Publications by authors named "Ines Chicos"
Deployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI.
Radiol Artif Intell
March 2022
This study develops, validates, and deploys deep learning for automated total kidney volume (TKV) measurement (a marker of disease severity) on T2-weighted MRI studies of autosomal dominant polycystic kidney disease (ADPKD). The model was based on the U-Net architecture with an EfficientNet encoder, developed using 213 abdominal MRI studies in 129 patients with ADPKD. Patients were randomly divided into 70% training, 15% validation, and 15% test sets for model development.
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- - Autosomal dominant polycystic kidney disease (ADPKD) is a genetic condition causing multiple cysts in kidneys, primarily due to mutations in the PKD1 and PKD2 genes that code for polycystin proteins.
- - Researchers analyzed DNA from 90 kidney cysts from 24 patients, discovering that 93% of these cysts had harmful somatic mutations in PKD1 or PKD2, mainly resulting in significant gene alterations like truncations.
- - The study suggests that cyst formation in ADPKD follows a cellular recessive mechanism, implicating both inherited and acquired mutations in the PKD1 and PKD2 genes within kidney cyst cells.
Background: Screening for rapidly progressing autosomal dominant polycystic kidney disease (ADPKD) is necessary for assigning and monitoring therapies. Height-adjusted total kidney volume (ht-TKV) is an accepted biomarker for clinical prognostication, but represents only a small fraction of information on abdominal MRI.
Purpose: To investigate the utility of other MR features of ADPKD to predict progression.