Modeling Patient-Informed Liver Contrast Perfusion in Contrast-enhanced Computed Tomography.
Journal Article (Journal Article)
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.
Full Text
Duke Authors
Cited Authors
- Setiawan, H; Ria, F; Abadi, E; Fu, W; Smith, TB; Samei, E
Published In
Volume / Issue
- 44 / 6
Start / End Page
- 882 - 886
PubMed ID
- 33196597
Electronic International Standard Serial Number (EISSN)
- 1532-3145
Digital Object Identifier (DOI)
- 10.1097/RCT.0000000000001095
Language
- eng
Conference Location
- United States