Journal
METHODS
Volume 188, Issue -, Pages 105-111Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ymeth.2020.06.020
Keywords
Radiomics; Lymphoma; Artificial intelligence; Positron emission tomography; Magnetic resonance imaging
Funding
- NIH/NCI Cancer Center Support Grant [P30 CA008748]
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Lymphomas are ideal targets for radiomics due to their distinct characteristics, and functional imaging tests play a crucial role in diagnosing and predicting treatment outcomes for lymphoma. However, more data is needed to support the application of radiomics in lymphoma due to differences in lymphoma subtypes and increasing treatment options.
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.
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