Longitudinal Image Data for Outcome Modeling.
Delta radiomics
Longitudinal analysis
Longitudinal data
Medical imaging
Outcome modeling
Radiation oncology
Journal
Clinical oncology (Royal College of Radiologists (Great Britain))
ISSN: 1433-2981
Titre abrégé: Clin Oncol (R Coll Radiol)
Pays: England
ID NLM: 9002902
Informations de publication
Date de publication:
27 Jun 2024
27 Jun 2024
Historique:
received:
23
10
2023
revised:
15
04
2024
accepted:
24
06
2024
medline:
14
7
2024
pubmed:
14
7
2024
entrez:
13
7
2024
Statut:
aheadofprint
Résumé
In oncology, medical imaging is crucial for diagnosis, treatment planning and therapy execution. Treatment responses can be complex and varied and are known to involve factors of treatment, patient characteristics and tumor microenvironment. Longitudinal image analysis is able to track temporal changes, aiding in disease monitoring, treatment evaluation, and outcome prediction. This allows for the enhancement of personalized medicine. However, analyzing longitudinal 2D and 3D images presents unique challenges, including image registration, reliable segmentation, dealing with variable imaging intervals, and sparse data. This review presents an overview of techniques and methodologies in longitudinal image analysis, with a primary focus on outcome modeling in radiation oncology.
Identifiants
pubmed: 39003124
pii: S0936-6555(24)00277-2
doi: 10.1016/j.clon.2024.06.053
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.