VAPOR: Visual Analytics for the Exploration of Pelvic Organ Variability in Radiotherapy

Katarína Furmanová, Nicolas Grossmann, Ludvig Paul Muren, Oscar Casares-Magaz, Vitali Moiseenko, John P. Einck, Meister Eduard Gröller, Renata Raidou
VAPOR: Visual Analytics for the Exploration of Pelvic Organ Variability in Radiotherapy
Computer & Graphics, 91:25-38, October 2020.

Information

Abstract

In radiation therapy (RT) for prostate cancer, changes in patient anatomy during treatment might lead to inadequate tumor coverage and higher irradiation of healthy tissues in the nearby pelvic organs. Exploring and analyzing anatomical variability throughout the course of RT can support the design of more robust treatment strategies, while identifying patients that are prone to radiation-induced toxicity. We present VAPOR, a novel application for the exploration of pelvic organ variability in a cohort of patients, across the entire treatment process. Our application addresses: (i) the global exploration and analysis of anatomical variability in an abstracted tabular view, (ii) the local exploration and analysis thereof in anatomical 2D/3D views, where comparative and ensemble visualizations are integrated, and (iii) the correlation of anatomical variability with radiation doses and potential toxicity. The workflow is based on available retrospective cohort data, which include segmentations of the bladder, the prostate, and the rectum through the entire treatment period. VAPOR is applied to four usage scenarios, which were conducted with two medical physicists. Our application provides clinical researchers with promising support in demonstrating the significance of treatment adaptation to anatomical changes.

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@article{furmanova_2020,
  title =      "VAPOR: Visual Analytics for the Exploration of Pelvic Organ
               Variability in Radiotherapy",
  author =     "Katar\'{i}na  Furmanov\'{a} and Nicolas Grossmann and Ludvig
               Paul Muren and Oscar Casares-Magaz and Vitali Moiseenko and
               John P. Einck and Meister Eduard Gr\"{o}ller and Renata
               Raidou",
  year =       "2020",
  abstract =   "In radiation therapy (RT) for prostate cancer, changes in
               patient anatomy during treatment might lead to inadequate
               tumor coverage and higher irradiation of healthy tissues in
               the nearby pelvic organs. Exploring and analyzing anatomical
               variability throughout the course of RT can support the
               design of more robust treatment strategies, while
               identifying patients that are prone to radiation-induced
               toxicity. We present VAPOR, a novel application for the
               exploration of pelvic organ variability in a cohort of
               patients, across the entire treatment process. Our
               application addresses: (i) the global exploration and
               analysis of anatomical variability in an abstracted tabular
               view, (ii) the local exploration and analysis thereof in
               anatomical 2D/3D views, where comparative and ensemble
               visualizations are integrated, and (iii) the correlation of
               anatomical variability with radiation doses and potential
               toxicity. The workflow is based on available retrospective
               cohort data, which include segmentations of the bladder, the
               prostate, and the rectum through the entire treatment
               period. VAPOR is applied to four usage scenarios, which were
               conducted with two medical physicists. Our application
               provides clinical researchers with promising support in
               demonstrating the significance of treatment adaptation to
               anatomical changes.",
  month =      oct,
  doi =        "https://doi.org/10.1016/j.cag.2020.07.001",
  journal =    "Computer & Graphics",
  note =       "Special Section on VCBM 2019",
  volume =     "91",
  pages =      "25--38",
  URL =        "/research/publications/2020/furmanova_2020/",
}