Modeling Patient-Informed Liver Contrast Perfusion in Contrast-enhanced Computed Tomography.

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.