A patient-informed approach to predict iodinated-contrast media enhancement in the liver. Issue 156 (November 2022)

• Record Type: Journal Article
• Authors: Setiawan, Hananiel; Chen, Chaofan; Abadi, Ehsan; Fu, Wanyi; Marin, Daniele; Ria, Francesco; Samei, Ehsan
• Is Part Of: European journal of radiology. Issue 156(2022)
• Publication Date: 2022-11
• Subjects: contrast-enhanced CT -- contrast CT -- Iodinated contrast enhancement -- Contrast perfusion -- Liver enhancement; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2022.110555
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Inconsistencies in contrast administration and CT imaging protocol led to inconsistencies in contrast enhancement in patients. A pharmacokinetics- and patient attributes-informed machine learning model was built to predict liver contrast enhancement. The proposed contrast enhancement model can be used to improve consistency of enhancement and image quality in liver imaging. Abstract: Objective: To devise a patient-informed time series model that predicts liver contrast enhancement, by integrating clinical data and pharmacokinetics models, and to assess its feasibility to improve enhancement consistency in contrast-enhanced liver CT scans. Methods: The study included 1577 Chest/Abdomen/Pelvis CT scans, with 70–30% training/validation-testing split. A Gaussian function was used to approximate the early arterial, late arterial, and the portal venous phases of the contrast perfusion curve of each patient using their respective bolus tracking and diagnostic scan data. Machine learning models were built to predict the Gaussian parameters of each patient using the patient attributes (weight, height, age, sex, BMI). Pearson's coefficient, mean absolute error, and root mean squared error were used to assess the prediction accuracy. Results: The integration of the pharmacokinetics model with a two-layered neural network achieved the highest prediction accuracy on the test data (R 2 = 0.61), significantly exceeding the performance of the pharmacokinetics model alone (R 2 = 0.11). Applying the model demonstrated that adjusting the contrast administration directed by the model may reduce clinical enhancement inconsistency by up to 40 %. Conclusions: A new model using a Gaussian function and supervised machine learning can be used to build liver parenchyma contrast enhancement prediction model. The model can have utility in clinical settings to optimize and improve consistency in contrast-enhanced liver imaging.