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Radiomics has shown predictive utility in kidney function decline for patients with autosomal dominant polycystic kidney disease (ADPKD), but a limiting factor in the clinical use of radiomics is the standardization of pre-processing parameters, which may be disease specific. Currently, there is a need to identify texture-based differences of riskstratified Mayo Imaging Classification (MIC) groups in ADPKD and the optimal pre-processing parameters for feature extraction. A cohort of 128 age- and gender-matched low/intermediate (MIC classes 1A-1B) and high-risk (MIC classes 1C-1E) patients and their respective T2-weighted fat saturated MRI representative coronal images were used to classify MIC risk using radiomic features extracted from (1) the non-cystic kidney parenchyma and (2) the entire kidney. Graylevel discretization across 8, 16, 32, 64, 128, and 256 gray levels using (1) fixed bin size and (2) fixed bin number methods were used for feature extraction with up-sampling (1.0×1.0 mm2) and down-sampling (2.0×2.0 mm2) pixel resampling. Feature selection using least absolute shrinkage operator (LASSO) combined relevant features into a logistic regression model to classify risk-stratified MIC classes. The non-cystic kidney classification yielded area under the receiver operating characteristic curve (AUC) values that ranged from 0.68-0.84, and the entire kidney texture classification yielded AUC values that ranged from 0.83-0.88. The non-cystic kidney parenchyma AUC values had a decreasing trend with increasing gray levels and were sensitive across pre-processing methods more so than features extracted from the entire kidney. Results suggest there are texture-based differences among risk-stratified MIC classes in both the non-cystic and entire kidney parenchyma that may help better identify patients who are at risk for end-stage kidney disease.
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