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

OBJECTIVES: 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 requi...

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Autores principales: Aapkes, Sophie E., Barten, Thijs R. M., Coudyzer, Walter, Drenth, Joost P. H., Geijselaers, Ivo M. A., ter Grote, Sterre A. M., Gansevoort, Ron T., Nevens, Frederik, van Gastel, Maatje D. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10121488/
https://www.ncbi.nlm.nih.gov/pubmed/36640173
http://dx.doi.org/10.1007/s00330-022-09346-6
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author Aapkes, Sophie E.
Barten, Thijs R. M.
Coudyzer, Walter
Drenth, Joost P. H.
Geijselaers, Ivo M. A.
ter Grote, Sterre A. M.
Gansevoort, Ron T.
Nevens, Frederik
van Gastel, Maatje D. A.
author_facet Aapkes, Sophie E.
Barten, Thijs R. M.
Coudyzer, Walter
Drenth, Joost P. H.
Geijselaers, Ivo M. A.
ter Grote, Sterre A. M.
Gansevoort, Ron T.
Nevens, Frederik
van Gastel, Maatje D. A.
author_sort Aapkes, Sophie E.
collection PubMed
description OBJECTIVES: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: Semi-automatic liver segmentation is a fast and accurate method to determine TLV and liver growth in PLD patients. KEY POINTS: • 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-022-09346-6.
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spelling pubmed-101214882023-04-23 Validation of a semi-automatic method to measure total liver volumes in polycystic liver disease on computed tomography — high speed and accuracy Aapkes, Sophie E. Barten, Thijs R. M. Coudyzer, Walter Drenth, Joost P. H. Geijselaers, Ivo M. A. ter Grote, Sterre A. M. Gansevoort, Ron T. Nevens, Frederik van Gastel, Maatje D. A. Eur Radiol Computed Tomography OBJECTIVES: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: Semi-automatic liver segmentation is a fast and accurate method to determine TLV and liver growth in PLD patients. KEY POINTS: • 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-022-09346-6. Springer Berlin Heidelberg 2023-01-14 2023 /pmc/articles/PMC10121488/ /pubmed/36640173 http://dx.doi.org/10.1007/s00330-022-09346-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Computed Tomography
Aapkes, Sophie E.
Barten, Thijs R. M.
Coudyzer, Walter
Drenth, Joost P. H.
Geijselaers, Ivo M. A.
ter Grote, Sterre A. M.
Gansevoort, Ron T.
Nevens, Frederik
van Gastel, Maatje D. A.
Validation of a semi-automatic method to measure total liver volumes in polycystic liver disease on computed tomography — high speed and accuracy
title Validation of a semi-automatic method to measure total liver volumes in polycystic liver disease on computed tomography — high speed and accuracy
title_full Validation of a semi-automatic method to measure total liver volumes in polycystic liver disease on computed tomography — high speed and accuracy
title_fullStr Validation of a semi-automatic method to measure total liver volumes in polycystic liver disease on computed tomography — high speed and accuracy
title_full_unstemmed Validation of a semi-automatic method to measure total liver volumes in polycystic liver disease on computed tomography — high speed and accuracy
title_short Validation of a semi-automatic method to measure total liver volumes in polycystic liver disease on computed tomography — high speed and accuracy
title_sort validation of a semi-automatic method to measure total liver volumes in polycystic liver disease on computed tomography — high speed and accuracy
topic Computed Tomography
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10121488/
https://www.ncbi.nlm.nih.gov/pubmed/36640173
http://dx.doi.org/10.1007/s00330-022-09346-6
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