ArticleScientific reports2015
The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 230 papers, 1 of them a synthesis that pooled it.
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230 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Assessment of Influencing Factors and Robustness of Computable Image Texture Features in Digital Images.Tomography (Ann Arbor, Mich.) · 2025Article
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- Evaluating the prognostic value of radiomics and clinical features in metastatic prostate cancer using [Physical and engineering sciences in medicine · 2025Article
- Enhancing MRI radiomics feature reproducibility and classification performance in Parkinson's disease: a harmonization approach to gray-level discretization variability.Magma (New York, N.Y.) · 2025Article
170 more citing papers are in PubMed but not listed here.
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9 authors.
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Abstract
FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins. We focused on the implications of the manner in which this discretization is implemented. Two methods were evaluated: (1) R(D), dividing the SUV range into D equally spaced bins, where the intensity resolution (i.e. bin size) varies per image; and (2) R(B), maintaining a constant intensity resolution B. Clinical feasibility was assessed on 35 lung cancer patients, imaged before and in the second week of radiotherapy. Forty-four textural features were determined for different D and B for both imaging time points. Feature values depended on the intensity resolution and out of both assessed methods, R(B) was shown to allow for a meaningful inter- and intra-patient comparison of feature values. Overall, patients ranked differently according to feature values–which was used as a surrogate for textural feature interpretation–between both discretization methods. Our study shows that the manner of SUV discretization has a crucial effect on the resulting textural features and the interpretation thereof, emphasizing the importance of standardized methodology in tumor texture analysis.
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