Radiomic features have been shown to add predictive power to risk-assessment models for future kidney decline in patients with autosomal dominant polycystic kidney disease (ADPKD), and these previous studies utilized only one imaging timepoint. Delta radiomics incorporates image features from multiple imaging timepoints and the change in features across these timepoints. There is a need to investigate utilizing delta radiomics in ADPKD and the benefit of incorporating delta-features in risk-assessment models, taking advantage of imaging that is clinically indicated for these patients. A cohort of 152 patients and their respective T2-weighted fat saturated magnetic resonance imaging coronal images were used to predict progression to chronic kidney disease (CKD) stage 3A, 3B, and >30% reduction in estimated glomerular filtration rate (eGFR) at 60-months follow up using radiomic features at (1) baseline imaging, (2) 24-months follow up, and (3) 24-months delta-features. Prediction models utilizing delta radiomics alone yielded area under the receiver operating characteristic curve (AUC) values that ranged from 0.52-0.55, versus using radiomic features from single timepoints and combined timepoint AUC values 0.67-0.76. Trends of increasing AUC values were observed when combining clinical and radiomics features for predicting CKD stage 3A and >30% reduction in eGFR.
PurposeOur study aims to investigate the impact of preprocessing on magnetic resonance imaging (MRI) radiomic features extracted from the noncystic kidney parenchyma of patients with autosomal dominant polycystic kidney disease (ADPKD) in the task of classifying PKD1 versus PKD2 genotypes, which differ with regard to cyst burden and disease outcome.ApproachThe effect of preprocessing on radiomic features was investigated using a single T2-weighted fat saturated (T2W-FS) MRI scan from PKD1 and PKD2 subjects (29 kidneys in total) from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease study. Radiomic feature reproducibility using the intraclass correlation coefficient (ICC) was computed across MRI normalizations (z-score, reference-tissue, and original image), gray-level discretization, and upsampling and downsampling pixel schemes. A second dataset for genotype classification from 136 subjects T2W-FS MRI images previously enrolled in the HALT Progression of Polycystic Kidney Disease study was matched for age, gender, and Mayo imaging classification class. Genotype classification was performed using a logistic regression classifier and radiomic features extracted from (1) the noncystic kidney parenchyma and (2) the entire kidney. The area under the receiver operating characteristic curve (AUC) was used to evaluate the classification performance across preprocessing methods.ResultsRadiomic features extracted from the noncystic kidney parenchyma were sensitive to preprocessing parameters, with varying reproducibility depending on the parameter. The percentage of features with good-to-excellent ICC scores ranged from 14% to 58%. AUC values ranged between 0.47 to 0.68 and 0.56 to 0.73 for the noncystic kidney parenchyma and entire kidney, respectively.ConclusionsReproducibility of radiomic features extracted from the noncystic kidney parenchyma was dependent on the preprocessing parameters used, and the effect on genotype classification was sensitive to preprocessing parameters. The results suggest that texture features may be indicative of genotype expression in ADPKD.
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.
This study investigated the impact of reference-tissue normalization on radiomic texture features extracted from magnetic resonance images (MRI) of non-cystic kidney parenchyma in patients with autosomal dominant polycystic kidney disease (ADPKD). Image normalization has been shown to improve robustness of features and disease classification. Texture analysis is a promising technique to differentiate between PKD1 and PKD2 variants of ADPKD, which differ in progression and patient outcomes. Regions of interest (ROIs) were placed on the liver and psoas muscle, and Z-score image normalization was performed separately based on the two different ROI placements. This pilot study included 7 PKD1 and 8 PKD2 patients (29 kidney images in total). Right and left kidneys were manually segmented on the single coronal image for each individual kidney that contained the renal artery, and a thresholding tool was used to exclude cysts from the pixels used for feature extraction. This was performed using the open-source platform Pyradiomics on the original and two variants of normalized images. Intraclass correlation coefficients (ICCs) were calculated to compare the reliability of features across normalized images. A linear discriminant analysis (LDA) classifier was used to merge the top-three performing reliable texture features for PKD1 and PKD2 classification based on the receiver operating characteristic (ROC) analysis. Seventeen of the 93 features demonstrated good-to-excellent reliability between normalization approaches. Psoas muscle-normalized images yielded the highest area under the ROC curve (AUC) value of 0.74 (0.53-0.89). Image normalization impacts texture features and classification of PKD1 and PKD2 using MRI-based texture features and should be further explored.
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