug responses in cancer researchBriefings in Bioinformatics11010.1093/bib/bbw065586231027444372Open DOISearch in Google Scholar

R. Gallasch et al., (2013), Mathematical models for translational and clinical oncology, Journal of Clinical Bioinformatics, 3, No 23, 1-8. 10.1186/2043-9113-3-23GallaschR.2013Mathematical models for translational and clinical oncologyJournal of Clinical Bioinformatics3231810.1186/2043-9113-3-23382862524195863Open DOISearch in Google Scholar

R. A. Gatenby, (2009), A change of strategy in the war on cancer, Nature, 459, 508-509. 10.1038/459508aGatenbyR. A.2009A change of strategy in the war on cancer, Nature45950850910.1038/459508a19478766Open DOISearch in Google Scholar

B. Oronsky et al., (2015), The war on cancer: a military perspective, Frontiers in Oncology, 4, Article 387, 1-5. doi 10.3389/fonc.2014.00387OronskyB. 2015The war on cancer: a military perspectiveFrontiers in Oncology 4Article3871510.3389/fonc.2014.00387430631025674537Open DOISearch in Google Scholar

A. R. Akhmetzhanov and M. E. Hochberg, (2015), Dynamics of preventive vs post-diagnostic cancer control using low-impact measures, eLIFE, 4:e06266, 1-27. 10.7554/eLife.06266.001AkhmetzhanovA. R.HochbergM. E.2015Dynamics of preventive vs post-diagnostic cancer control using low-impact measureseLIFE, 4:e0626612710.7554/eLife.06266.001Open DOISearch in Google Scholar

R. A. Gatenby et al., (2009), Adaptive Therapy, Cancer Research, 69, No 11, 4894-4903. 10.1158/0008-5472.CAN-08-365819487300GatenbyR. A.2009Adaptive TherapyCancer Research69114894490310.1158/0008-5472.CAN-08-3658372882619487300Open DOISearch in Google Scholar

S. Benzekry et al., (2015), Metronomic reloaded: Theoretical models bringing chemotherapy into the era of precision medicine, Seminars in Cancer Biology, 35, 53-61. 10.1016/j.semcancer.2015.09.00226361213BenzekryS.2015Metronomic reloaded: Theoretical models bringing chemotherapy into the era of precision medicineSeminars in Cancer Biology35536110.1016/j.semcancer.2015.09.00226361213Open DOISearch in Google Scholar

N. André, M. Carré and E. Pasquier, (2014), Metronomics: towards personalized chemotherapy?, Nature Reviews Clinical Oncology, 11, 413-431. 10.1038/nrclinonc.2014.8924913374AndréN.CarréM.PasquierE.2014Metronomics: towards personalized chemotherapy?Nature Reviews Clinical Oncology1141343110.1038/nrclinonc.2014.8924913374Open DOISearch in Google Scholar

V. M. Pérez-García and L. A. Pérez-Romasanta, (2016), Extreme protraction for low-grade gliomas: theoretical proof of concept of a novel therapeutical strategy, Mathematical Medicine and Biology, 33, No 3, 253-271. 10.1093/imammb/dqv017Pérez-GarcíaV. M.Pérez-RomasantaL. A.2016Extreme protraction for low-grade gliomas: theoretical proof of concept of a novel therapeutical strategyMathematical Medicine and Biology33325327110.1093/imammb/dqv01725969501Open DOISearch in Google Scholar

Y. Iwasa and F. Michor, (2011), Evolutionary Dynamics of Intratumor Heterogeneity, PLoS ONE, 6, No 3, e17866. 10.1371/journal.pone.001786621479218IwasaY.MichorF.2011Evolutionary Dynamics of Intratumor HeterogeneityPLoS ONE63e1786610.1371/journal.pone.0017866306814821479218Open DOISearch in Google Scholar

