Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childhood Cancer Survivor Study

Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childhood Cancer Survivor Study

BackgroundCardiac disease (CD) is a primary long-term diagnosed pathology among childhood cancer survivors. Dosiomics (radiomics extracted from the dose distribution) have received attention in the past few years to assess better the induced risk of radiotherapy (RT) than standard dosimetric feature...

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Bibliographic Details
Main Authors: Mahmoud Bentriou, Véronique Letort, Stefania Chounta, Brice Fresneau, Duyen Do, Nadia Haddy, Ibrahima Diallo, Neige Journy, Monia Zidane, Thibaud Charrier, Naila Aba, Claire Ducos, Vincent S. Zossou, Florent de Vathaire, Rodrigue S. Allodji, Sarah Lemler
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-12-01
Series:Frontiers in Oncology
Subjects:
survival analysis
dosiomics
cardiac disease
childhood cancer
machine learning
FCCSS
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2024.1241221/full
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https://www.frontiersin.org/articles/10.3389/fonc.2024.1241221/full

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