| Abstract |
Introduction: Interest in using higher order features of the planned 3D dose distributions (i.e., dosiomics) to predict radiotherapy outcomes is growing. This is driving many retrospective studies where historical data are mined to train machine learning models; however, recent decades have seen considerable advances in dose calculation that could have a direct impact on the dosiomic features such studies seek to extract. Is it necessary to recalculate planned dose distributions using a common algorithm if retrospective datasets from different institutions are included? Does a change in dose calculation grid size part way through a retrospective cohort, introduce bias in the extracted dosiomic features? The purpose of this study is to assess the stability of dosiomic features against variations in three factors: the dose calculation algorithm type, version, and dose grid size.
Methods: Dose distributions for 27 prostate patients who received EBRT were recalculated in the Eclipse Treatment Planning System (Varian Medical Systems, Palo Alto, California, USA) using two algorithms (AAA and Acuros XB), two versions (version 13.6 and 15.6), and three dose grids (2, 2.5 s, and 3 mm) - 12 dose distributions for each patient. Ninety-three dosiomic features were extracted from each dose distribution and each of the following regions-of-interest: high dose PTV (PTV_High), 1 cm rind around PTV_High (PTV_Ring), low dose PTV (PTV_Low), rectum, and bladder using PyRadiomics. The coefficient of variation (CV) was calculated for each dosiomic feature. Hierarchical clustering was used to group features with high and low variability. Three-way repeated measures ANOVA was performed to investigate the effect of the three different factors on dosiomic features that were classified with high variation. Additionally, CVs were calculated for cumulative dose volume histograms (DVHs) to test their ability to detect the variations in dose distributions.
Results: For PTV_Ring, PTV_Low, and rectum, all the dosiomic features had low CV (average CV ≤ 0.26) across the varying dose calculation conditions. For PTV_High, six dosiomic features showed CV > 0.26, and dose calculation algorithm type and grid size were the major sources of within-patient variation. For bladder, one dosiomic feature had average CV > 0.26, but none of the three dose calculation-related factors led to a statistically significant variation. The CVs for all the DVHs were very small (CV < 0.05).
Conclusion: For all the regions-of-interest examined in this study, the majority of the dosiomic features were stable against variations in dose calculation; however, some of the dosiomic features for PTV_High and bladder had significant variations due to differences in dose calculation details. DVHs were detecting less variation than dosiomic features.
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