Visual Analysis of Tumor Control Models for Prediction of Radiotherapy Response

Computer Graphics Forum, Volume 35, Number 3, page 231--240 - 2016
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In radiotherapy, tumors are irradiated with a high dose, while surrounding healthy tissues are spared. In clinical research, statistical models were built to quantify the probability that a tumor is effectively treated, given an amount of dose. These models 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 anal- ysis, 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

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BibTex references

@Article { RCMHRBV16,
  author       = "Raidou, Renata Georgia and Casares-Magaz, Oscar and Muren, Ludvig Paul and Heide, U.A. van der  and Roervik, Jarle and Breeuwer, Marcel and Vilanova, Anna",
  title        = "Visual Analysis of Tumor Control Models for Prediction of Radiotherapy Response",
  journal      = "Computer Graphics Forum",
  number       = "3",
  volume       = "35",
  pages        = "231--240",
  year         = "2016",
  url          = ""

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