Editorial Predictive Models of Tumour Response to Treatment ...downloads.hindawi.com/journals/cmmm/2015/571351.pdfEditorial Predictive Models of Tumour Response to Treatment Using

EditorialPredictive Models of Tumour Response to Treatment UsingFunctional Imaging Techniques

Loredana G. Marcu,1,2 Eva Bezak,2,3 Iuliana Toma-Dasu,4 and Alexandru Dasu5

1Faculty of Science, University of Oradea, 410087 Oradea, Romania2School of Chemistry & Physics, University of Adelaide, Adelaide, SA 5000, Australia3Department of Physics, Royal Adelaide Hospital, Adelaide, SA 5000, Australia4Medical Radiation Physics Division, Stockholm University and Karolinska Institutet, 171 76 Stockholm, Sweden5Department of Medical and Health Sciences, Radiation Physics, Linkoping University, 581 85 Linkoping, Sweden

Correspondence should be addressed to Loredana G. Marcu; [email protected]

Received 14 December 2014; Accepted 14 December 2014

Copyright © 2015 Loredana G. Marcu et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

The aim of the current special issue was to bring togetherarticles on various aspects of tumour modelling focusing ontreatment response and prediction of clinical outcome basedon functional imaging techniques.

Together with technological and radiobiological advan-ces, tumourmodelling is an emerging area of oncology, whichplays a key role in predicting treatment outcome in cancerpatients. Despite the complexity of tumour biology and itsmicroenvironment, computational andmathematicalmodelsof virtual cancer behaviour and response to treatment aresuccessfully developed and employed for clinical research. Amathematical (delay differential) model with optimal controlfor tumour-immune response with chemoimmunotherapyis presented by F. A. Rihan et al. in their research paperthat describes the interaction of tumour cells and immuneresponse cells with external therapy. In their model, theauthors propose a numerical technique to solve the optimalcontrol problem and identify the best strategy to combinechemotherapy and immunotherapy in order to minimisetumour load while maximising the strength of the immunesystem.

Current diagnostic and imaging methods provide in-depth metabolic information that can be employed fortumour modelling. Latest technical and molecular advancesin the field of nuclear medicine offer new possibilitiesin functional imaging, overcoming some of the confines

imposed by previous diagnostic techniques. Positron emis-sion tomography (PET) is the most advanced technologydesigned to provide metabolic information of disease, treat-ment monitoring, and also evaluation of treatment outcome.Together with other quantitative imaging techniques, suchas dynamic contrast-enhanced MRI, PET is a promisingdiagnostic tool assisting in patient stratification for specifictherapies, evaluation of drug efficacy, assessment of chemo-and radiotherapy outcome, and prediction of survival.

This research idea is underlined in the paper by M.Jennings et al., which focuses on PET-specific parametersand radiotracers in theoretical tumour modelling. The workreviews the use of PET/CT information applied to in silicomodels of tumour growth, development, and behaviourduring treatment. Tumour-related parameters such as cellproliferation, hypoxia, angiogenesis, and pH as well asPET/CT-specific biophysical parameters by means of SUV(standardized uptake value) and Hounsfield units are revis-ited in the context of computational modelling of complexprocessed involving tumour kinetics and treatment outcome.

While several PET radiotracers have been developedfor clinical use to target specific tumour properties, FDG(fluorodeoxyglucose) continues to be the most commonlyused radionuclide in functional imaging as it offers uniqueinformation for tumour detection, staging, target definition,and response monitoring during radio- and chemotherapy.

