Prediction of insufficient hepatic enhancement during the Hepatobiliary phase of Gd-EOB DTPA-enhanced MRI using machine learning classifier and feature selection algorithms
- 주제(키워드) Hepatobiliary images , Insufficient hepatic enhancement , Machine learning , Gadolinium ethoxybenzyl DTPA
- 주제(기타) Radiology, Nuclear Medicine & Medical Imaging
- 설명문(일반) [Ko, Ji Su; Byun, Jieun; Woo, Ji Young] Hallym Univ, Kangnam Sacred Heart Hosp, Dept Radiol, Coll Med, Seoul, South Korea; [Byun, Jieun] Ewha Womans Univ, Coll Med, Dept Radiol, Seoul, South Korea; [Park, Seongkeun] Soonchunhyang Univ, Dept Smart Automobile, Machine Intelligence Lab, Asan, Chungcheongnamd, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 SPRINGER
- 발행년도 2022
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000191017
- 본문언어 영어
- Published As https://doi.org/10.1007/s00261-021-03308-0
- PubMed https://pubmed.ncbi.nlm.nih.gov/34647145
초록/요약
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. Graphic abstract
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