Abstract
BackgroundTo evaluate whether machine learning algorithms allow the prediction of Child-Pugh classification on clinical multiphase computed tomography (CT).MethodsA 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.ResultsEleven 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).ConclusionsThe performance of a CNN in assessing Child-Pugh class based on multiphase abdominal CT images is comparable to that of ERs.
Highlights
To evaluate whether machine learning algorithms allow the prediction of Child-Pugh classification on clinical multiphase computed tomography (CT)
Computer tomography (CT) is routinely used in the diagnosis and clinical management of patients with chronic liver disease [1, 2] and it is recognised as a sensitive diagnostic tool for evaluating morphological changes of liver parenchyma [2,3,4]
Accurate assessment of liver cirrhosis seems to be challenging against the background of the inherent disease heterogeneity
Summary
To evaluate whether machine learning algorithms allow the prediction of Child-Pugh classification on clinical multiphase computed tomography (CT). Computer tomography (CT) is routinely used in the diagnosis and clinical management of patients with chronic liver disease [1, 2] and it is recognised as a sensitive diagnostic tool for evaluating morphological changes of liver parenchyma [2,3,4]. To widen the value of image-based diagnosis, recent studies investigated machine learning algorithms and their potential clinical application, in particular the value of predicting biological or molecular characteristics through image-specific features [8,9,10,11]. In adjunction with clinical assessment, they continue to form the basis for the most widely used clinical scores for liver cirrhosis, that is, the Child-Pugh classification and the model of end-stage liver disease (MELD) [18]
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