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

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

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File(s)
41186985.pdf (1.64 MB)
No Access Until
2026-11-04
Permanent Link(s)
https://hdl.handle.net/1813/124367
Collections
Department of Radiology
Author
Xiong, Q.
He, X.
Scalco, E.
Pasini, S.
Zhu, C.
Moghadam, M.C.
Sattar, U.
Davoudi, V.
Bazojoo, V.
Dev, H.
Shen, M.
Hu, Z.
Shih, S.
Prince, S.J.
Blumenfeld, J.D.
Min, R.J.
Chevalier, J.M.
Shimonov, D.
Lepping, R.J.
Yu, A.S.L.
Sabuncu, M.R.
Caroli, A.
Prince, M.R.
Abstract

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: 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. RESULTS: 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%. CONCLUSIONS: 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.

Journal / Series
Journal of the American Society of Nephrology : JASN
Volume & Issue
37(5)
Date Issued
2025-11-04
Publisher
Lippincott, Williams & Wilkins
Keywords
WCM Library Coordinated Deposit
•
Humans
•
Polycystic Kidney, Autosomal Dominant/diagnostic imaging/pathology
•
Magnetic Resonance Imaging
•
Kidney/diagnostic imaging/pathology
•
Cysts/diagnostic imaging/pathology
•
Liver/diagnostic imaging/pathology
•
Female
•
Male
•
Tomography, X-Ray Computed
•
Middle Aged
•
Deep Learning
•
Adult
•
Organ Size
•
Adpkd
•
cystic kidney
•
cystic kidney disease
•
kidney
•
kidney disease
•
kidney volume
•
liver cysts
•
polycystic kidney disease
Related DOI
https://doi.org/10.1681/ASN.0000000904
Previously Published as
Xiong Q, He X, Scalco E, Pasini S, Zhu C, Moghadam MC, Sattar U, Davoudi V, Bazojoo V, Dev H, Shen M, Hu Z, Shih S, Prince SJ, Blumenfeld JD, Min RJ, Chevalier JM, Shimonov D, Lepping RJ, Yu ASL, Sabuncu MR, Caroli A, Prince MR. Automatically Measuring Kidney, Liver, and Cyst Volumes in Autosomal Dominant Polycystic Kidney Disease. Journal of the American Society of Nephrology : JASN. 2025;37(5):995-1009. doi: 10.1681/ASN.0000000904. PMID: 41186985.
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights URI
https://creativecommons.org/licenses/by-nc-nd/4.0/
Type
article

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