B. Stransky and S. J. de Souza, (2013), Modeling tumor evolutionary dynamics, Frontiers in Physiology, 3, Article 480, 1-6. 10.3389/fphys.2012.00480StranskyB.de SouzaS. J.2013Modeling tumor evolutionary dynamicsFrontiers in Physiology3Article 4801610.3389/fphys.2012.00480357268523420281Open DOISearch in Google Scholar

D. Wodarz and N. L. Komarova, (2014), Dynamics of Cancer. Mathematical Foundations of Oncology, World Scientific, Singapore.WodarzD.KomarovaN. L.2014Dynamics of Cancer. Mathematical Foundations of OncologyWorld ScientificSingapore10.1142/8973Search in Google Scholar

A. O. Pisco et al., (2013), Non-Darwinian dynamics in therapy-induced cancer drug resistance, Nature Communications, 4, Article 2467, 1-11. 10.1038/ncomms3467PiscoA. O.2013Non-Darwinian dynamics in therapy-induced cancer drug resistanceNature Communications4Article 246711110.1038/ncomms3467465795324045430Open DOISearch in Google Scholar

S. Prokopiou et al., (2015), A proliferation saturation index to predict radiation response and personalize radiotherapy fractionation, Radiation Oncology, 10, No 159, 1-8. 10.1186/s13014-015-0465-xProkopiouS.2015A proliferation saturation index to predict radiation response and personalize radiotherapy fractionationRadiation Oncology101591810.1186/s13014-015-0465-x452149026227259Open DOISearch in Google Scholar

A. L. Baldock et al., (2014), Patient-Specific Metrics of Invasiveness Reveal Significant Prognostic Benefit of Resection in a Predictable Subset of Gliomas, PLoS ONE, 9, No 10, e99057. 10.1371/journal.pone.0099057BaldockA. L.2014Patient-Specific Metrics of Invasiveness Reveal Significant Prognostic Benefit of Resection in a Predictable Subset of GliomasPLoS ONE910e9905710.1371/journal.pone.0099057421167025350742Open DOISearch in Google Scholar

R. Rockne et al., (2010), Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach, Physics in Medicine and Biology, 55, No 12, 3271-3285. 10.1088/0031-9155/55/12/00120484781RockneR.2010Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approachPhysics in Medicine and Biology55123271328510.1088/0031-9155/55/12/001378655420484781Open DOISearch in Google Scholar

J. Pérez-Beteta et al., (2016), Glioblastoma: does the pre-treatment geometry matter? A postcontrast T1 MRI-based study, European Radiology, 1-9. 10.1007/s00330-016-4453-9Pérez-BetetaJ.2016Glioblastoma: does the pre-treatment geometry matter? A postcontrast T1 MRI-based studyEuropean Radiology1910.1007/s00330-016-4453-927329522Open DOISearch in Google Scholar

Y. Hirata et al., (2012), Mathematically modelling and controlling prostate cancer under intermittent hormone therapy, Asian Journal of Andrology, 14, No 2, 270-277. 10.1038/aja.2011.15522231293HirataY.2012Mathematically modelling and controlling prostate cancer under intermittent hormone therapyAsian Journal of Andrology14227027710.1038/aja.2011.155373509522231293Open DOISearch in Google Scholar

D. Molina et al., (2016), Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival, British Journal of Radiology, 89, No 1064. 10.1259/bjr.20160242MolinaD.2016Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survivalBritish Journal of Radiology89106410.1259/bjr.20160242512489227319577Open DOISearch in Google Scholar

V. Kumar et al., (2012), Radiomics: the process and the challenges, Magnetic Resonance Imaging, 30, No 9, 1234-1248. 10.1016/j.mri.2012.06.01022898692KumarV.2012Radiomics: the process and the challengesMagnetic Resonance Imaging3091234124810.1016/j.mri.2012.06.010356328022898692Open DOISearch in Google Scholar

F. Davnall et al., (2012), Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?, Insights into Imaging, 3, No 6, 573-589. 10.1007/s13244-012-0196-623093486DavnallF.2012Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?Insights into Imaging3657358910.1007/s13244-012-0196-6350556923093486Open DOISearch in Google Scholar

