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Prediction of insufficient hepatic enhancement during the Hepatobiliary phase of Gd-EOB DTPA-enhanced MRI using machine learning classifier and feature selection algorithms

  • Hepatobiliary
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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

The purpose of this study was to reveal the usefulness of machine learning classifier and feature selection algorithms for prediction of insufficient hepatic enhancement in the HBP.

Methods

We retrospectively assessed 214 patients with chronic liver disease or liver cirrhosis who underwent MRI enhanced with Gd-EOB-DTPA. Various liver function tests, Child–Pugh score (CPS) and Model for End-stage Liver Disease Sodium (MELD-Na) score were collected as candidate predictors for insufficient hepatic enhancement. Insufficient hepatic enhancement was assessed using liver-to-portal vein signal intensity ratio and 5-level visual grading. The clinico-laboratory findings were compared using Student’s t-test and Mann–Whitney U test. Relationships between the laboratory tests and insufficient hepatic enhancement were assessed using Pearson’s and Spearman’s rank correlation coefficient. Feature importance was assessed by Random UnderSampling boosting algorithms. The predictive models were constructed using decision tree(DT), k-nearest neighbor(KNN), random forest(RF), and support-vector machine(SVM) classifier algorithms. The performances of the prediction models were analyzed by calculating the area under the receiver operating characteristic curve(AUC).

Results

Among four machine learning classifier algorithms using various feature combinations, SVM using total bilirubin(TB) and albumin(Alb) showed excellent predictive ability for insufficient hepatic enhancement(AUC = 0.93, [95% CI: 0.93–0.94]) and higher AUC value than conventional logistic regression(LR) model (AUC = 0.92, [95% CI; 0.92–0.93], predictive models using the MELD-Na (AUC = 0.90 [95% CI: 0.89–0.91]) and CPS (AUC = 0.89 [95% CI: 0.88–0.90]).

Conclusion

Machine learning-based classifier (i.e. SVM) and feature selection algorithms can be used to predict insufficient hepatic enhancement in the HBP before performing MRI.

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Data availability

All authors checked that all data and materials support published claims and comply with field standards.

Code availability

Not applicable.

Abbreviations

Gd-EOB-DTPA:

Gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid

HBP:

Hepatobiliary phase

OATPs:

Organic anion-transporting polypeptides

MRP:

Multidrug resistance-associated protein

MRI:

Magnetic resonance imaging

CPS:

Child–Pugh score

MELD:

Model for End-stage Liver Disease

LPR:

Liver-to-portal vein signal intensity ratio

LPVC:

Liver-to-portal vein contrast

RUSboosting:

Random UnderSampling boosting

DT:

Decision trees

KNN:

K-nearest neighbor

RF:

Random forest

SVM:

Support-vector machine

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Acknowledgements

This work was supported by Soonchunhyang university.

Funding

No funds, grants, or other support was received.

Author information

Author notes

  1. Jieun Byun and Seongkeun Park have equally contributed to this study.

Authors and Affiliations

  1. Department of Radiology, Hallym University College of Medicine, Kangnam Sacred Heart Hospital, Seoul, Republic of Korea

    Ji Su Ko, Jieun Byun & Ji Young Woo

  2. Department of Radiology, College of Medicine, Ewha Womans University, Seoul, Republic of Korea

    Jieun Byun

  3. Machine Intelligence Laboratory, Department of Smart Automobile, Soonchunhyang University, Asan, Chungcheongnamdo, Republic of Korea

    Seongkeun Park

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Contributions

All authors contributed to the study conception and design. Material preparation was performed by JB and SP, data collection were performed by JSK, JB, and JYW, and data analysis were performed by JB and SP. The first draft of the manuscript was written by JSK and JB, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jieun Byun.

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The authors declare that they have no conflicts to disclose.

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This retrospective study was conducted with approval of the Institutional Review Board of Kangnam Sacred Heart Hospital, and requirements for written informed consent were waived due to the retrospective nature of the study.

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This manuscript has been approved by all co-authors.

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Ko, J.S., Byun, J., Park, S. et al. Prediction of insufficient hepatic enhancement during the Hepatobiliary phase of Gd-EOB DTPA-enhanced MRI using machine learning classifier and feature selection algorithms. Abdom Radiol 47, 161–173 (2022). https://doi.org/10.1007/s00261-021-03308-0

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