Modeling Patient-Informed Liver Contrast Perfusion in Contrast-enhanced Computed Tomography.
Journal
Journal of computer assisted tomography
ISSN: 1532-3145
Titre abrégé: J Comput Assist Tomogr
Pays: United States
ID NLM: 7703942
Informations de publication
Date de publication:
Historique:
entrez:
16
11
2020
pubmed:
17
11
2020
medline:
15
12
2020
Statut:
ppublish
Résumé
To determine the correlation between patient attributes and contrast enhancement in liver parenchyma and demonstrate the potential for patient-informed prediction and optimization of contrast enhancement in liver imaging. The study included 418 chest/abdomen/pelvis computed tomography scans, with 75% to 25% training-testing split. Two regression models were built to predict liver parenchyma contrast enhancement over time: first model (model A) utilized patient attributes (height, weight, sex, age, bolus volume, injection rate, scan times, body mass index, lean body mass) and bolus-tracking data. A second model (model B) only used the patient attributes. Pearson coefficient was used to assess predictive accuracy. Weight- and height-related features were found to be statistically significant predictors (P < 0.05), weight being the strongest. Of the 2 models, model A (r = 0.75) showed greater accuracy than model B (r = 0.42). Patient attributes can be used to build prediction model for liver parenchyma contrast enhancement. The model can have utility in optimization and improved consistency in contrast-enhanced liver imaging.
Identifiants
pubmed: 33196597
doi: 10.1097/RCT.0000000000001095
pii: 00004728-202011000-00012
doi:
Substances chimiques
Contrast Media
0
Iohexol
4419T9MX03
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
882-886Références
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