Automatically Measuring Kidney, Liver, and Cyst Volumes in Autosomal Dominant Polycystic Kidney Disease.
Authors
Affiliations (9)
Affiliations (9)
- Department of Radiology, Weill Cornell Medicine, New York City, New York, U.S.A.
- Biomedical Engineering, Cornell University, Ithaca, New York, U.S.A.
- Institute of Biomedical Technologies, Italian National Research Council (ITB-CNR), Segrate (MI), Italy.
- Bioengineering Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Ranica (BG), Italy.
- Department of Medicine, Weill Cornell Medicine, New York City, New York, U.S.A.
- Rogosin Institute, New York, New York, USA.
- Department of Neurology, University of Kansas Medical Center, Kansas City, Kansas, U.S.A.
- Department of Internal Medicine, Division of Nephrology and Hypertension, University of Kansas Medical Center, Kansas City, Kansas, U.S.A.
- Jared Grantham Kidney Institute, University of Kansas Medical Center, Kansas City, Kansas, U.S.A.
Abstract
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. MRI and CT scans from ADPKD patients (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 (MIC). Automatic browser anonymization of DICOM images ensures privacy. Internal validation was conducted on 70 MRIs for kidney and liver segmentations, 46 MRIs for cyst segmentations and performance was compared to 5 open access segmentation models (TotalSegmentator, MR Annotator, Kim, Woznicki and Gregory-Kline). External validation was performed on one single-center dataset (n=58), one multicenter dataset (n=73), CRISP2 (n=30) and PKD-RRC (n=115) MRIs with T2-weighted and T1-weighted images. After training on 720 participants (mean age=48±15, eGFR=74±32 ml/min/1.73m2 and htTKV=826±772ml/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 and 0.95 (kidney), 0.81 (kidney cysts) for the multicenter dataset. Compared to CRISP volumes measured by stereology, mean absolute percent difference was 5.3% (kidneys, n=30), 11% (kidney cysts, n=30) and 5.5% (liver, n=22). Compared to PKD-RRC (n=115), mean absolute percent difference in TKV was 4.9%. TraceOrg is a publicly available web-based tool that automatically measures kidney, liver and cyst volumes from abdominal MRI in ADPKD with high accuracy compared to manual segmentations.