Feasibility of three-dimensional virtual surgical planning in living liver donors View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-03

AUTHORS

Jeong Hee Yoon, Jeong Min Lee, Ju Hyun Jun, Kyung-Suk Suh, Philippe Coulon, Joon Koo Han, Byung Ihn Choi

ABSTRACT

PURPOSE: The aim of the study was to determine the accuracy, reproducibility, and improvement in the clinical workflow of a semiautomatic computed tomography (CT) virtual surgical planning program in estimating graft volume using actual graft weight as a standard of reference in living liver donors. MATERIALS AND METHODS: This retrospective study was approved by our Institutional Review Board and the requirement for informed consent was waived. A total of 105 liver donor candidates who underwent preoperative liver CT were reviewed. Volumes of the whole liver (V T), four hepatic segments, and predicted volumes of the hepatic graft (V pred) were obtained using a semiautomatic analysis program by radiologists and a conventional manual volumetry program by surgeons. Intraobserver and interobserver agreements of V T and V pred were assessed using intraclass correlation coefficients (ICCs). V pred was also compared to the actual graft weight (W act) and analysis times were recorded. In addition, potential vascular complications were assessed using the surgical planning function of the software. RESULTS: The mean processing time of hepatic volumetry, segmentation, and surgical planning using software was significantly shorter than that using manual volumetry (175.9 ± 46.6 vs. 916.6 ± 52.8 s, P < 0.001). V T and V pred obtained using the semiautomatic analysis program showed significant intra and interobserver agreements (ICC = 0.98-0.99), and V pred showed strong correlation with W act (r = 0.83-0.86, P < 0.0001). Furthermore, image review using the liver analysis program detected 80% (4/5) vascular complications. CONCLUSION: Liver volumetry and estimation of graft volume using liver analysis software provided good accuracy and excellent reproducibility in a short time than manual volumetry method and was useful for identifying vascular complications. More... »

PAGES

510-520

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00261-014-0231-9

DOI

http://dx.doi.org/10.1007/s00261-014-0231-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1021989215

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25173793


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00261-014-0231-9'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00261-014-0231-9'

Turtle is a human-readable linked data format.

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RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00261-014-0231-9'


 

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