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Patient-informed modelling of hepatic contrast dynamics in contrast-enhanced CT imaging

机译：造影剂增强CT成像中患者的肝脏造影剂动力学信息建模

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Iodinated contrast agent is frequently used in computed tomography (CT) imaging to enhance organ contrast enhancement and improve diagnostic sensitivity. Despite this importance, there currently is a lack of standardization in contrast administration protocol across institutions, leading to many safety and clinical diagnostic risks. To solve this, we built three liver contrast enhancement/perfusion models: two using simple linear regression and another by combining a pre-existing pharmacokinetics mathematical model with clinical data with the eventual goal of individualizing contrast administration protocol to optimize contrast-enhanced CT imaging for each patient. These models primarily use patient attributes, such as height, weight, sex, age and contrast administration information, and bolus tracking information to make such predictions. 418 Chest/Abdomen/Pelvis CT scans were used in this study. 75% of cases were used to train these models and the rest were used to test the prediction accuracy. Pearson's correlation coefficient test was used to find the correlations between the patient attributes and contrast enhancement in liver parenchyma. Weight, height, BMI, and lean body mass were found to be statistically significant predictors for contrast enhancement (P<0.05), with weight as the strongest predictor. Of the predictive models, we found that including bolus tracking information increases predictive accuracy (r~2=0.75 v. 0.42) and that in the absence of bolus tracking information, combining clinical data with pre-existing pharmacokinetics model may provide the needed enhancement curve.
机译：碘化造影剂经常用于计算机断层扫描（CT）成像中，以增强器官造影剂的强度并提高诊断灵敏度。尽管具有这种重要性，但目前跨机构的造影剂管理协议尚缺乏标准化，导致许多安全性和临床诊断风险。为了解决这个问题，我们建立了三种肝脏造影剂增强/灌注模型：两种使用简单的线性回归模型，另一种通过将已有的药代动力学数学模型与临床数据相结合，最终目标是个性化造影剂给药方案，以优化造影剂增强的CT成像，每个病人。这些模型主要使用患者的属性（例如身高，体重，性别，年龄和对比管理信息）以及推注跟踪信息来做出此类预测。在这项研究中使用了418箱胸部/腹部/盆腔CT扫描。 75％的案例用于训练这些模型，其余的则用于检验预测的准确性。皮尔逊相关系数检验用于发现患者属性与肝实质对比增强之间的相关性。发现体重，身高，BMI和瘦体重是对比度增强的统计学显着预测因子（P <0.05），而体重是最强的预测因子。在预测模型中，我们发现包括推注跟踪信息可提高预测准确性（r〜2 = 0.75 v。0.42），并且在不存在推注跟踪信息的情况下，将临床数据与预先存在的药代动力学模型结合可以提供所需的增强曲线。

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