Y. Kuang, J. D. Nagy and S. E. Eikenberry, (2016), Introduction to Mathematical Oncology, Chapman & Hall/CRC Mathematical and Computational Biology.KuangY.NagyJ. D.EikenberryS. E.2016Introduction to Mathematical OncologyChapman & Hall/CRC Mathematical and Computational Biology.10.1201/9781315365404Search in Google Scholar

M. C. Joiner and A. van der Kogel, (2009), Basic Clinical Radiobiology, CRC Press.JoinerM. C.van der KogelA.2009Basic Clinical RadiobiologyCRC Press.10.1201/b15450Search in Google Scholar

J. A. Hobinet et al., (2012), Engaging basic scientists in translational research: identifying opportunities, overcoming obstacles, Journal of Translational Medicine, 10, No 72. 10.1186/1479-5876-10-7222500917HobinetJ. A.2012Engaging basic scientists in translational research: identifying opportunities, overcoming obstaclesJournal of Translational Medicine107210.1186/1479-5876-10-72341962622500917Open DOISearch in Google Scholar

I. D. J. Bross, (1981), Scientific Strategies to Save Your Life: A Statistical Approach to Primary Prevention, Marcel Dekker, New York.BrossI. D. J.1981Scientific Strategies to Save Your Life: A Statistical Approach to Primary PreventionMarcel DekkerNew YorkSearch in Google Scholar

G. K. Isbister and R. Bies, (2014), Pharmacometrics: so much mathematics and why planes achieve their destinations with almost perfect results . . . , British Journal of Clinical Pharmacology, 79, No 1, 1-3. 10.1111/bcp.12514IsbisterG. K.BiesR.2014Pharmacometrics: so much mathematics and why planes achieve their destinations with almost perfect results . . .British Journal of Clinical Pharmacology7911310.1111/bcp.12514429406925223922Open DOISearch in Google Scholar

M. Kline, (1973), Why Johnny can’t add: the failure of the new math, St. Martin’s Press, New York.KlineM.1973Why Johnny can’t add: the failure of the new mathSt. Martin’s PressNew YorkSearch in Google Scholar

G. E. P. Box, (1976), Science and Statistics, Journal of the American Statistical Association, 71, No 356, 791-799. 10.1080/01621459.1976.10480949BoxG. E. P.1976Science and StatisticsJournal of the American Statistical Association7135679179910.1080/01621459.1976.10480949Open DOISearch in Google Scholar

F. Dyson, (2004), A meeting with Enrico Fermi, Nature, 427, 297. 10.1038/427297aDysonF.2004A meeting with Enrico FermiNature42729710.1038/427297a14737148Open DOISearch in Google Scholar

S. Basu and J. Andrews, (2013), Complexity in Mathematical Models of Public Health Policies: A Guide for Consumers of Models, PLoS Medicine, 10, No 10, e1001540. 10.1371/journal.pmed.100154024204214BasuS.AndrewsJ.2013Complexity in Mathematical Models of Public Health Policies: A Guide for Consumers of ModelsPLoS Medicine1010e100154010.1371/journal.pmed.1001540381208324204214Open DOISearch in Google Scholar

A. J. Vickers, (2011), Prediction models in cancer care, CA: A Cancer Journal for Clinicians, 61, No 5, 315-326. 10.3322/caac.2011821732332VickersA. J.2011Prediction models in cancer careCA: A Cancer Journal for Clinicians61531532610.3322/caac.20118318941621732332Open DOISearch in Google Scholar

U. Alon, (2006), An Introduction to Systems Biology: Design Principles of Biological Circuits, Chapman & Hall/CRC, London.AlonU.2006An Introduction to Systems Biology: Design Principles of Biological CircuitsChapman & Hall/CRCLondon10.1201/9781420011432Search in Google Scholar

D. Hanahan and R. A. Weinberg, (2011), Hallmarks of Cancer: The Next Generation, Cell, 144, No 5, 646-674. 10.1016/j.cell.2011.02.01321376230HanahanD.WeinbergR. A.2011Hallmarks of Cancer: The Next GenerationCell144564667410.1016/j.cell.2011.02.01321376230Open DOISearch in Google Scholar

