(3.3.7)--脑科学与影像新技术.pdf
7CanCer InformatICs 2015:14(s4)IntroductionGliomas are one of the most common tumors that originate in the central nervous system(CNS).They derive most plausibly from multipotent progenitor cells,showing histological features similar to astrocytes or oligodendroglial cells,1 and develop usually near the vascular niches.2,3 Glioblastoma multiforme(GBM)is the most aggressive type of glioma,classified as grade IV by the World Health Organization.4,5 The main characteristics of GBM include cellular polymorphism,brisk mitotic activity,microvascular proliferation,necrosis,6 high degree of invasiveness,and infiltrative edema.Particularly for edema,white matter surrounding the lesion is edematogenous;edema consists mainly of infiltrating tumor cells and a lesser proportion of vasculature.7,8 Despite a multimodal treatment strategy and extensive research on possible new treatment approaches during the last decades,mortality has not changed significantly,with average life expectancy ranging between 12 and 15 months.6,9Cancer cells in solid tumors form a mass with augmented metabolic needs due to constant,vigorous changes.As the solid tumor develops,it must generate its own blood supply due to insufficient diffusion of nutrients and oxygen from preexisting vasculature.GBM is a highly vascularized tumor,recruiting preexisting vessels of an already welloxygenated organ like brain and generating neovasculature from excessive levels of circulating vascular endothelial growth factor(VEGF)apart from other proangiogenic molecules.10 Intratumoral hypoxia is considered to be the main driving force of induced angiogenesis within the tumor,in agreement with Folkmans assertion.11 Hypoxiainducible factor(HIF1)is a transcription factor that promotes ischemiadriven angiogenesis through the induction of differential expression of VEGF.VEGF appears to be a key molecule for both the proangiogenic events and the survival of newly formed vessels.12 At a cellular/tissue level,the series of events is a multistep,repeatable process.In brief,first,the preexisting neighboring vessels stretch A Proposed Paradigm Shift in Initializing Cancer Predictive Models with DCE-MRI Based PK Parameters:A Feasibility Studyalexandros roniotis1,*,mariam-eleni oraiopoulou1,2,*,eleftheria tzamali1,eleftherios Kontopodis1,Sofie Van Cauter3,Vangelis Sakkalis1,Kostas marias11Foundation for Research and Technology Hellas(FORTH),Institute of Computer Science,Computational BioMedicine Lab,Heraklion,Greece.2Faculty of Medicine,University of Crete,Heraklion,Greece.3Department of Radiology,University Hospitals Leuven,Leuven,Belgium.*These authors contributed equally as first authors of this work.Supplementary Issue:Computer Simulation,Visualization,and Image Processing of Cancer Data and ProcessesAbstrAct:Glioblastoma multiforme is the most aggressive type of glioma and the most common malignant primary intraaxial brain tumor.In an effort to predict the evolution of the disease and optimize therapeutical decisions,several models have been proposed for simulating the growth pattern of glioma.One of the latest models incorporates cell proliferation and invasion,angiogenic net rates,oxygen consumption,and vasculature.These factors,particularly oxygenation levels,are considered fundamental factors of tumor heterogeneity and compartmentalization.This paper focuses on the initialization of the cancer cell populations and vasculature based on imaging examinations of the patient and presents a feasibility study on vasculature prediction over time.To this end,pharmacokinetic parameters derived from dynamic contrastenhanced magnetic resonance imaging using Tofts model are used in order to feed the model.Ktrans is used as a metric of the density of endothelial cells(vasculature);at the same time,it also helps to discriminate distinct image areas of interest,under a set of assumptions.Feasibility results of applying the model to a real clinical case are presented,including a study on the effect of certain parameters on the pattern of the simulated tumor.Keywords:dynamic contrastenhanced magnetic resonance imaging(DCEMRI),pharmacokinetics,Tofts model,vasculature,tumor physiology,tumor growth model,glioblastoma multiforme,in silico oncology,translational oncologySUPPLEMENT:Computer Simulation,Visualization,and Image Processing of Cancer Data and ProcessesCITATIoN:Roniotis,Oraiopoulou et al.A Proposed Paradigm Shift in Initializing Cancer Predictive Models with DCE-MRI Based PK Parameters:A Feasibility Study.Cancer Informatics 2015:14(s4)718 doi:10.4137/CIn.s19339.RECEIVED:January 30,2015.RESUBMITTED:March 02,2015.ACCEPTED FoR PUBLICATIoN:March 06,2015.ACADEMIC EDIToR:J.T.Efird,Editor in ChiefTYPE:Original ResearchFUNDINg:The research leading to these results has received funding from the European Union Seventh Framework Programme under grant agreement number 600841,Computational Horizons in Cancer(CHIC;http:/www.chic-vph.eu).the authors confirm that the