ArticleFrontiers in medicine2024
Enhancing prognostic prediction in hepatocellular carcinoma post-TACE: a machine learning approach integrating radiomics and clinical features.
Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Limited incremental value and cross-cohort transferability of CT radiomics for survival prediction after TACE in hepatocellular carcinoma.Abdominal radiology (New York) · 2026Article
- Vessel-density distribution mapping on portal venous CT and deep learning for 2-year overall survival risk stratification after transarterial chemoembolization in hepatocellular carcinoma.Abdominal radiology (New York) · 2026Article
- Arterial and biliary complications after transarterial chemoembolization for hepatocellular carcinoma.World journal of clinical oncology · 2026Review
- Quantitative CT Perfusion and Radiomics Reveal Complementary Markers of Treatment Response in HCC Patients Undergoing TACE.Diagnostics (Basel, Switzerland) · 2025Article
- Integrating radiomics and machine learning for the diagnosis and prognosis of hepatocellular carcinoma.World journal of gastrointestinal oncology · 2025Article
- A Machine Learning Model for Predicting Prognosis in HCC Patients With Diabetes After TACE.Journal of hepatocellular carcinoma · 2025Article
- Machine Learning-Based Survival Analysis for Patients Receiving Lenvatinib for Unresectable Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2025Article
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Authors and funding
7 authors.
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Abstract
Objective: This study aimed to investigate the use of radiomics features and clinical information by four machine learning algorithms for predicting the prognosis of patients with hepatocellular carcinoma (HCC) who have been treated with transarterial chemoembolization (TACE). Methods: A total of 105 patients with HCC treated with TACE from 2002 to 2012 were enrolled retrospectively and randomly divided into two cohorts for training ( Results: A total of 1,834 radiomics features were extracted from the computed tomography images of each patient. The clinical risk factors for HCC prognosis were age at diagnosis, TNM stage, and metastasis, which were analyzed using univariate Cox regression. In various models, the efficacy of the combined models generally surpassed that of the radiomics and clinical models. Among four machine learning algorithms, XGBoost exhibited the best performance in combined models, achieving an AUC of 0.979 in the training set and 0.750 in the testing set, demonstrating its strong prognostic prediction capability. Conclusion: The superior performance of the XGBoost-based combined model underscores its potential as a powerful tool for enhancing the precision of prognostic assessments for patients with HCC.
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