• 2018-07
  • 2020-07
  • 2020-08
  • 2021-03
  • br Of interest is the value of zone na


    Of interest is the value of zone-naïve prostate features instead of GTV features for assessment of treatment response, because a prostate-focused analysis would allow for stream-lined work-flow without need for time- and computationally-intensive deformable registration. Deformable registration may be required for a GTV-focused analysis given poorly visible prostate in post-EBRT MR images, as per the ex-amples in Fig. 1. Out of the numerous correlating features noted in Table 4, of particular interest were the 61 correlations between ADC feature changes in GTV and prostate volumes. ADC is a broadly ac-cepted biomarker of tumor response post-EBRT, unlike T2 which changes predominantly in the peripheral zone rather than in the central gland or GTV [11]. A very large number of correlations were also noted
    Table 3
    Most significantly different radiomics features for prostate ADC (mean ± 2σ). Bold-face denotes p < 1e−07. The symbol * denotes units of 10−6 mm2/s. The remaining features are dimensionless.
    Feature Baseline Week Six Feature Baseline Week Six
    Table 4
    Numbers of correlated features within prostate and GTV volumes at baseline (BL) and week 6 (Wk 6). refers to the feature change between time-points.
    Prostate, ADC
    Prostate, T2w
    between T2-weighted features and feature changes in the prostate and GTV. This result is consistent with similar T2 reduction post-EBRT for GTV and whole prostate [11].
    Strengths of this study include implementation of standardizable approaches for radiomics, deformable registration, and ADC analyses. Feature extraction utilized the open-source Pyradiomics package and an appropriate Bonferroni correction. ADC accuracy of the Siemens Verio MRI system used in this study has already been validated, using the ice water standardization phantom [16,21]. ADC map generation included b = 0 s/mm2 exclusion for PIRADS-2 compliance [4], but strong con-sistency was demonstrated between the extracted feature sets with and without b = 0 s/mm2 exclusion for both GTV and prostate ROIs. The potential for gradient non-linearity bias was assessed by tracking the offset from isocenter of the Lipo3000 lesion [15]. Just three patients were found to be susceptible to gradient non-linearity bias, and re-peated analyses with these patients excluded found minimal impact on the extracted feature sets.
    The primary study limitation was the absence of analyses with re-gards to clinical outcomes. Instead, methodology for prostate ADC radiomics analysis was established, and promising early response fea-tures were identified. When mature outcomes become available, ma-chine learning and radiogenomics methods are envisaged to test the predictive values of all features and their combinations [22,23]. A 
    Table 5
    Twenty strongest correlations between GTV ADC and prostate ADC feature changes between baseline and week six of radiotherapy. Pearson’s correlation coefficient (rho) for a p-value threshold of 0.00008 is approximately 0.50 for the cohort size of 59 patients. The rho value for these strongest correlations ranges from 0.54 to 0.60.
    Prostate feature GTV feature
    ZoneVariance LowGrayLevelZoneEmphasis Median 10Percentile ZoneVariance ShortRunLowGrayLevelEmphasis RootMeanSquared 10Percentile ZoneVariance LowGrayLevelEmphasis VoxelNum VoxelNum Mean 10Percentile ZoneVariance SmallAreaLowGrayLevelEmphasis ZoneVariance SmallDependenceLowGrayLevelEmphasis LargeDependenceEmphasis LowGrayLevelZoneEmphasis ZoneVariance LowGrayLevelZoneEmphasis 90Percentile Mean VoxelNum RunLengthNonUniformity 90Percentile RootMeanSquared DependenceVariance LowGrayLevelZoneEmphasis RootMeanSquared Mean LargeDependenceEmphasis ShortRunLowGrayLevelEmphasis DependenceVariance ShortRunLowGrayLevelEmphasis LargeDependenceEmphasis LowGrayLevelRunEmphasis DependenceVariance LowGrayLevelRunEmphasis
    predictive algorithm which is robust and less reliant on post-EBRT GTV contours may be favored. It is hoped that a set of the dominant features noted in Tables 2, 3, and 5 will prove to be clinically relevant pre-dictors. This study also did not investigate quantitative T2 mapping or DCE, though DCE images were used for tumor detection. T2 shortening be-tween malignant versus benign prostate and post-EBRT is known, but clinical T2 mapping is superceded by T2-weighted imaging [11,24]. Quantitative DCE-MRI poses multiple standardization challenges [25], and its inclusion with T2w and ADC may not be necessary for more accurate radiomics-based prostate cancer diagnosis or staging [26].
    To conclude, our preliminary data confirmed GTV and prostate radiomic feature changes in ADC and T2-weighted images during radiotherapy. These features warrant further investigation as potential predictive biomarkers of clinical outcomes.