US Veterans Administration Autosomal Dominant Polycystic Kidney Disease Cohort: Demographic, Comorbidity, and Key Laboratory Data Characteristics.

Key Points: We built a cohort of 12,217 patients diagnosed with autosomal dominant polycystic kidney disease from 1999 to 2020 in the national Veteran Affairs electronic medical record system. We characterized the cohort on demographics, comorbidities, and key laboratory measurements.

Background: We used the largest integrated US healthcare system, the Veterans Health Administration, to establish a robust resource for demographic, longitudinal outcome, and predictive modeling studies in autosomal dominant polycystic kidney disease (ADPKD).

nnUNet for Automatic Kidney and Cyst Segmentation in Autosomal Dominant Polycystic Kidney Disease.

Background: Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a genetic disorder that causes uncontrolled kidney cyst growth, leading to kidney volume enlargement and renal function loss over time. Total kidney volume (TKV) and cyst burdens have been used as prognostic imaging biomarkers for ADPKD.

Objective: This study aimed to evaluate nnUNet for automatic kidney and cyst segmentation in T2-weighted (T2W) MRI images of ADPKD patients.

Distinct developmental reprogramming footprint of macrophages during acute kidney injury across species.

Acute kidney injury (AKI) is a common finding in hospitalized patients, particularly those who are critically ill. The development of AKI is associated with several adverse outcomes including mortality, morbidity, progression to chronic kidney disease, and an increase in healthcare expenditure. Despite the well-established negative impact of AKI and rigorous efforts to better define, identify, and implement targeted therapies, the overall approach to the treatment of AKI continues to principally encompass supportive measures.

Deep learning-based automated kidney and cyst segmentation of autosomal dominant polycystic kidney disease using single vs. multi-institutional data.

Purpose: This study aimed to investigate if a deep learning model trained with a single institution's data has comparable accuracy to that trained with multi-institutional data for segmenting kidney and cyst regions in magnetic resonance (MR) images of patients affected by autosomal dominant polycystic kidney disease (ADPKD).

Methods: We used TensorFlow with a Keras custom UNet on 2D slices of 756 MRI images of kidneys with ADPKD obtained from four institutions in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study. The ground truth was determined via a manual plus global thresholding method.