ArticleEuropean radiology experimental2020
Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach.
Article in European radiology experimental, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
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- The Relationship between the Number of Stem Cells and the Concentration of Stromal Cell-Derived Factor-1 with Disease Severity in Patients with Liver Cirrhosis.International journal of hematology-oncology and stem cell research · 2025Article
- Data Augmentation and Machine Learning algorithms for multi-class imbalanced morphometrics data of stingless bees.Heliyon · 2025Article
- A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation.Scientific reports · 2024Article
- Applying a deep convolutional neural network to monitor the lateral spread response during microvascular surgery for hemifacial spasm.PloS one · 2022Article
- Advances in Predictive Modeling Using Machine Learning in the Field of Hepatology.Clinical liver disease · 2021Review
- A Radiomics Approach to Predict the Emergence of New Hepatocellular Carcinoma in Computed Tomography for High-Risk Patients with Liver Cirrhosis.Diagnostics (Basel, Switzerland) · 2021Article
- CT-Based Prediction of Liver Function and Post-PVE Hypertrophy Using an Artificial Neural Network.Journal of clinical medicine · 2021Article
- A Computed Tomography Nomogram for Assessing the Malignancy Risk of Focal Liver Lesions in Patients With Cirrhosis: A Preliminary Study.Frontiers in oncology · 2021Article
- Article
- Association of serum osteoprotegerin with severity of chronic liver disease in female patients: A potential biomarker.Pakistan journal of medical sciencesArticle
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Authors and funding
8 authors.
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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 (ρ
conclusionsThe performance of a CNN in assessing Child-Pugh class based on multiphase abdominal CT images is comparable to that of ERs.
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