ArticleDiagnostics (Basel, Switzerland)2023
The Additive Value of Radiomics Features Extracted from Baseline MR Images to the Barcelona Clinic Liver Cancer (BCLC) Staging System in Predicting Transplant-Free Survival in Patients with Hepatocellular Carcinoma: A Single-Center Retrospective Analysis.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects.Abdominal radiology (New York) · 2026Review
- Multiparametric MRI-Based Machine Learning Radiomics Prognostic Models for Multifocal Hepatocellular Carcinoma Beyond Milan Criteria: A Retrospective Study.Journal of hepatocellular carcinoma · 2025Article
- Predicting Overall Survival with Deep Learning from 18F-FDG PET-CT Images in Patients with Hepatocellular Carcinoma before Liver Transplantation.Diagnostics (Basel, Switzerland) · 2023Article
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9 authors.
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Abstract
backgroundTo study the additive value of radiomics features to the BCLC staging system in clustering HCC patients.
methodsA total of 266 patients with HCC were included in this retrospective study. All patients had undergone baseline MR imaging, and 95 radiomics features were extracted from 3D segmentations representative of lesions on the venous phase and apparent diffusion coefficient maps. A random forest algorithm was utilized to extract the most relevant features to transplant-free survival. The selected features were used alongside BCLC staging to construct Kaplan-Meier curves.
resultsOut of 95 extracted features, the three most relevant features were incorporated into random forest classifiers. The Integrated Brier score of the prediction error curve was 0.135, 0.072, and 0.048 for the BCLC, radiomics, and combined models, respectively. The mean area under the receiver operating curve (ROC curve) over time for the three models was 81.1%, 77.3%, and 56.2% for the combined radiomics and BCLC models, respectively.
conclusionsRadiomics features outperformed the BCLC staging system in determining prognosis in HCC patients. The addition of a radiomics classifier increased the classification capability of the BCLC model. Texture analysis features could be considered as possible biomarkers in predicting transplant-free survival in HCC patients.
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