ArticleWorld journal of surgical oncology2021
An MR-based radiomics model for differentiation between hepatocellular carcinoma and focal nodular hyperplasia in non-cirrhotic liver.
Article in World journal of surgical oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 30 citations in OpenAlex.
- Artificial intelligence-driven CT radiomics model predicts prognosis in TACE-refractory hepatocellular carcinoma.Abdominal radiology (New York) · 2026Article
- Hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) showing iso- or hyperintensity in the hepatobiliary phase: differentiation using Gd-EOB-DTPA enhanced MRI radiomics and deep learning features.BMC medical imaging · 2025Article
- Evaluating artificial intelligence for a focal nodular hyperplasia diagnosis using magnetic resonance imaging: preliminary findings.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- A T2 weighted imaging-based radiomics nomogram for the classification of hepatic blood-rich lesions: hepatocellular carcinoma and benign liver lesions.Discover oncology · 2025Article
- Development and validation of an ultrasound-based interpretable machine learning model for the classification of ≤3 cm hepatocellular carcinoma: a multicentre retrospective diagnostic study.EClinicalMedicine · 2025Article
- [Current status and prospects for screening early-stage hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Review
- Radiomic analysis of multiple MRI sequences for diagnosing liver cancer vs. focal nodular hyperplasia.American journal of translational research · 2025Article
- Angiomyxoma coexisting with focal nodular hyperplasia: A case report.Experimental and therapeutic medicine · 2024Article
- Article
- Gd-EOB-DTPA enhanced MRI nomogram model to differentiate hepatocellular carcinoma and focal nodular hyperplasia both showing iso- or hyperintensity in the hepatobiliary phase.BMC medical imaging · 2024Article
- Research on multi-model imaging machine learning for distinguishing early hepatocellular carcinoma.BMC cancer · 2024Article
- Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score.World journal of gastroenterology · 2024Review
- Bridging the Gap Between Imaging and Molecular Characterization: Current Understanding of Radiomics and Radiogenomics in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2024Review
- Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application.Abdominal radiology (New York) · 2024Review
- The role of artificial intelligence and image processing in the diagnosis, treatment, and prognosis of liver cancer: a narrative-review.Przeglad gastroenterologiczny · 2024Review
- Article
- Focal Lesions of the Liver and Radiomics: What Do We Know?Diagnostics (Basel, Switzerland) · 2023Review
- Stability of Liver Radiomics across Different 3D ROI Sizes-An MRI In Vivo Study.Tomography (Ann Arbor, Mich.) · 2021Article
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
purposeWe aimed to develop and validate a radiomics model for differentiating hepatocellular carcinoma (HCC) from focal nodular hyperplasia (FNH) in non-cirrhotic livers using Gd-DTPA contrast-enhanced magnetic resonance imaging (MRI).
methodsWe retrospectively enrolled 149 HCC and 75 FNH patients treated between May 2015 and May 2019 at our center. Patients were randomly allocated to a training (n=156) and validation set (n=68). In total, 2260 radiomics features were extracted from the arterial phase and portal venous phase of Gd-DTPA contrast-enhanced MRI. Using Max-Relevance and Min-Redundancy, random forest, least absolute shrinkage, and selection operator algorithm for dimensionality reduction, multivariable logistic regression was used to build the radiomics model. A clinical model and combined model were also established. The diagnostic performance of the models was compared.
resultsEight radiomics features were chosen for the radiomics model, and four clinical factors (age, sex, HbsAg, and enhancement pattern) were chosen for the clinical model. A combined model was built using the factors from the previous models. The classification accuracy of the combined model differentiated HCC from FNH in both the training and validation sets (0.956 and 0.941, respectively). The area under the receiver operating characteristic curve of the combined model was significantly better than that of the clinical model for both the training (0.984 vs. 0.937, p=0.002) and validation (0.972 vs. 0.903, p=0.032) sets.
conclusionsThe combined model provided a non-invasive quantitative method for differentiating HCC from FNH in non-cirrhotic liver with high accuracy. Our model may assist clinicians in the clinical decision-making process.
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