Automatically Measuring Kidney, Liver, and Cyst Volumes in Autosomal Dominant Polycystic Kidney Disease

Abstract

Key points: TraceOrg is a web-based tool that automatically labels kidney, liver, and cysts, reporting volumes and Mayo Imaging Classification. External validation showed high performance and good generalizability on Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease, Polycystic Kidney Disease-Research Resource Consortium, and other external datasets. Training on multiple pulse sequences enables TraceOrg to process images from a wide variety of protocols.

Background: Kidney, liver, and cyst volumes are important for diagnosis, classification, and management of autosomal dominant polycystic kidney disease (ADPKD) but challenging to measure accurately and reproducibly. Here, we develop a web-based deep learning platform to automatically and robustly measure kidneys, liver, and cyst volumes in ADPKD.

Methods: Magnetic resonance imaging (MRI) and computed tomography scans from patients with ADPKD ( n =611) and participants without ADPKD ( n =109) were used to train a 3D hybrid model combining U-Net and transformer elements for segmenting kidneys, liver, and cysts. The model is implemented as a web-based calculator at www.traceorg.com , providing segmentation labels, volumes, and Mayo Clinic Image Classification. Automatic browser anonymization of digital imaging and communications in medicine images ensures privacy. Internal validation was conducted on 70 MRIs for kidney and liver segmentations and 46 MRIs for cyst segmentations, and performance was compared with five open access segmentation models (TotalSegmentator, MRAnnotator, Kim, Woznicki, and Gregory-Kline). External validation was performed on one single-center dataset ( n =58), one multicenter dataset ( n =73), Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease 2 (CRISP, n =30), and Polycystic Kidney Disease-Research Resource Consortium (PKD-RRC, n =115) MRIs with T2-weighted and T1-weighted images.

Results: After training on 720 participants (mean age=48±15, eGFR=74±32 ml/min per 1.73 m 2 and height-adjusted total kidney volume=826±772 ml/m), TraceOrg internal validation performance achieved high mean Dice scores of 0.97 (kidneys), 0.97 (liver), 0.93 (kidney cysts), and 0.82 (liver cysts) outperforming existing models for ADPKD. External validation showed strong performance with Dice scores of 0.92-0.94 (kidney), 0.87-0.96 (liver), 0.85 (kidney cysts), and 0.76-0.90 (liver cysts) for the single-center dataset and 0.95 (kidney) and 0.81 (kidney cysts) for the multicenter dataset. Compared with CRISP volumes measured by stereology, the mean absolute percent difference was 5.3% (kidneys, n =30), 11% (kidney cysts, n =30), and 5.5% (liver, n =22). Compared with PKD-RRC ( n =115), the mean absolute percent difference in total kidney volume was 4.9%.

Conclusions: TraceOrg, a publicly available web-based tool, automatically measured kidney, liver, and cyst volumes from abdominal MRI in ADPKD with high accuracy compared with manual segmentations.

Podcast: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/JASN/2026_02_03_ASN0000000904.mp3.

Keywords: ADPKD; cystic kidney; cystic kidney disease; kidney; kidney disease; kidney volume; liver cysts; polycystic kidney disease.