ArticleContrast media & molecular imaging2022
Radiomics Analysis on Noncontrast CT for Distinguishing Hepatic Hemangioma (HH) and Hepatocellular Carcinoma (HCC).
Article in Contrast media & molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Radiomics-based automated machine learning for differentiating focal liver lesions on unenhanced computed tomography.Abdominal radiology (New York) · 2025Article
- Prediction models for differentiating benign from malignant liver lesions based on multiparametric dual-energy non-contrast CT.European radiology · 2025Article
- Radiomics and Clinical Features for Distinguishing Kidney Stone-Associated Urinary Tract Infection: A Comprehensive Analysis of Machine Learning Classification.Open forum infectious diseases · 2024Article
- Research on multi-model imaging machine learning for distinguishing early hepatocellular carcinoma.BMC cancer · 2024Article
- Hepatocellular carcinoma‑cavernous hemangioma collision tumor: A case report.Oncology letters · 2024Article
- Deep Learning-Based Automatic Segmentation Combined with Radiomics to Predict Post-TACE Liver Failure in HCC Patients.Journal of hepatocellular carcinoma · 2024Article
- Development and validation of a CT-based nomogram for accurate hepatocellular carcinoma detection in high risk patients.Frontiers in oncology · 2024Article
- Focal Lesions of the Liver and Radiomics: What Do We Know?Diagnostics (Basel, Switzerland) · 2023Review
- Editorial: The use of data mining in radiological-pathological images for personal medicine.Frontiers in genetics · 2023Article
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Authors and funding
10 authors.
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Abstract
Background: To form a radiomic model on the basis of noncontrast computed tomography (CT) to distinguish hepatic hemangioma (HH) and hepatocellular carcinoma (HCC). Methods: In this retrospective study, a total of 110 patients were reviewed, including 72 HCC and 38 HH. We accomplished feature selection with the least absolute shrinkage and operator (LASSO) and built a radiomics signature. Another improved model (radiomics index) was established using forward conditional multivariate logistic regression. Both models were tested in an internal validation group (38 HCC and 21 HH). Results: The radiomic signature we built including 5 radiomic features demonstrated significant differences between the hepatic HH and HCC groups ( Conclusions: Our developed radiomics-based model can successfully distinguish HH and HCC patients, which can help clinical decision-making with lower cost.
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