Visual Analysis of Tumor Control Models for Prediction of Radiotherapy Response

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dc.contributor.authorRaidou, Renata Georgiaen_US
dc.contributor.authorCasares-Magaz, Oscaren_US
dc.contributor.authorMuren, Ludvig Paulen_US
dc.contributor.authorHeide, Uulke A. van deren_US
dc.contributor.authorRørvik, Jarleen_US
dc.contributor.authorBreeuwer, Marcelen_US
dc.contributor.authorVilanova, Annaen_US
dc.contributor.editorKwan-Liu Ma and Giuseppe Santucci and Jarke van Wijken_US
dc.date.accessioned2016-06-09T09:32:46Z
dc.date.available2016-06-09T09:32:46Z
dc.date.issued2016en_US
dc.description.abstractIn radiotherapy, tumors are irradiated with a high dose, while surrounding healthy tissues are spared. To quantify the probability that a tumor is effectively treated with a given dose, statistical models were built and employed in clinical research. These are called tumor control probability (TCP) models. Recently, TCP models started incorporating additional information from imaging modalities. In this way, patient-specific properties of tumor tissues are included, improving the radiobiological accuracy of models. Yet, the employed imaging modalities are subject to uncertainties with significant impact on the modeling outcome, while the models are sensitive to a number of parameter assumptions. Currently, uncertainty and parameter sensitivity are not incorporated in the analysis, due to time and resource constraints. To this end, we propose a visual tool that enables clinical researchers working on TCP modeling, to explore the information provided by their models, to discover new knowledge and to confirm or generate hypotheses within their data. Our approach incorporates the following four main components: (1) It supports the exploration of uncertainty and its effect on TCP models; (2) It facilitates parameter sensitivity analysis to common assumptions; (3) It enables the identification of inter-patient response variability; (4) It allows starting the analysis from the desired treatment outcome, to identify treatment strategies that achieve it. We conducted an evaluation with nine clinical researchers. All participants agreed that the proposed visual tool provides better understanding and new opportunities for the exploration and analysis of TCP modeling.en_US
dc.description.number3en_US
dc.description.sectionheadersPrediction and Forecastingen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume35en_US
dc.identifier.doi10.1111/cgf.12899en_US
dc.identifier.issn1467-8659en_US
dc.identifier.pages231-240en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12899en_US
dc.identifier.urihttps://diglib.eg.org:443/handle/10
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.8 [Computer Graphics]en_US
dc.subjectApplicationsen_US
dc.subjectApplicationsen_US
dc.subjecten_US
dc.subjectJ.3 [Computer Applications]en_US
dc.subjectLife and Medical Sciencesen_US
dc.subjectLife and Medical Sciencesen_US
dc.titleVisual Analysis of Tumor Control Models for Prediction of Radiotherapy Responseen_US
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35-Issue 3
EuroVis16: Eurographics Conference on Visualization