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Research Article
Jieun Byun
Department of Radiology, Hallym University College of Medicine, Kangnam Sacred Heart Hospital
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
We retrospectively assessed 214 patients with chronic liver disease or liver cirrhosis who underwent magnetic resonance imaging (MRI) enhanced with gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) from August 2016 to May 2020 to evaluate the relationship between biochemical results that reflect liver function and hepatic enhancement. With the information gained we employed a machine learning approach with the K-Nearest Neighbor (KNN) algorithm to develop a predictive model for determining insufficient hepatic enhancement during the hepatobiliary phase (HBP) in Gd-EOB-DTPA-enhanced MRI. Using both quantitative and qualitative assessments, the total bilirubin (TB), albumin (Alb), prothrombin time-international normalized ratio, platelet, Child-Pugh score (CPS), and Model for End-stage Liver Disease Sodium (MELD-Na) score were related to decreased hepatic enhancement. In a multivariate analysis, TB and Alb were associated with insufficient enhancement (p < 0.001). The predictive model showed that a combination of a variety of biochemical parameters had better performance (accuracy = 82.8%, area under the curve (AUC) = 0.861) in predicting insufficient enhancement than either the CPS (accuracy = 79.5%, AUC = 0.845) or the MELD-Na score (accuracy = 80.8%, AUC = 0.821). By using a machine-learning-based predictive model with the KNN algorithm, radiologists can predict insufficient hepatic enhancement during HBP in advance and adjust each patient's individually optimized MRI protocol.
Keywords
chronic liver disease, cirrhosis, magnetic resonance imaging (MRI), Gd-EOB-DTPA, Child-Pugh score (CPS)
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No competing interests reported.
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This is a preprint. It has not completed peer review.
Research Article
Jieun Byun
Department of Radiology, Hallym University College of Medicine, Kangnam Sacred Heart Hospital
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 11 Mar, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
We retrospectively assessed 214 patients with chronic liver disease or liver cirrhosis who underwent magnetic resonance imaging (MRI) enhanced with gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) from August 2016 to May 2020 to evaluate the relationship between biochemical results that reflect liver function and hepatic enhancement. With the information gained we employed a machine learning approach with the K-Nearest Neighbor (KNN) algorithm to develop a predictive model for determining insufficient hepatic enhancement during the hepatobiliary phase (HBP) in Gd-EOB-DTPA-enhanced MRI. Using both quantitative and qualitative assessments, the total bilirubin (TB), albumin (Alb), prothrombin time-international normalized ratio, platelet, Child-Pugh score (CPS), and Model for End-stage Liver Disease Sodium (MELD-Na) score were related to decreased hepatic enhancement. In a multivariate analysis, TB and Alb were associated with insufficient enhancement (p < 0.001). The predictive model showed that a combination of a variety of biochemical parameters had better performance (accuracy = 82.8%, area under the curve (AUC) = 0.861) in predicting insufficient enhancement than either the CPS (accuracy = 79.5%, AUC = 0.845) or the MELD-Na score (accuracy = 80.8%, AUC = 0.821). By using a machine-learning-based predictive model with the KNN algorithm, radiologists can predict insufficient hepatic enhancement during HBP in advance and adjust each patient's individually optimized MRI protocol.
Figure 1
Figure 2
Figure 3
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