Hindawi Publishing CorporationComputational and Mathematical Methods in MedicineVolume 2015, Article ID 571351, 2 pageshttp://dx.doi.org/10.1155/2015/571351

2 Computational and Mathematical Methods in Medicine

In this special issue, J. Jeong and J. O. Deasy have modelledthe relationship between FGD uptake and tumour radiore-sistance as a function of the tumour microenvironment.The mechanistic model has considered cellular status to bedictated by glucose and oxygen content, showing that cellsin the intermediate stress state (that receive glucose but notoxygen) present an increased avidity of FDGwhen comparedto well-oxygenated cells. The role of hypoxia and the currentstatus of hypoxia imaging including PET-specific markersare discussed by L. G. Marcu et al. in their review paperwritten in the context of head and neck cancer. Hypoxia-specific PET markers have been implemented in severalclinical trials to quantify hypoxic tumour subvolumes fordose painting and personalised treatment planning. Tracerpharmacokinetics and PET-derived functional parametersserve as important input data for in silicomodels that aim tosimulate or interpret the acquired image.The paper discussestwo main streams of PET tracer modelling: (1) models ofspecific tracer/oxygen dynamics with the aim of simulatingPET images leading to results that are in close agreementwith real PET images and (2) utilisation of PET data withina separate tumour model with the aim of creating a morespecific model that is predictive of response to treatment.The authors have collated analytical compartment modelsof tracer pharmacokinetics, stochastic models of treatmentresponse using probability distribution functions, and reportson model predictions within clinical radiotherapy planningsystems of dose distribution. A pertinent conclusion ofthe review is that the role of computer models based onfunctional imaging techniques in understanding patient-specific tumour behaviour is effusively justified.

The challenge of hypoxia in cancermanagement is furtheranalysed by M. M. Iglesias et al. in a research article lookingat the multimodality functional imaging in radiotherapyplanning and the relationship between dynamic contrast-enhanced (DCE) MRI, diffusion-weighted MRI, and FDG-PET in order to develop predictive individualised models oftumour response to radiotherapy in head and neck cancerpatients. For an optimal biologically guided radiotherapy,to obtain relevant datasets on tumour hypoxia and cellulardensity, there is a need to understand the correlations andinteractions between various functional imaging modalities.Parameters such as SUV (PET), Hounsfield units, dose, ADC(apparent diffusion coefficient) maps (MRI), and contrastexchange coefficients (DCE-MRI) have been recorded foreach patient from the study and the relationship betweenparameters analysed. The authors concluded that the abovefunctional parameters based on different image datasetsare valuable in describing in a complex manner tumouroxygenation and vascularisation, cell density, and tumourmalignity offering, therefore, treatment personalisation andoptimisation.

A central motif of today’s cancer management is per-sonalised treatment planning and delivery. While this trendis featured in all papers of the current special issue, amore focused view upon treatment individualisation viamultidimensional radiotherapy is presented in the work of I.Toma-Dasu and A. Dasu. According to the authors, the keyapproach tomaximise the individualisation of treatment is by

combining multiparameter information from imaging withpredictive information from biopsies and molecular analysesand also by monitoring tumour response to treatment. Theemphasis of the paper is on biologically adapted radiationtherapy (BIOART), which is based on both pretreatment con-ditions and intrinsic responsiveness assessed using functionalimaging techniques. The authors suggest that the BIOARTconcept should replace the simple dose painting approachfor amore optimalmanagement of radioresistant subvolumeswithin the BTV (biological target volume). Functional imag-ing is shown to have the potential to provide a paradigm shiftin treatment planning and optimisation that extends beyondtarget definition.

In silico modelling in cancer research was and continuesto be a very important tool of treatment simulation andoptimisation, contributing to a more personalised medicinefor an improved patient outcome. The use of computationalmodels for treatment assessment and outcome prediction isfast growing and the power of in silico models as preclinicaltools becomes acknowledged. Therefore, multidisciplinaryresearch leading to predictive models of tumour response totreatment using functional imaging techniques needs to beencouraged to enable further developments in the diagnosticand treatment of cancer.

Loredana G. MarcuEva Bezak

Iuliana Toma-DasuAlexandru Dasu

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Editorial Predictive Models of Tumour Response to Treatment ...downloads.hindawi.com/journals/cmmm/2015/571351.pdfEditorial Predictive Models of Tumour Response to Treatment Using