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Longitudinal Image Data for Outcome Modeling
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DOI：10.1016/j.clon.2024.06.053.png)
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Abstract
En 中文
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. (c) 2024 The Author(s). Published by Elsevier Ltd on behalf of The Royal College of Radiologists. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).
Keywords:
Delta radiomics
Longitudinal analysis
Longitudinal data
Medical imaging
Outcome modeling
Radiation oncology
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