ArticleBiomedicines2022
Radiomics-Based Predictive Model of Radiation-Induced Liver Disease in Hepatocellular Carcinoma Patients Receiving Stereo-Tactic Body Radiotherapy.
Article in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Research progress of MRI-based radiomics in hepatocellular carcinoma.Frontiers in oncology · 2025Pooled it
- Effect and safety of tislelizumab combined with anlotinib and radiotherapy in advanced hepatocellular carcinoma: A single-arm, single-center, phase II clinical study.Scientific reports · 2026Trial
- Effect of cytokines on advanced hepatocellular carcinoma prognosis receiving radiotherapy and tislelizumab plus anlotinib: a single-center phase II clinical trial.Scientific reports · 2024Trial
- Survival of patients with advanced hepatocellular carcinoma based on Gustave Roussy immune score and portal vein tumor thrombosis: a single-center, retrospective, non-randomized case-controlled study.Discover oncology · 2025Article
- From texture analysis to artificial intelligence: global research landscape and evolutionary trajectory of radiomics in hepatocellular carcinoma.Discover oncology · 2025Article
- Tislelizumab plus anlotinib with or without radiotherapy as first-line therapy in advanced hepatocellular carcinoma: a single center, non-randomized retrospective case-control study.Discover oncology · 2025Article
- An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival of hepatocellular carcinoma patients after stereotactic body radiation therapy.Journal of cancer research and clinical oncology · 2025Article
- Radiomics of Dynamic Contrast-Enhanced MRI for Predicting Radiation-Induced Hepatic Toxicity After Intensity Modulated Radiotherapy for Hepatocellular Carcinoma: A Machine Learning Predictive Model Based on the SHAP Methodology.Journal of hepatocellular carcinoma · 2025Article
- Targeting Oxidative Stress: The Potential of Vitamin C in Protecting against Liver Damage after Electron Beam Therapy.Biomedicines · 2024Article
- Advances in gross tumor target volume determination in radiotherapy for patients with hepatocellular carcinoma.Frontiers in oncology · 2024Review
- Potential benefits of using radioactive ion beams for range margin reduction in carbon ion therapy.Scientific reports · 2022Article
- Recent Advances and Challenges in Stereotactic Body Radiotherapy.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
(1) Background: The application of stereotactic body radiation therapy (SBRT) in hepatocellular carcinoma (HCC) limited the risk of the radiation-induced liver disease (RILD) and we aimed to predict the occurrence of RILD more accurately. (2) Methods: 86 HCC patients were enrolled. We identified key predictive factors from clinical, radiomic, and dose-volumetric parameters using a multivariate analysis, sequential forward selection (SFS), and a K-nearest neighbor (KNN) algorithm. We developed a predictive model for RILD based on these factors, using the random forest or logistic regression algorithms. (3) Results: Five key predictive factors in the training set were identified, including the albumin-bilirubin grade, difference average, strength, V5, and V30. After model training, the F1 score, sensitivity, specificity, and accuracy of the final random forest model were 0.857, 100, 93.3, and 94.4% in the test set, respectively. Meanwhile, the logistic regression model yielded an F1 score, sensitivity, specificity, and accuracy of 0.8, 66.7, 100, and 94.4% in the test set, respectively. (4) Conclusions: Based on clinical, radiomic, and dose-volumetric factors, our models achieved satisfactory performance on the prediction of the occurrence of SBRT-related RILD in HCC patients. Before undergoing SBRT, the proposed models may detect patients at high risk of RILD, allowing to assist in treatment strategies accordingly.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.