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- W4255455614 abstract "Abstract 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." @default.
- W4255455614 created "2022-05-12" @default.
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- W4255455614 date "2021-03-11" @default.
- W4255455614 modified "2023-09-28" @default.
- W4255455614 title "Machine learning approach for prediction of hepatic enhancement during the hepatobiliary phase of Gd-EOB DTPA-enhanced MRI." @default.
- W4255455614 doi "https://doi.org/10.21203/rs.3.rs-274189/v1" @default.
- W4255455614 hasPublicationYear "2021" @default.
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