Validation of a semi-automatic method to measure total liver volumes in polycystic liver disease on computed tomography - high speed and accuracy.


Journal

European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774

Informations de publication

Date de publication:
May 2023
Historique:
received: 15 06 2022
accepted: 29 11 2022
revised: 27 09 2022
medline: 25 4 2023
pubmed: 15 1 2023
entrez: 14 1 2023
Statut: ppublish

Résumé

Polycystic liver disease (PLD) is characterized by growth of hepatic cysts, causing hepatomegaly. Disease severity is determined using total liver volume (TLV), which can be measured from computed tomography (CT). The gold standard is manual segmentation which is time-consuming and requires expert knowledge of the anatomy. This study aims to validate the commercially available semi-automatic MMWP (Multimodality Workplace) Volume tool for CT scans of PLD patients. We included adult patients with one (n = 60) or two (n = 46) abdominal CT scans. Semi-automatic contouring was compared with manual segmentation, using comparison of observed volumes (cross-sectional) and growth (longitudinal), correlation coefficients (CC), and Bland-Altman analyses with bias and precision, defined as the mean difference and SD from this difference. Inter- and intra-reader variability were assessed using coefficients of variation (CV) and we assessed the time to perform both procedures. Median TLV was 5292.2 mL (IQR 3141.4-7862.2 mL) at baseline. Cross-sectional analysis showed high correlation and low bias and precision between both methods (CC 0.998, bias 1.62%, precision 2.75%). Absolute volumes were slightly higher for semi-automatic segmentation (manual 5292.2 (3141.4-7862.2) versus semi-automatic 5432.8 (3071.9-7960.2) mL, difference 2.7%, p < 0.001). Longitudinal analysis demonstrated that semi-automatic segmentation accurately measures liver growth (CC 0.908, bias 0.23%, precision 4.04%). Inter- and intra-reader variability were small (2.19% and 0.66%) and comparable to manual segmentation (1.21% and 0.63%) (p = 0.26 and p = 0.37). Semi-automatic segmentation was faster than manual tracing (19 min versus 50 min, p = 0.009). Semi-automatic liver segmentation is a fast and accurate method to determine TLV and liver growth in PLD patients. • Semi-automatic liver segmentation using the commercially available MMWP volume tool accurately determines total liver volume as well as liver growth over time in polycystic liver disease patients. • This method is considerably faster than manual segmentation through the use of Hounsfield unit settings. • We used a real-life CT set for the validation and showed that the semi-automatic tool measures accurately regardless of contrast used for the CT scan or not, presence of polycystic kidneys, liver volume, and previous invasive treatment for polycystic liver disease.

Identifiants

pubmed: 36640173
doi: 10.1007/s00330-022-09346-6
pii: 10.1007/s00330-022-09346-6
pmc: PMC10121488
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

3222-3231

Informations de copyright

© 2023. The Author(s).

Références

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Auteurs

Sophie E Aapkes (SE)

Department Nephrology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB, Groningen, The Netherlands.

Thijs R M Barten (TRM)

European Reference Center RARE-LIVER, Hamburg, Germany.
Department Gastroenterology and Hepatology, Radboud University Medical Center, Nijmegen, the Netherlands.

Walter Coudyzer (W)

Department Radiology, Universiteitsziekenhuis Leuven, Leuven, Belgium.

Joost P H Drenth (JPH)

European Reference Center RARE-LIVER, Hamburg, Germany.
Department Gastroenterology and Hepatology, Radboud University Medical Center, Nijmegen, the Netherlands.

Ivo M A Geijselaers (IMA)

Department Nephrology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB, Groningen, The Netherlands.

Sterre A M Ter Grote (SAM)

Department Nephrology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB, Groningen, The Netherlands.

Ron T Gansevoort (RT)

Department Nephrology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB, Groningen, The Netherlands.

Frederik Nevens (F)

European Reference Center RARE-LIVER, Hamburg, Germany.
Department Gastroenterology and Hepatology, Universiteitsziekenhuis Leuven, Leuven, Belgium.

Maatje D A van Gastel (MDA)

Department Nephrology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB, Groningen, The Netherlands. m.d.a.van.gastel@umcg.nl.

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