Mr. John M. Trevino Profile

John M. Trevino

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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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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.

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