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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Eurographics Workshop on Visual Computing for Biology and Medicine

ISBN

9783038681779

ISSN

2070-5786

e-ISSN

2070-5778

Počet stran výsledku

5

Strana od-do

65-69

Název nakladatele

The Eurographics Association

Místo vydání

Neuveden

Místo konání akce

Vienna

Datum konání akce

1. 1. 2022

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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