Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach

Thuering J, Rippel O, Haarburger C, Merhof D, Schad P, Bruners P, Kuhl CK, Truhn D (2020)

Publication Type: Journal article

Publication year: 2020

Journal

European Radiology Experimental European Society of Radiology

Book Volume: 4

Article Number: 20

Journal Issue: 1

DOI: 10.1186/s41747-020-00148-3

Abstract

Background: To evaluate whether machine learning algorithms allow the prediction of Child-Pugh classification on clinical multiphase computed tomography (CT). Methods: A total of 259 patients who underwent diagnostic abdominal CT (unenhanced, contrast-enhanced arterial, and venous phases) were included in this retrospective study. Child-Pugh scores were determined based on laboratory and clinical parameters. Linear regression (LR), Random Forest (RF), and convolutional neural network (CNN) algorithms were used to predict the Child-Pugh class. Their performances were compared to the prediction of experienced radiologists (ERs). Spearman correlation coefficients and accuracy were assessed for all predictive models. Additionally, a binary classification in low disease severity (Child-Pugh class A) and advanced disease severity (Child-Pugh class ≥ B) was performed. Results: Eleven imaging features exhibited a significant correlation when adjusted for multiple comparisons with Child-Pugh class. Significant correlations between predicted and measured Child-Pugh classes were observed (ρLA = 0.35, ρRF = 0.32, ρCNN = 0.51, ρERs = 0.60; p < 0.001). Significantly better accuracies for the prediction of Child-Pugh classes versus no-information rate were found for CNN and ERs (p ≤ 0.034), not for LR and RF (p ≥ 0.384). For binary severity classification, the area under the curve at receiver operating characteristic analysis was significantly lower (p ≤ 0.042) for LR (0.71) and RF (0.69) than for CNN (0.80) and ERs (0.76), without significant differences between CNN and ERs (p = 0.144). Conclusions: The performance of a CNN in assessing Child-Pugh class based on multiphase abdominal CT images is comparable to that of ERs.

Involved external institutions

Universitätsklinikum Aachen (UKA) DE Germany (DE) Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen DE Germany (DE)

How to cite

APA:

Thuering, J., Rippel, O., Haarburger, C., Merhof, D., Schad, P., Bruners, P.,... Truhn, D. (2020). Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach. European Radiology Experimental, 4(1). https://doi.org/10.1186/s41747-020-00148-3

MLA:

Thuering, Johannes, et al. "Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach." European Radiology Experimental 4.1 (2020).

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