A. Lorz et al., (2013), Populational adaptive evolution, chemotherapeutic resistance and multiple anti-cancer therapies, ESAIM: Mathematical Modelling and Numerical Analysis, 47, No 2, 377-399. 10.1051/m2an/2012031LorzA.2013Populational adaptive evolution, chemotherapeutic resistance and multiple anti-cancer therapiesESAIM: Mathematical Modelling and Numerical Analysis47237739910.1051/m2an/2012031Open DOISearch in Google Scholar

O. Lavi et al., (2013), The Role of Cell Density and Intratumoral Heterogeneity in Multidrug Resistance, Cancer Research, 73, No 24, 7168-7175. 10.1158/0008-5472.CAN-13-176824163380LaviO.2013The Role of Cell Density and Intratumoral Heterogeneity in Multidrug ResistanceCancer Research73247168717510.1158/0008-5472.CAN-13-1768478277324163380Open DOISearch in Google Scholar

J. Greene et al., (2014), The Impact of Cell Density and Mutations in a Model of Multidrug Resistance in Solid Tumors, Bulletin of Mathematical Biology, 76, No 3, 627-653. 10.1007/s11538-014-9936-8GreeneJ.2014The Impact of Cell Density and Mutations in a Model of Multidrug Resistance in Solid TumorsBulletin of Mathematical Biology76362765310.1007/s11538-014-9936-8479410924553772Open DOISearch in Google Scholar

J. Pasquieret al. (2011), Consequences of cell-to-cell P-glycoprotein transfer on acquired multidrug resistance in breast cancer: a cell population dynamics model, Biology Direct, 6, No 5, 1-18. 10.1186/1745-6150-6-5Pasquieret alJ.2011Consequences of cell-to-cell P-glycoprotein transfer on acquired multidrug resistance in breast cancer: a cell population dynamics modelBiology Direct6511810.1186/1745-6150-6-5303898821269489Open DOISearch in Google Scholar

M. R. Durán et al., (2016), Transfer of Drug Resistance Characteristics Between Cancer Cell Subpopulations: A Study Using Simple Mathematical Models, Bulletin of Mathematical Biology, 78, No 6, 1218-1237. 10.1007/s11538-016-0182-027337966DuránM. R.2016Transfer of Drug Resistance Characteristics Between Cancer Cell Subpopulations: A Study Using Simple Mathematical ModelsBulletin of Mathematical Biology7861218123710.1007/s11538-016-0182-027337966Open DOISearch in Google Scholar

A. C. Obenauf et al., (2015), Therapy-induced tumour secretomes promote resistance and tumour progression, Nature, 520, 368-372. 10.1038/nature1433625807485ObenaufA. C.2015Therapy-induced tumour secretomes promote resistance and tumour progressionNature52036837210.1038/nature14336450780725807485Open DOISearch in Google Scholar

J. D. Tenenbaum et al., (2016), An informatics research agenda to support precision medicine: seven key areas, Journal of the American Medical Informatics Association, 23, No 4, 791-795. 10.1093/jamia/ocv213TenenbaumJ. D.2016An informatics research agenda to support precision medicine: seven key areasJournal of the American Medical Informatics Association23479179510.1093/jamia/ocv213492673827107452Open DOISearch in Google Scholar

M. C. Lloyd et al., (2015), Pathology to Enhance Precision Medicine in Oncology: Lessons From Landscape Ecology, Advances in Anatomic Pathology, 22, No 4, 267-272. 10.1097/PAP.000000000000007826050264LloydM. C.2015Pathology to Enhance Precision Medicine in Oncology: Lessons From Landscape EcologyAdvances in Anatomic Pathology22426727210.1097/PAP.0000000000000078472944326050264Open DOISearch in Google Scholar

M. Gerlinger et al., (2012), Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing, The New England Journal of Medicine, 366, 883-892. 10.1056/NEJMoa111320522397650GerlingerM.2012Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion SequencingThe New England Journal of Medicine36688389210.1056/NEJMoa1113205487865322397650Open DOISearch in Google Scholar

N. Just, (2014), Improving tumour heterogeneity MRI assessment with histograms, British Journal of Cancer, 111, 2205-2213. 10.1038/bjc.2014.51225268373JustN.2014Improving tumour heterogeneity MRI assessment with histogramsBritish Journal of Cancer1112205221310.1038/bjc.2014.512426443925268373Open DOISearch in Google Scholar

B. M. Ellingson et al., (2015), Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials, Neuro-Oncology, 17, No 9, 1188-1198. 10.1093/neuonc/nov09526250565EllingsonB. M.2015Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trialsNeuro-Oncology1791188119810.1093/neuonc/nov095458875926250565Open DOISearch in Google Scholar

N. Gordillo, E. Montseny and P. Sobrevilla, (2013), State of the art survey on MRI brain tumor segmentation, Magnetic Resonance Imaging, 31, No 8, 1426-1438. 10.1016/j.mri.2013.05.00223790354GordilloN.MontsenyE.SobrevillaP.2013State of the art survey on MRI brain tumor segmentationMagnetic Resonance Imaging3181426143810.1016/j.mri.2013.05.00223790354Open DOISearch in Google Scholar

R. Meier et al., (2016), Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry, Scientific Reports, 6, Article number: 23376. 10.1038%2Fsrep2337627001047MeierR.2016Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor VolumetryScientific Reports6Article number: 2337610.1038%2Fsrep23376Open DOISearch in Google Scholar

E. Crowley et al., (2013), Liquid biopsy: monitoring cancer-genetics in the blood, Nature Reviews Clinical Oncology, 10, 472-484. 10.1038/nrclinonc.2013.11023836314CrowleyE.2013Liquid biopsy: monitoring cancer-genetics in the bloodNature Reviews Clinical Oncology1047248410.1038/nrclinonc.2013.11023836314Open DOISearch in Google Scholar

L. A. Díaz Jr and A. Bardelii, (2014), Liquid Biopsies: Genotyping Circulating Tumor DNA, Journal of Clinical Oncology, 32, No 6, 579-586. 10.1200/JCO.2012.45.2011JrL. A. DíazBardeliiA.2014Liquid Biopsies: Genotyping Circulating Tumor DNAJournal of Clinical Oncology32657958610.1200/JCO.2012.45.2011482076024449238Open DOISearch in Google Scholar

P. Y. Wen et al., (2010), Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group, Journal of Clinical Oncology, 28, No 11, 1963-1972. 10.1200/JCO.2009.26.3541WenP. Y.2010Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working GroupJournal of Clinical Oncology28111963197210.1200/JCO.2009.26.354120231676Open DOISearch in Google Scholar

D. A. Jaffray, (2012), Image-guided radiotherapy: from current concept to future perspectives, Nature Reviews Clinical Oncology, 9, 688-699. 10.1038/nrclinonc.2012.19423165124JaffrayD. A.2012Image-guided radiotherapy: from current concept to future perspectivesNature Reviews Clinical Oncology968869910.1038/nrclinonc.2012.19423165124Open DOISearch in Google Scholar

J. L. Synge, (1944), Focal Properties of Optical and Electromagnetic Systems, The American Mathematical Monthly, 51, No 4, 185-200. 10.2307/2305772SyngeJ. L.1944Focal Properties of Optical and Electromagnetic SystemsThe American Mathematical Monthly51418520010.2307/2305772Open DOISearch in Google Scholar

