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Kidney Int. 2017 Nov;92(5):1206-1216. doi: 10.1016/j.kint.2017.03.026. Epub 2017 May 20.

Image texture features predict renal function decline in patients with autosomal dominant polycystic kidney disease.

Author information

1
Department of Radiology, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
2
Division of Nephrology and Hypertension, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
3
University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
4
The Kidney Institute, Department of Internal Medicine, Kansas University Medical Center, Kansas City, Kansas, USA.
5
Department of Medicine, University of Chicago, Chicago, Illinois, USA.
6
Division of Nephrology, University of Alabama and Department of Veterans Affairs Medical Center, Birmingham, Alabama, USA.
7
Legacy Transplant Services, Legacy Good Samaritan Hospital, Portland, Oregon, USA.
8
Department of Radiology, Mayo Clinic College of Medicine, Rochester, Minnesota, USA. Electronic address: bje@mayo.edu.

Abstract

Magnetic resonance imaging (MRI) examinations provide high-resolution information about the anatomic structure of the kidneys and are used to measure total kidney volume (TKV) in patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD). Height-adjusted TKV (HtTKV) has become the gold-standard imaging biomarker for ADPKD progression at early stages of the disease when estimated glomerular filtration rate (eGFR) is still normal. However, HtTKV does not take advantage of the wealth of information provided by MRI. Here we tested whether image texture features provide additional insights into the ADPKD kidney that may be used as complementary information to existing biomarkers. A retrospective cohort of 122 patients from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study was identified who had T2-weighted MRIs and eGFR values over 70 mL/min/1.73m2 at the time of their baseline scan. We computed nine distinct image texture features for each patient. The ability of each feature to predict subsequent progression to CKD stage 3A, 3B, and 30% reduction in eGFR at eight-year follow-up was assessed. A multiple linear regression model was developed incorporating age, baseline eGFR, HtTKV, and three image texture features identified by stability feature selection (Entropy, Correlation, and Energy). Including texture in a multiple linear regression model (predicting percent change in eGFR) improved Pearson correlation coefficient from -0.51 (using age, eGFR, and HtTKV) to -0.70 (adding texture). Thus, texture analysis offers an approach to refine ADPKD prognosis and should be further explored for its utility in individualized clinical decision making and outcome prediction.

KEYWORDS:

gray-level co-occurrence matrix; magnetic resonance imaging; multiple linear regression; polycystic kidney disease; total kidney volume

PMID:
28532709
PMCID:
PMC5651185
[Available on 2018-11-01]
DOI:
10.1016/j.kint.2017.03.026
[Indexed for MEDLINE]

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