Dr. Arlene B. Chapman Profile

Dr. Arlene B. Chapman

at Univ of Chicago

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Publications (4)

Proceedings Article | 3 April 2024 Poster + Paper

Temporal assessment of magnetic resonance imaging radiomic features to predict renal function decline in patients with autosomal dominant polycystic kidney disease

Linnea Kremer, Madison Orlins, John Trevino, Jordan Fuhrman, Arlene Chapman, Sam Armato

Proceedings Volume 12927, 1292731 (2024) https://doi.org/10.1117/12.3008506

KEYWORDS: Radiomics, Kidney, Diseases and disorders, Magnetic resonance imaging, Feature extraction, Image segmentation

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SPIE Journal Paper | 27 December 2023

Magnetic resonance imaging preprocessing and radiomic features for classification of autosomal dominant polycystic kidney disease genotype

Linnea Kremer, Arlene Chapman, Samuel Armato

JMI, Vol. 10, Issue 06, 064503, (December 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.6.064503

KEYWORDS: Kidney, Magnetic resonance imaging, Radiomics, Feature extraction, Reproducibility, Diseases and disorders, Signal intensity, Image segmentation, Cooccurrence matrices, Image classification

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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.

Proceedings Article | 7 April 2023 Paper

Radiomics-based classification of autosomal dominant polycystic kidney disease (ADPKD) Mayo imaging classification (MIC) and the effect of gray-level discretization

Linnea Kremer, Boris Fosso, Lucy Groothuis, Arlene Chapman, Samuel Armato

Proceedings Volume 12465, 124652Y (2023) https://doi.org/10.1117/12.2654476

KEYWORDS: Kidney, Feature extraction, Radiomics, Cooccurrence matrices, Magnetic resonance imaging, Diseases and disorders, Matrices, Image classification, Correlation coefficients, Voxels

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Proceedings Article | 4 April 2022 Poster + Paper

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

Linnea Kremer, Natalie Perri, Eliza Sorber, Arlene Chapman, Samuel Armato

Proceedings Volume 12033, 120332Z (2022) https://doi.org/10.1117/12.2613164

KEYWORDS: Kidney, Magnetic resonance imaging, Reliability, Tissues, Liver, Feature extraction, Image segmentation, Image classification, Feature selection, Signal processing

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