ArticleInsights into imaging2024
Predicting microvascular invasion in small (≤ 5 cm) hepatocellular carcinomas using radiomics-based peritumoral analysis.
Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 19 citations in OpenAlex.
- Integrating B-mode ultrasound radiomics and clinical variables to predict 26-week progression-free survival in advanced hepatocellular carcinoma under targeted immunotherapy.Abdominal radiology (New York) · 2026Article
- Development and validation of a Random Forest prediction model integrating clinical and ultrasound radiomics features for microvascular invasion in hepatocellular carcinoma patients: a retrospective study.Translational cancer research · 2026Article
- MRI-based clinical radiomics nomogram for the prognostic prediction of adult diffuse low-grade gliomas.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Article
- Multiparametric MRI-based habitat analysis integrating deep learning and radiomics for predicting preoperative Ki-67 expression level in breast cancer.BMC medical imaging · 2026Article
- MRI Radiomics-Based Evaluation of Vessels Encapsulating Tumor Clusters and Microvascular Invasion in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
- Dynamic Vascular Spatiotemporal Heterogeneity on Multiphase CT for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
- Gadoxetic Acid-enhanced MRI Radiomics Features of Tumor Margins for Predicting High-Risk Solitary Hepatocellular Carcinoma Aggressiveness and Prognosis.Radiology. Imaging cancer · 2026Article
- MRI Radiomics for Distinguishing HBV-Related Early Hepatocellular Carcinoma from Precancerous Nodules.Journal of hepatocellular carcinoma · 2026Article
- Noninvasive prediction of microvascular invasion in hepatocellular carcinoma using a fusion model of 5-mm optimal peritumoral radiomics and imaging features based on multiphasic gadoxetic acid-enhanced MRI.Frontiers in oncology · 2026Article
- A CT-based radiomics model for preoperative prediction of lymphovascular invasion in colorectal cancer.Frontiers in oncology · 2026Article
- Interpretable machine learning integrating intra-/peritumoral CT radiomics and serum indicators for predicting poorly differentiated esophageal squamous cell carcinoma.Frontiers in oncology · 2026Article
- From texture analysis to artificial intelligence: global research landscape and evolutionary trajectory of radiomics in hepatocellular carcinoma.Discover oncology · 2025Article
- Augmenting conventional criteria: a CT-based deep learning radiomics nomogram for early recurrence risk stratification in hepatocellular carcinoma after liver transplantation.Insights into imaging · 2025Article
- Radiomics analysis based on dynamic contrast-enhanced MRI for predicting early recurrence after hepatectomy in hepatocellular carcinoma patients.Scientific reports · 2025Article
- Article
- ChatGPT as an effective tool for quality evaluation of radiomics research.European radiology · 2025Article
- Intra- and Peritumoral Radiomic Signatures on CECT: Prediction of Aggressive Hepatocellular Carcinoma Subtypes and 2-Year Recurrence.Journal of hepatocellular carcinoma · 2025Article
- CT-based radiomics nomogram to predict proliferative hepatocellular carcinoma and explore the tumor microenvironment.Journal of translational medicine · 2024Article
- Advancing Hepatocellular Carcinoma Management Through Peritumoral Radiomics: Enhancing Diagnosis, Treatment, and Prognosis.Journal of hepatocellular carcinoma · 2024Review
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveWe assessed the predictive capacity of computed tomography (CT)-enhanced radiomics models in determining microvascular invasion (MVI) for isolated hepatocellular carcinoma (HCC) ≤ 5 cm within peritumoral margins of 5 and 10 mm.
methodsRadiomics software was used for feature extraction. We used the least absolute shrinkage and selection operator (LASSO) algorithm to establish an effective model to predict patients' preoperative MVI status.
resultsThe area under the curve (AUC) values in the validation sets for the 5- and 10-mm radiomics models concerning arterial tumors were 0.759 and 0.637, respectively. In the portal vein phase, they were 0.626 and 0.693, respectively. Additionally, the combined radiomics model for arterial tumors and the peritumoral 5-mm margin had an AUC value of 0.820. The decision curve showed that the combined tumor and peritumoral radiomics model exhibited a somewhat superior benefit compared to the traditional model, while the fusion model demonstrated an even greater advantage, indicating its significant potential in clinical application.
conclusionThe 5-mm peritumoral arterial model had superior accuracy and sensitivity in predicting MVI. Moreover, the combined tumor and peritumoral radiomics model outperformed both the individual tumor and peritumoral radiomics models. The most effective combination was the arterial phase tumor and peritumor 5-mm margin combination. Using a fusion model that integrates tumor and peritumoral radiomics and clinical data can aid in the preoperative diagnosis of the MVI of isolated HCC ≤ 5 cm, indicating considerable practical value. CRITICAL RELEVANCE STATEMENT: The radiomics model including a 5-mm peritumoral expansion is a promising noninvasive biomarker for preoperatively predicting microvascular invasion in patients diagnosed with a solitary HCC ≤ 5 cm. KEY POINTS: • Radiomics features extracted at a 5-mm distance from the tumor could better predict hepatocellular carcinoma microvascular invasion. • Peritumoral radiomics can be used to capture tumor heterogeneity and predict microvascular invasion. • This radiomics model stands as a promising noninvasive biomarker for preoperatively predicting MVI in individuals.
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