Effect of 18F-FDG PET image discretization on radiomic features of patients undergoing definitive radiotherapy for oropharyngeal cancer

Abstract

<p>Purpose: To characterize the effect of different discretization techniques (methods and values) on radiomic features extracted from positron emission tomography (PET) images of patients undergoing definitive radiotherapy for oropharyngeal cancer (OPC) and determine if there are optimal binning techniques associated with the computed texture and histogram measurements.Methods: 71 patients were enrolled in a prospective clinical trial to receive definitive radiotherapy (70Gy) for OPC. PET/CT images were acquired both prior to treatment and two weeks into treatment (i.e., after 20 Gy). All patients were scanned on the same PET/CT imaging system. The gross tumor volume at the primary tumor site was manually segmented on CT and transferred to PET, from which 74 quantitative radiomic features were extracted as potential imaging biomarkers. The sensitivity of feature extraction to common discretization techniques (fixed bin number vs. fixed bin size) was systematically evaluated by measuring radiomic feature values at monotonically increasing bin numbers (32, 64, 128, 256) and bin sizes (0.1, 0.5, 1.0, 5.0). Disparities in radiomics data parameterized by these different discretization settings were quantified based on t-tests of individual features and cross-correlation of matrix-level feature spaces. A discretization invariance score (DIS) was defined as an aggregation of each unique probability of rejecting the null hypothesis that any two discretization techniques produce the same feature value. To evaluate the generalization of these characteristics during treatment, DIS values were compared between pre- and intra-treatment imaging. Results: Only 50% of radiomic features were robust (DIS > 0.7) to changes in bin number, compared to 66% of features when varying bin size. Regardless of discretization technique, grey level variance (DIS=0.0) and high grey level size emphasis (DIS=0.21) were the most sensitive to binning perturbations, while skewness (DIS=1.0) and kurtosis (DIS=1.0) were nearly invariant. The cross-correlation between discretization-specific feature spaces was maximized for fixed bin number and minimized for fixed bin size. Ranked DIS measurements were comparable between pre-treatment and intra-treatment imaging, implying that feature sensitivity is invariant to changes in the absolute feature value over time. Conclusion: The impact of discretization is largely feature-dependent. Individual features demonstrated a non-linear response to systematic changes in discretization parameters, which was captured by our DIS metric. DIS values can be used to optimize downstream radiomic biomarkers, where the prognostic value of individual features may depend on feature-specific discretization. </p>