PREVIS: Predictive visual analytics of anatomical variability for radiotherapy decision support

Katarína Furmanová, Ludvig Paul Muren, Oscar Casares-Magaz, Vitali Moiseenko, John P. Einck, Sara Pilskog, Renata Raidou
PREVIS: Predictive visual analytics of anatomical variability for radiotherapy decision support
Computers and Graphics, 97:126-138, April 2021. [paper]

Information

Abstract

adiotherapy (RT) requires meticulous planning prior to treatment, where the RT plan is optimized with organ delineations on a pre-treatment Computed Tomography (CT) scan of the patient. The conventionally fractionated treatment usually lasts several weeks. Random changes (e.g., rectal and bladder filling in prostate cancer patients) and systematic changes (e.g., weight loss) occur while the patient is being treated. Therefore, the delivered dose distribution may deviate from the planned. Modern technology, in particular image guidance, allows to minimize these deviations, but risks for the patient remain.

We present PREVIS, a visual analytics tool for:

(i) the exploration and prediction of changes in patient anatomy during the upcoming treatment, and

(ii) the assessment of treatment strategies, with respect to the anticipated changes.

Records of during-treatment changes from a retrospective imaging cohort with complete data are employed in PREVIS, to infer expected anatomical changes of new incoming patients with incomplete data, using a generative model. Abstracted representations of the retrospective cohort partitioning provide insight into an underlying automated clustering, showing main modes of variation for past patients. Interactive similarity representations support an informed selection of matching between new incoming patients and past patients. A Principal Component Analysis (PCA)-based generative model describes the predicted spatial probability distributions of the incoming patient’s organs in the upcoming weeks of treatment, based on observations of past patients. The generative model is interactively linked to treatment plan evaluation, supporting the selection of the optimal treatment strategy.

We present a usage scenario, demonstrating the applicability of PREVIS in a clinical research setting, and we evaluate our visual analytics tool with eight clinical researchers.

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BibTeX

@article{raidou_previs2021,
  title =      "PREVIS: Predictive visual analytics of anatomical
               variability for radiotherapy decision support",
  author =     "Katar\'{i}na  Furmanov\'{a} and Ludvig Paul Muren and Oscar
               Casares-Magaz and Vitali Moiseenko and John P. Einck and
               Sara Pilskog and Renata Raidou",
  year =       "2021",
  abstract =   "adiotherapy (RT) requires meticulous planning prior to
               treatment, where the RT plan is optimized with organ
               delineations on a pre-treatment Computed Tomography (CT)
               scan of the patient. The conventionally fractionated
               treatment usually lasts several weeks. Random changes (e.g.,
               rectal and bladder filling in prostate cancer patients) and
               systematic changes (e.g., weight loss) occur while the
               patient is being treated. Therefore, the delivered dose
               distribution may deviate from the planned. Modern
               technology, in particular image guidance, allows to minimize
               these deviations, but risks for the patient remain.  We
               present PREVIS, a visual analytics tool for:  (i) the
               exploration and prediction of changes in patient anatomy
               during the upcoming treatment, and  (ii) the assessment of
               treatment strategies, with respect to the anticipated
               changes.  Records of during-treatment changes from a
               retrospective imaging cohort with complete data are employed
               in PREVIS, to infer expected anatomical changes of new
               incoming patients with incomplete data, using a generative
               model. Abstracted representations of the retrospective
               cohort partitioning provide insight into an underlying
               automated clustering, showing main modes of variation for
               past patients. Interactive similarity representations
               support an informed selection of matching between new
               incoming patients and past patients. A Principal Component
               Analysis (PCA)-based generative model describes the
               predicted spatial probability distributions of the incoming
               patient’s organs in the upcoming weeks of treatment, based
               on observations of past patients. The generative model is
               interactively linked to treatment plan evaluation,
               supporting the selection of the optimal treatment strategy. 
               We present a usage scenario, demonstrating the applicability
               of PREVIS in a clinical research setting, and we evaluate
               our visual analytics tool with eight clinical researchers.",
  month =      apr,
  doi =        "https://doi.org/10.1016/j.cag.2021.04.010",
  journal =    "Computers and Graphics",
  volume =     "97",
  pages =      "126--138",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2021/raidou_previs2021/",
}