dc.contributor.author |
Setiawan, Hananiel |
|
dc.contributor.author |
Ria, Francesco |
|
dc.contributor.author |
Abadi, Ehsan |
|
dc.contributor.author |
Fu, Wanyi |
|
dc.contributor.author |
Smith, Taylor B |
|
dc.contributor.author |
Samei, Ehsan |
|
dc.date.accessioned |
2020-11-20T16:58:24Z |
|
dc.date.available |
2020-11-20T16:58:24Z |
|
dc.date.issued |
2020-11 |
|
dc.identifier |
00004728-202011000-00012 |
|
dc.identifier.issn |
0363-8715 |
|
dc.identifier.issn |
1532-3145 |
|
dc.identifier.uri |
https://hdl.handle.net/10161/21707 |
|
dc.description.abstract |
OBJECTIVE: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.
|
|
dc.language |
eng |
|
dc.publisher |
Ovid Technologies (Wolters Kluwer Health) |
|
dc.relation.ispartof |
J Comput Assist Tomogr |
|
dc.relation.isversionof |
10.1097/RCT.0000000000001095 |
|
dc.title |
Modeling Patient-Informed Liver Contrast Perfusion in Contrast-enhanced Computed Tomography. |
|
dc.type |
Journal article |
|
duke.contributor.id |
Setiawan, Hananiel|0753422 |
|
duke.contributor.id |
Ria, Francesco|0691782 |
|
duke.contributor.id |
Abadi, Ehsan|0682983 |
|
duke.contributor.id |
Samei, Ehsan|0261465 |
|
dc.date.updated |
2020-11-20T16:58:23Z |
|
pubs.begin-page |
882 |
|
pubs.end-page |
886 |
|
pubs.issue |
6 |
|
pubs.organisational-group |
Staff |
|
pubs.organisational-group |
Duke |
|
pubs.publication-status |
Published |
|
pubs.volume |
44 |
|
duke.contributor.orcid |
Ria, Francesco|0000-0001-5902-7396 |
|
duke.contributor.orcid |
Abadi, Ehsan|0000-0002-9123-5854 |
|
duke.contributor.orcid |
Samei, Ehsan|0000-0001-7451-3309 |
|