L. Bromham, R. Dinnage and X. Hua, (2016), Interdisciplinary research has consistently lower funding success, Nature, 534, 684-687. 10.1038/nature1831527357795BromhamL.DinnageR.HuaX.2016Interdisciplinary research has consistently lower funding successNature53468468710.1038/nature1831527357795Open DOISearch in Google Scholar

http://physics.cancer.govhttp://physics.cancer.govSearch in Google Scholar

https://www.eva2.inserm.fr/EVA/jsp/AppelsOffres/CANCER/index_F.jsphttps://www.eva2.inserm.fr/EVA/jsp/AppelsOffres/CANCER/index_F.jspSearch in Google Scholar

https://www.jsmf.org/programs/csbc/index.htmhttps://www.jsmf.org/programs/csbc/index.htmSearch in Google Scholar

R. Krishna, H. G. Schaefer and O. J. Bjerrum, (2007), Effective integration of systems biology, biomarkers, biosimulation and modelling in streamlining drug development, European Journal of Pharmaceutical Sciences, 31, No 1, 62-67. 10.1016/j.ejps.2007.02.003KrishnaR.SchaeferH. G.BjerrumO. J.2007Effective integration of systems biology, biomarkers, biosimulation and modelling in streamlining drug developmentEuropean Journal of Pharmaceutical Sciences311626710.1016/j.ejps.2007.02.003Open DOISearch in Google Scholar

E. A. Jackson, (1992), Perspectives of Nonlinear Dynamics. Vols 1 & 2. Cambridge University Press, New York.JacksonE. A.1992Perspectives of Nonlinear Dynamics1 & 2Cambridge University PressNew YorkSearch in Google Scholar

E. N. Lorenz, (1963), Deterministic Nonperiodic Flow, Journal of the Atmospheric Sciences, 20, 130-141. 10.1175/1520-0469(1963)020%3C0130:DNF%3E2.0.CO;2LorenzE. N.1963Deterministic Nonperiodic FlowJournal of the Atmospheric Sciences2013014110.1175/1520-0469(1963)020%3C0130:DNF%3E2.0.CO;2Open DOISearch in Google Scholar

R. N. May, (1976), Simple mathematical models with very complicated dynamics, Nature, 261, 459-467. 10.1038/261459a0934280MayR. N.1976Simple mathematical models with very complicated dynamicsNature26145946710.1038/261459a0Open DOISearch in Google Scholar

V. M. Pérez-García et al., (2011), Bright solitary waves in malignant gliomas, Physical Review E, 84, 021921. 10.1103/PhysRevE.84.021921Pérez-GarcíaV. M.2011Bright solitary waves in malignant gliomasPhysical Review E8402192110.1103/PhysRevE.84.021921Open DOISearch in Google Scholar

D. Molina et al., (2016), Geometrical Measures Obtained from Pretreatment Postcontrast T1 Weighted MRIs Predict Survival Benefits from Bevacizumab in Glioblastoma Patients, PLoS ONE, 11, No 8, e0161484. 10.1371/journal.pone.016148427557121MolinaD. M2016Geometrical Measures Obtained from Pretreatment Postcontrast T1 Weighted MRIs Predict Survival Benefits from Bevacizumab in Glioblastoma PatientsPLoS ONE118e016148410.1371/journal.pone.0161484Open DOISearch in Google Scholar

A. Martínez-González et al., (2015), Combined therapies of antithrombotics and antioxidants delay in silico brain tumour progression, Mathematical Medicine and Biology, 32, No 3, 239-262. 10.1093/imammb/dqu002Martínez-GonzálezA.2015Combined therapies of antithrombotics and antioxidants delay in silico brain tumour progressionMathematical Medicine and Biology32323926210.1093/imammb/dqu002Open DOISearch in Google Scholar

J. M. Ayuso et al., (2015), An in vitro model for glioblastoma using microfluidics: Generating pseudopalisades on a chip, Cancer Research, 75, No 23: Abstract nr B04. 10.1158/1538-7445.BRAIN15-B04AyusoJ. M.2015An in vitro model for glioblastoma using microfluidics: Generating pseudopalisades on a chipCancer Research7523Abstract nr B0410.1158/1538-7445.BRAIN15-B04Open DOISearch in Google Scholar

