Understanding the Impact of Statistical and Machine Learning Choices on Predictive Models for Radiotherapy

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dc.contributor.authorBöröndy, Ádámen_US
dc.contributor.authorFurmanová, Katarínaen_US
dc.contributor.authorRaidou, Renata Georgiaen_US
dc.contributor.editorRenata G. Raidouen_US
dc.contributor.editorBjörn Sommeren_US
dc.contributor.editorTorsten W. Kuhlenen_US
dc.contributor.editorMichael Kroneen_US
dc.contributor.editorThomas Schultzen_US
dc.contributor.editorHsiang-Yun Wuen_US
dc.date.accessioned2022-09-19T11:46:29Z
dc.date.available2022-09-19T11:46:29Z
dc.date.issued2022
dc.description.abstractDuring 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.en_US
dc.description.sectionheadersUncertainties, Ensembles, and Comparisons
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20221188
dc.identifier.isbn978-3-03868-177-9
dc.identifier.issn2070-5786
dc.identifier.pages65-69
dc.identifier.pages5 pages
dc.identifier.urihttps://doi.org/10.2312/vcbm.20221188
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20221188
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Visual Analytics; Applied computing → Life and medical sciences"
dc.subjectHuman
dc.subjectcentered computing → Visual Analytics
dc.subjectApplied computing → Life and medical sciences"
dc.titleUnderstanding the Impact of Statistical and Machine Learning Choices on Predictive Models for Radiotherapyen_US
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VCBM 2022: Eurographics Workshop on Visual Computing for Biology and Medicine