Normalization of MRI signal intensity in polycystic kidney disease and the effect on radiomic features

Linnea E. Kremer; Natalie Perri; Eliza Sorber; Arlene Chapman M.D.; Samuel G. Armato III

doi:10.1117/12.2613164

4 April 2022 Normalization of MRI signal intensity in polycystic kidney disease and the effect on radiomic features

Linnea E. Kremer, Natalie Perri, Eliza Sorber, Arlene Chapman M.D., Samuel G. Armato III

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120332Z (2022) https://doi.org/10.1117/12.2613164
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

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.

Citation Download Citation

Linnea E. Kremer, Natalie Perri, Eliza Sorber, Arlene Chapman M.D., and Samuel G. Armato III "Normalization of MRI signal intensity in polycystic kidney disease and the effect on radiomic features", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120332Z (4 April 2022); https://doi.org/10.1117/12.2613164

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