Setiawan Hananiel, Ria Francesco, Abadi Ehsan, Fu Wanyi, Smith Taylor B, Samei Ehsan
From the Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology.
J Comput Assist Tomogr. 2020 Nov/Dec;44(6):882-886. doi: 10.1097/RCT.0000000000001095.
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.
确定患者属性与肝实质对比增强之间的相关性，并证明在肝脏成像中基于患者信息进行对比增强预测和优化的潜力。
该研究纳入了418例胸部/腹部/骨盆计算机断层扫描，采用75%至25%的训练-测试分割。建立了两个回归模型来预测肝实质随时间的对比增强：第一个模型（模型A）利用患者属性（身高、体重、性别、年龄、团注体积、注射速率、扫描时间、体重指数、瘦体重）和团注追踪数据。第二个模型（模型B）仅使用患者属性。使用皮尔逊系数评估预测准确性。
发现与体重和身高相关的特征是具有统计学意义的预测因子（P < 0.05），体重是最强的预测因子。在这两个模型中，模型A（r = 0.75）的准确性高于模型B（r = 0.42）。
患者属性可用于建立肝实质对比增强的预测模型。该模型可用于优化肝脏对比增强成像，并提高其一致性。