funder had no influence over the study design,content of the article,or selection of this journal.CoMPETINg INTERESTS:Authors disclose no potential conflicts of interest.CoRRESPoNDENCE:roniotisics.forth.gr,kmariasics.forth.grCoPYRIghT:the authors,publisher and licensee Libertas Academica Limited.This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.Paper subject to independent expert blind peer review by minimum of two reviewers.All editorial decisions made by independent academic editor.Upon submission manuscript was subject to anti-plagiarism scanning.Prior to publication all authors have given signed confirmation of agreement to article publication and compliance with all applicable ethical and legal requirements,including the accuracy of author and contributor information,disclosure of competing interests and funding sources,compliance with ethical requirements relating to human and animal study participants,and compliance with any copyright requirements of third parties.This journal is a member of the Committee on Publication Ethics(COPE).Published by Libertas Academica.Learn more about this journal.Roniotis,Oraiopoulou et al8CanCer InformatICs 2015:14(s4)and expand toward tumor as a response to the proangiogenic factors released by cancer cells.The new endothelial cells migrate in a concrete way while degrading concurrently the extracellular matrix,and eventually form tube structures.1315 However,the mechanisms of angiogenesis in tumors fail to promote mature vascular networks and lead to the formation of abnormal,leaky,tortuous,and/or shunt vessels,16 which fail to restore oxygen supply in hypoxic regions,which further induces HIF expression and a perpetual cycle of events from hypoxia and HIF1 expression to VEGF,angiogenesis,and tumor growth.The extreme invasive and neoplastic growth of GBM has motivated the development of mathematical models for augmenting the understanding of the mechanism of glioma growth and predicting the temporal evolution of growth and therapy response.A number of different mathematical models have been proposed,allowing the description of tumor growth and invasion at different spatiotemporal scales.17,18 Among them,discrete mathematical models describe tumor cells as individual entities and study how their microinteractions affect tumor behavior and morphology,19 while continuum approaches are better in describing tumors at tissue level assuming that tumor cells can be represented by densities or volume fractions.2025 More recently,the incorporation of patientspecific,noninvasive imaging data to the existing mathematical approaches seems to be critical for the validation and clinical translation of such models,which is evident by the growing interest in this direction.26,27Magnetic resonance imaging(MRI)is used for the estimation of tumor size,borders,and vasculature.Dynamic contrastenhanced MRI(DCEMRI)is a technique where MRI sequences are obtained before,during,and after the intravenous administration of a low molecular weight gadolinium(Gd)chelate contrast agent.DCEMRI data can later be processed using Tofts model(TM)28 in order to evaluate pharmacokinetic(PK)parameters that are able to quantify the differential leakage during the pass of the tracers bolus in the tumor compartment of the model.DCEMRI is frequently applied in brain oncology because of the prominence of vasculature that characterizes these tumors.Several approaches attempting to decode cancer physiology through imaging biomarkers and use them toward initializing computational models with clinical data have recently emerged.Swanson et al.29 utilized two pretreatment time points of Gdenhanced,T1weighted(T1Gd)and T2weighted(T2)volume data to derive the microscopic tumor growth parameters of invasion and proliferation.Ellingson et al.30 proposed a method of using serial diffusionweighted MR(DWMRI)images in order to estimate the same microscopic parameters.Szeto et al.31,32 combined MRI and positron emission tomography(PET)images from GBM cases to show that tumor aggressiveness estimated by a reactiondiffusion equation and MRI data is correlated with hypoxic burden visible on fluoromisonidazole(FMISO)PET.Recently Yankeelov et al.26,33 emphasized the importance of having a direct relevance to clinical outcome using both diffusion and perfusion MRI for parameterizing a logistic growth model and predicting chemotherapy effect and cellularity in breast cancer.They used serial apparent diffusion coefficient measurements to estimate the tumor cell population and approximate the proliferation rate of tumor cells.An extended TM(ETM)was used in their work in order to incorporate the ve and vp PK parameters into the estimation of tumor cell number.33 However,none of the above has made an effort or presented a framework to predict vascularity macroscopically based on imaging studies and predictive models.In this work,we focus on translating anatomical information and functional imaging biomarkers derived from