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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F22%3A00126656" target="_blank" >RIV/00216224:14330/22:00126656 - isvavai.cz</a>

Result on the web

<a href="https://diglib.eg.org/handle/10.2312/vcbm20221188" target="_blank" >https://diglib.eg.org/handle/10.2312/vcbm20221188</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2312/vcbm.20221188" target="_blank" >10.2312/vcbm.20221188</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10200 - Computer and information sciences

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Eurographics Workshop on Visual Computing for Biology and Medicine

ISBN

9783038681779

ISSN

2070-5786

e-ISSN

2070-5778

Number of pages

5

Pages from-to

65-69

Publisher name

The Eurographics Association

Place of publication

Neuveden

Event location

Vienna

Event date

Jan 1, 2022

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

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