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

European Radiology Experimental, vol. 4, pp. 20

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: = 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.

Authors 8

1. Johannes Thüring corresponding Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, RWTH Aachen University Hospital, Pauwelsstraße 30, 52072, Aachen, Germany. thejo.thuering@gmx.de

   Department of Diagnostic and Interventional Radiology, RWTH Aachen University Hospital, Pauwelsstraße 30, 52072, Aachen, Germany

2. Oliver Rippel Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

3. Christoph Haarburger Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

4. Dorit Merhof Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

5. Philipp Schad Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, RWTH Aachen University Hospital, Pauwelsstraße 30, 52072, Aachen, Germany

6. Philipp Bruners Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, RWTH Aachen University Hospital, Pauwelsstraße 30, 52072, Aachen, Germany

7. Christiane Katharina Kuhl Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, RWTH Aachen University Hospital, Pauwelsstraße 30, 52072, Aachen, Germany

8. Daniel Truhn Aachen Department of Diagnostic and Interventional Radiology Institute of Imaging & Computer Vision

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, RWTH Aachen University Hospital, Pauwelsstraße 30, 52072, Aachen, Germany

   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany