Functional imaging using radiomic features in assessment of lymphoma Review

   


Authors: Mayerhoefer, M. E.; Umutlu, L.; Schöder, H.
Review Title: Functional imaging using radiomic features in assessment of lymphoma
Abstract: Lymphomas are typically large, well-defined, and relatively homogeneous tumors, and therefore represent ideal targets for the use of radiomics. Of the available functional imaging tests, [18F]FDG-PET for body lymphoma and diffusion-weighted MRI (DWI) for central nervous system (CNS) lymphoma are of particular interest. The current literature suggests that two main applications for radiomics in lymphoma show promise: differentiation of lymphomas from other tumors, and lymphoma treatment response and outcome prognostication. In particular, encouraging results reported in the limited number of presently available studies that utilize functional imaging suggest that (1) MRI-based radiomics enables differentiation of CNS lymphoma from glioblastoma, and (2) baseline [18F]FDG-PET radiomics could be useful for survival prognostication, adding to or even replacing commonly used metrics such as standardized uptake values and metabolic tumor volume. However, due to differences in biological and clinical characteristics of different lymphoma subtypes and an increasing number of treatment options, more data are required to support these findings. Furthermore, a consensus on several critical steps in the radiomics workflow –most importantly, image reconstruction and post processing, lesion segmentation, and choice of classification algorithm– is desirable to ensure comparability of results between research institutions. © 2020 Elsevier Inc.
Keywords: positron emission tomography; magnetic resonance imaging; artificial intelligence; lymphoma; radiomics
Journal Title: Methods
Volume: 188
ISSN: 1046-2023
Publisher: Academic Press Inc., Elsevier Science  
Date Published: 2021-04-01
Start Page: 105
End Page: 111
Language: English
DOI: 10.1016/j.ymeth.2020.06.020
PUBMED: 32634555
PROVIDER: scopus
PMCID: PMC8349521
DOI/URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087776839&doi=10.1016%2fj.ymeth.2020.06.020&partnerID=40&md5=4755d2618161d0d80ce68afe451353ab
Notes: Review -- Export Date: 1 April 2021 -- Source: Scopus
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MSK Authors
  1. Heiko Schoder
    542 Schoder
  2. Marius E Mayerhoefer
    70 Mayerhoefer
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