Open AccessGray-level discretization impacts reproducible MRI radiomics texture features
Author(s) -
Loïc Duron,
D. Balvay,
Saskia Vande Perre,
Afef Bouchouicha,
Julien Savatovsky,
Jean-Claude Sadik,
Isabelle ThomassinNaggara,
Laure Fournier,
Augustin Lecler
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0213459
Subject(s) - discretization , concordance correlation coefficient , artificial intelligence , mathematics , computer science , pattern recognition (psychology) , voxel , radiomics , repeatability , reproducibility , nuclear medicine , statistics , medicine , mathematical analysis
Objectives To assess the influence of gray-level discretization on inter- and intra-observer reproducibility of texture radiomics features on clinical MR images. Materials and methods We studied two independent MRI datasets of 74 lacrymal gland tumors and 30 breast lesions from two different centers. Two pairs of readers performed three two-dimensional delineations for each dataset. Texture features were extracted using two radiomics softwares (Pyradiomics and an in-house software). Reproducible features were selected using a combination of intra-class correlation coefficient (ICC) and concordance and coherence coefficient (CCC) with 0.8 and 0.9 as thresholds, respectively. We tested six absolute and eight relative gray-level discretization methods and analyzed the distribution and highest number of reproducible features obtained for each discretization. We also analyzed the number of reproducible features extracted from computer simulated delineations representative of inter-observer variability. Results The gray-level discretization method had a direct impact on texture feature reproducibility, independent of observers, software or method of delineation (simulated vs. human). The absolute discretization consistently provided statistically significantly more reproducible features than the relative discretization. Varying the bin number of relative discretization led to statistically significantly more variable results than varying the bin size of absolute discretization. Conclusions When considering inter-observer reproducible results of MRI texture radiomics features, an absolute discretization should be favored to allow the extraction of the highest number of potential candidates for new imaging biomarkers. Whichever the chosen method, it should be systematically documented to allow replicability of results.