DCEMRI into tumor characteristics in order to set the initial state of glioma growth models with perspectives on clinical outcome.Our central objective is to demonstrate the feasibility to predict vascularity changes over time under the assumption that the PK parameter Ktrans well characterizes the vasculature in each imaging session.This assumption is based on published clinical studies where it is reported that determination of tracer kinetics by Ktrans is a primary marker for monitoring vascular and angiogenic treatment effect.34 We propose a method based on DCEMRI in order to initialize an extended mathematical model35,36 that describes the spatiotemporal evolution of tumor cells and their microenvironment.The model incorporates three types of cell populations(normoxic,hypoxic,and necrotic),endothelial cells(building vasculature),angiogenic factors,and oxygen concentration.The interactions among the different species are described using a system of coupled partial differential equations.DCEMRI is used for extracting PK information and particularly Ktrans,which is then used for(1)guiding tumors compartment(viable cells and necrosis)selection within the region of interest(ROI)and(2)setting up the initial map of the vasculature.36,37 To our knowledge,this is the first attempt of a glioma model initialization and validation based on the parametric map.The methodology proposed is subsequently applied to a real clinical case where the followup(FU)imaging examination is used as the gold standard for assessing vasculature prediction.The simulated tumor vasculature is correlated with the real tumor evolution,and the effect of certain model parameters is also examined.MethodsIn this work,we mainly address the question of whether the evolution of tumor vascularity can be predicted(through the integration of medical imaging and computational modeling).To elaborate on this,we first chose a welldeveloped tumor growth computational model that accounts for vasculature evolution.36 Then,through DCEMRI patient data,we incorporated spatial information of the initial vasculature Initializing cancer predictive models with DCE-MRI based PK parameters9CanCer InformatICs 2015:14(s4)into the model in the least possible arbitrary way.Although numerous research studies have been focused on tumor vascularization,36,3841 to our knowledge,this is the first attempt where the vasculature estimated by clinical MRI data is introduced to initialize a computational model of tumor growth.DCEMRI,routinely used by clinicians,is used to extract tumor physiology information through PK model analysis.For this reason,we fitted TM,which converts Gd concentration time curves into the more informative PK parameters.In fact,the properties of Ktrans,the PK parameter that we used,depend on the model used to calculate it.TM not only is the most common model for such estimations but also,as shown in comparative studies,is the best in terms of fitting errors and ambiguity compared to other more complex analytical models.42 Among the variables that are evaluated by TM,we chose Ktrans because it is indicative of vascular permeability,and also depends on surface area and blood volume and flow.43,44 The following sections explain our method in detail.PK dce-MrI parameters.Perfusion DCEMRI may characterize the rate of delivery of nutrients via blood into brain tissue parenchyma.The maps that are constructed through the analysis of such data reveal microvasculature information,which can be correlated to the levels of angiogenesis and the grade of the tumor.DCEMRI is performed by acquiring T1weighted images before,during,and after the intravenous injection of a low molecular weight Gd chelate,which enters the blood circulation and is deposited on the tissue without entering the cells.Due to the disruption of the bloodbrain barrier(BBB),tumor sites show a faster and more acute development than healthy background tissue,followed by deamplification after repletion of the available space.45 The boosted infiltration and vascularization of tumor lesions leads to enhanced permeability of contrast agents in blood vessels supplying the tumor,which results in corresponding“bright”image regions in the postcontrast phase during image acquisition.Afterwards,the Gd tracer evacuates the tissue with reentry to the vessels.The concentration of the contrast agent,along with other parameters such as the transport rate constants,is measured when passing through the vessels to the extracellular space,and vice versa.DCEMRI is used for the calculation of the vascular permeability constants through PK models and is a useful tool for characterizing tumor blood vessels and different compartments of the affected tissue.This work uses TM28 in order to quantify the permeability of the vessels by the measurement of Ktrans(min1),the transfer constant between blood plasma and extravascularextracellular space(EES),which is indicative of the volume of blood that flows out of the vessels
