Visual Analytics for Digital Radiotherapy: Towards a Comprehensible Pipeline

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The Eurographics Association
Prostate cancer is one of the most frequently occurring types of cancer in males. It is often treated with radiation therapy, which aims at irradiating tumors with a high dose, while sparing the surrounding healthy tissues. In the course of the years, radiotherapy technology has undergone great advancements. However, tumors are not only different from each other, they are also highly heterogeneous within, consisting of regions with distinct tissue characteristics, which should be treated with different radiation doses. Tailoring radiotherapy planning to the specific needs and intra-tumor tissue characteristics of each patient is expected to lead to more effective treatment strategies. Currently, clinical research is moving towards this direction, but an understanding of the specific tumor characteristics of each patient, and the integration of all available knowledge into a personalizable radiotherapy planning pipeline are still required. The present work describes solutions from the field of Visual Analytics, which aim at incorporating the information from the distinct steps of the personalizable radiotherapy planning pipeline, along with eventual sources of uncertainty, into comprehensible visualizations. All proposed solutions are meant to increase the - up to now, limited - understanding and exploratory capabilities of clinical researchers. These approaches contribute towards the interactive exploration, visual analysis and understanding of the involved data and processes at different steps of the radiotherapy planning pipeline, creating a fertile ground for future research in radiotherapy planning.

, booktitle = {
EG 2017 - Dirk Bartz Prize
}, editor = {
Stefan Bruckner and Timo Ropinski
}, title = {{
Visual Analytics for Digital Radiotherapy: Towards a Comprehensible Pipeline
}}, author = {
Raidou, Renata G.
Breeuwer, Marcel
Vilanova, Anna
}, year = {
}, publisher = {
The Eurographics Association
}, ISSN = {
}, ISBN = {}, DOI = {
} }