![]() ![]() On-board imaging (OBI) utilizing megavoltage (MV) and kilovoltage (kV) cone beam CT (CBCT) is a widely used imaging technique for daily patient bony alignment and prostate marker alignment 15. This study extracted a total of 137 radiomic features from planning CT images of head and neck cancer patients and warned that variation in delineation can significantly affect some radiomic features. A study by Pavic et al., examined intra-observer variation effects on radiomic features extracted from CT images 12. ![]() The study demonstrated that the impact of contour uncertainty on PET-based radiomic features varied widely and cautioned predictive use in the context of contouring uncertainty for models involving PET-based radiomic features. Contours are typically created by a trained radiation oncologist however, inter-, and intra-observer contouring variation can still be significant when considering radiomics 10, 13.Ī recent study by Yang et al., investigated the impact of contouring variability on PET-based radiomic features for lung cancer 14. Several groups have studied the robustness of radiomic features with respect to contouring variability 3, 4, 5, 10, 11, 12. Research has shown the power of radiomics for many disease sites however, these studies also show variability with respect to imaging modality, reconstruction algorithms, feature selection, and volume of interest (VOI) 3, 4, 5, 6, 7, 8, 9. Previous studies have linked several radiomic features directly to patient survival 2. The extraction and analysis of quantitative radiological features provides valuable information before, during and after radiation therapy (RT) 1. ![]() Radiomics is a promising tool with potential diagnostic, prognostic and predictive powers. The use of auto contours for radiomic feature analysis is promising but must be done with caution. Auto-propagation of prostate contours resulted in nearly 95% of DIRs within tolerance recommendations of TG-132, leading to the majority of features being regarded as acceptably robust. 30 radiomic features had a CCC > 0.90 and 21 had a mean |%∆|RF < 5%. Of the 1,010 fractions under consideration, 94.8% of DIRs were within TG-132 recommended tolerance. Lin’s concordance correlation coefficient (CCC) and mean absolute percent difference in radiomic feature-derived data (mean |%Δ|RF) between auto and manual contours were calculated. Radiomic features from 6 classes were extracted from each contour. Quantitative assessment of deformed versus manually drawn prostate contours on daily CBCT images was performed using Dice similarity coefficient (DSC), mean distance-to-agreement (MDA), difference in center-of-mass position (ΔCM) and difference in volume (ΔVol). The prostate contours were also manually drawn by a physician. Planning CTs (pCTs) were deformably registered to daily cone-beam CTs (CBCTs) to generate prostate contours (auto contours). Twenty-eight prostate cancer patients enrolled on an institutional review board (IRB) approved protocol were selected. This study provides a quantitative assessment of the accuracy of a commercially available deformable image registration (DIR) algorithm to automatically generate prostate contours and additionally investigates the robustness of radiomic features to differing contours.
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