Visual Analysis of Tumor Control Models for Prediction of Radiotherapy Response.

Information

• Publication Type: Journal Paper with Conference Talk
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: 2016
• Journal: EuroVis - Eurographics/IEEE-VGTC Symposium on Visualization 2016
• Lecturer:
  • Kwan Liu Ma
  • Giuseppe Santucci
  • Jarke van Wijk

Abstract

In radiotherapy, tumors are irradiated with a high dose, while surrounding healthy tissues are spared. To quantify the prob-ability 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 informationfrom imaging modalities. In this way, patient-specific properties of tumor tissues are included, improving the radiobiologicalaccuracy of models. Yet, the employed imaging modalities are subject to uncertainties with significant impact on the modelingoutcome, while the models are sensitive to a number of parameter assumptions. Currently, uncertainty and parameter sensitivityare not incorporated in the analysis, due to time and resource constraints. To this end, we propose a visual tool that enablesclinical researchers working on TCP modeling, to explore the information provided by their models, to discover new knowledgeand 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 com-mon assumptions; (3) It enables the identification of inter-patient response variability; (4) It allows starting the analysis fromthe desired treatment outcome, to identify treatment strategies that achieve it. We conducted an evaluation with nine clinicalresearchers. All participants agreed that the proposed visual tool provides better understanding and new opportunities for theexploration and analysis of TCP modeling.

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BibTeX

@article{raidou_eurovis16,
  title =      "Visual Analysis of Tumor Control Models for Prediction of
               Radiotherapy Response.",
  author =     "Renata Raidou and Oscar Casares-Magaz and Ludvig Paul Muren
               and Uulke A van der Heide and Jarle Roervik and Marcel
               Breeuwer and Anna Vilanova i Bartroli",
  year =       "2016",
  abstract =   "In radiotherapy, tumors are irradiated with a high dose,
               while surrounding healthy tissues are spared. To quantify
               the prob-ability 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 informationfrom imaging modalities. In this way,
               patient-specific properties of tumor tissues are included,
               improving the radiobiologicalaccuracy of models. Yet, the
               employed imaging modalities are subject to uncertainties
               with significant impact on the modelingoutcome, while the
               models are sensitive to a number of parameter assumptions.
               Currently, uncertainty and parameter sensitivityare not
               incorporated in the analysis, due to time and resource
               constraints. To this end, we propose a visual tool that
               enablesclinical researchers working on TCP modeling, to
               explore the information provided by their models, to
               discover new knowledgeand 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 com-mon assumptions; (3)
               It enables the identification of inter-patient response
               variability; (4) It allows starting the analysis fromthe
               desired treatment outcome, to identify treatment strategies
               that achieve it. We conducted an evaluation with nine
               clinicalresearchers. All participants agreed that the
               proposed visual tool provides better understanding and new
               opportunities for theexploration and analysis of TCP
               modeling.",
  journal =    "EuroVis - Eurographics/IEEE-VGTC Symposium on Visualization
               2016",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2016/raidou_eurovis16/",
}