J. M. Ayuso et al., (2016), Glioblastoma on a microfluidic chip: Generating pseudopalisades and enhancing aggressiveness through thrombotic events, to appear in Neuro-Oncology.AyusoJ. M.2016Glioblastoma on a microfluidic chip: Generating pseudopalisades and enhancing aggressiveness through thrombotic eventsto appear in Neuro-Oncology.10.1093/neuonc/now230Search in Google Scholar

J. Frontiñán et al., (2016), Coenzyme Q10 sensitizes human glioblastoma cells to radiation and temozolomide by inhibiting catalase activity, lactate and glutathione synthesis, preprint.FrontiñánJ.2016Coenzyme Q10 sensitizes human glioblastoma cells to radiation and temozolomide by inhibiting catalase activity, lactate and glutathione synthesispreprintSearch in Google Scholar

P. M. Enriquez-Navas et al., (2016), Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer, Science Translational Medicine, 8, No 327. 10.1126/scitranslmed.aad784226912903Enriquez-NavasP. M.2016Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancerScience Translational Medicine832710.1126/scitranslmed.aad7842Open DOISearch in Google Scholar

https://clinicaltrials.gov/ct2/show/NCT02415621https://clinicaltrials.gov/ct2/show/NCT02415621Search in Google Scholar

H. L.P. Harpold, E. C. Alvord and K. R. Swanson, (2007), The Evolution of Mathematical Modeling of Glioma Proliferation and Invasion, Journal of Neuropathology & Experimental Neurology, 66, No 1, 1-9. 10.1097/nen.0b013e31802d9000HarpoldH. L.P.AlvordE. C.SwansonK. R.2007The Evolution of Mathematical Modeling of Glioma Proliferation and InvasionJournal of Neuropathology & Experimental Neurology6611910.1097/nen.0b013e31802d9000Open DOISearch in Google Scholar

C. H. Wang et al., (2009), Prognostic Significance of Growth Kinetics in Newly Diagnosed Glioblastomas Revealed by Combining Serial Imaging with a Novel Biomathematical Model, Cancer Research, 69, No 23, 9133-9140. 10.1158/0008-5472.CAN-08-386319934335WangC. H.2009Prognostic Significance of Growth Kinetics in Newly Diagnosed Glioblastomas Revealed by Combining Serial Imaging with a Novel Biomathematical ModelCancer Research69239133914010.1158/0008-5472.CAN-08-3863346715019934335Open DOISearch in Google Scholar

K. Leder et al., (2014), Mathematical modeling of PDGF-driven glioblastoma reveals optimized radiation dosing schedules, Cell, 156, No 3, 603-616. 10.1016/j.cell.2013.12.02924485463LederK.2014Mathematical modeling of PDGF-driven glioblastoma reveals optimized radiation dosing schedulesCell156360361610.1016/j.cell.2013.12.029392337124485463Open DOISearch in Google Scholar

N. L. Albert et al., (2016), Response Assessment in Neuro-Oncology working group and European Association for Neuro-Oncology recommendations for the clinical use of PET imaging in gliomas, Neuro-Oncology, 18, No 9, 1199-1208. 10.1093/neuonc/now058AlbertN. L.2016Response Assessment in Neuro-Oncology working group and European Association for Neuro-Oncology recommendations for the clinical use of PET imaging in gliomasNeuro-Oncology1891199120810.1093/neuonc/now058499900327106405Open DOISearch in Google Scholar

D. R. Santiago-Dieppa et al., (2014), Extracellular vesicles as a platform for ‘liquid biopsy’ in glioblastoma patients, Expert Review of Molecular Diagnostics, 14, No 7, 819-825. 10.1586/14737159.2014.94319325136839Santiago-DieppaD. R.2014Extracellular vesicles as a platform for ‘liquid biopsy’ in glioblastoma patientsExpert Review of Molecular Diagnostics14781982510.1586/14737159.2014.943193443624425136839Open DOISearch in Google Scholar

http://cordis.europa.eu/project/rcn/110724_en.htmlhttp://cordis.europa.eu/project/rcn/110724_en.htmlSearch in Google Scholar

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