Understanding the impact of statistical and machine learning choices on predictive models for radiotherapy

Information

• Publication Type: Conference Paper
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: 2022
• ISBN: 978-3-03868-177-9
• Publisher: The Eurographics Association
• Open Access: yes
• Location: Wien
• Lecturer: Ádam Böröndy
• Event: Eurographics Workshop on Visual Computing for Biology and Medicine (2022)
• DOI: 10.2312/vcbm.20221188
• Booktitle: VCBM 2022: Eurographics Workshop on Visual Computing for Biology and Medicine
• Pages: 5
• Volume: 2022
• Conference date: 22. September 2022 – 23. September 2022
• Pages: 65 – 69
• Keywords: Human-centered computing, Visual Analytics, Life and medical sciences, Applied computing

Abstract

During radiotherapy (RT) planning, an accurate description of the location and shape of the pelvic organs is a critical factor for the successful treatment of the patient. Yet, during treatment, the pelvis anatomy may differ significantly from the planning phase. A series of recent publications, such as PREVIS [FMCM∗21], have examined alternative approaches to analyzing and predicting pelvic organ variability of individual patients. These approaches are based on a combination of several statistical and machine learning methods, which have not been thoroughly and quantitatively evaluated within the scope of pelvic anatomical variability. Several of their design decisions could have an impact on the outcome of the predictive model. The goal of this work is to assess the impact of alternative choices, focusing mainly on the two key-aspects of shape description and clustering, to generate better predictions for new patients. The results of our assessment indicate that resolution-based descriptors provide more accurate and reliable organ representations than state-of-the-art approaches, while different clustering settings (distance metric and linkage) yield only slightly different clusters. Different clustering methods are able to provide comparable results, although when more shape variability is considered their results start to deviate. These results are valuable for understanding the impact of statistical and machine learning choices on the outcomes of predictive models for anatomical variability.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.2312/vcbm.20221188

BibTeX

@inproceedings{boeroendy-2022-uio,
  title =      "Understanding the impact of statistical and machine learning
               choices on predictive models for radiotherapy",
  author =     "Ádam B\"{o}r\"{o}ndy and Katar\'{i}na  Furmanov\'{a} and
               Renata Raidou",
  year =       "2022",
  abstract =   "During radiotherapy (RT) planning, an accurate description
               of the location and shape of the pelvic organs is a critical
               factor for the successful treatment of the patient. Yet,
               during treatment, the pelvis anatomy may differ
               significantly from the planning phase. A series of recent
               publications, such as PREVIS [FMCM∗21], have examined
               alternative approaches to analyzing and predicting pelvic
               organ variability of individual patients. These approaches
               are based on a combination of several statistical and
               machine learning methods, which have not been thoroughly and
               quantitatively evaluated within the scope of pelvic
               anatomical variability. Several of their design decisions
               could have an impact on the outcome of the predictive model.
               The goal of this work is to assess the impact of alternative
               choices, focusing mainly on the two key-aspects of shape
               description and clustering, to generate better predictions
               for new patients. The results of our assessment indicate
               that resolution-based descriptors provide more accurate and
               reliable organ representations than state-of-the-art
               approaches, while different clustering settings (distance
               metric and linkage) yield only slightly different clusters.
               Different clustering methods are able to provide comparable
               results, although when more shape variability is considered
               their results start to deviate. These results are valuable
               for understanding the impact of statistical and machine
               learning choices on the outcomes of predictive models for
               anatomical variability.",
  isbn =       "978-3-03868-177-9",
  publisher =  "The Eurographics Association",
  location =   "Wien",
  event =      "Eurographics Workshop on Visual Computing for Biology and
               Medicine (2022)",
  doi =        "10.2312/vcbm.20221188",
  booktitle =  "VCBM 2022: Eurographics Workshop on Visual Computing for
               Biology and Medicine",
  pages =      "5",
  volume =     "2022",
  pages =      "65--69",
  keywords =   "Human-centered computing, Visual Analytics, Life and medical
               sciences, Applied computing",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2022/boeroendy-2022-uio/",
}