The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis

By R. Leijenaar, G. Nalbantov, S. Carvalho, W. van Elmpt, E. Troost, Ronald Boellaard, Hugo J. W. L. Aerts, Robert James Gillies, Philippe Lambin. Scientific Reports, 2015

409 citations recorded by the source corpus.

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) RD, dividing the SUV range into D equally spaced bins, where the intensity resolution (i.e. bin size) varies per image; and (2) RB, 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, RB 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.

Subfields: Oncology, Radiology, Nuclear Medicine and Imaging, Surgery

View Paper: https://www.semanticscholar.org/paper/056759ec13336142ecdd542823e1752b0448e892

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Citations in the corpus

Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures (2017, background): “In addition to this, it is shown that the specific way of SUV discretization has a crucial effect on the resulting textural features, and obviously their interpretation, in both oesophageal(21) and lung carcinomas.(22) This effect turned out to be comparable in both carbon-11 choline and fluorine-18”
Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer (2015, background): “Several studies have investigated the variability of radiomic features with respect to different imaging scanners (39, 40), tumor delineation methods (13, 41), reconstruction methods (42), discretization (43), etc.”
Introduction to Radiomics (2020, background): “Other studies also found radiomics repeatability similar to SUV repeatability (25), and a high sensitivity to image discretization (26).”
Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology (2017, background): “Several studies [19, 20, 23] have shown that absolute discretization results to features with better repeatability and lower sensitivity to changes, with the added advantage of not being volume dependent.”
A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change. (2018, methodology): “Notably, given the heterogeneity in acquisition and reconstruction of positron emission tomography images, caution has been urged in developing quantitative imaging models using positron emission tomography (60-63).”
Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries (2017, positive): “Other prognostic factors have been identified, such as performance status (13-15), weight loss (13, 14), presence of comorbidity (15), chemotherapy use in combination with RT (13, 16), radiation dose (13, 17), tumor size (12, 18-21), and image features, the so-called radiomics approach (22-26).”
LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity. (2018, methodology): “Relative and absolute quantization is available in LIFEx, yielding significantly different results in terms of tissue-type discrimination and value interpretation (11, 12).”
Applications and limitations of radiomics (2016, background): “Despite the potential impact of these factors on quantification, strong prognostic signals of the features could still be found (Cheng et al 2013a, 2014, Cook et al 2013, Aerts et al 2014, Coroller et al 2015, Leijenaar et al 2015a, Parmar et al 2015b).”