ArticleFrontiers in oncology2021
Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Q.Liver software for the planning of treatment of liver cancer via transarterial radioembolization with yttrium-90 resin microspheres based on single-photon emission computed tomography-computed tomography.Quantitative imaging in medicine and surgery · 2026Article
- Interpretable model based on multisequence magnetic resonance imaging radiomics for predicting the pathological grades of hepatocellular carcinomas.World journal of radiology · 2025Article
- Colorectal cancer liver metastases: A radiologic point of view.World journal of gastrointestinal oncology · 2025Article
- Preoperative prediction of microvascular invasion and relapse-free survival in hepatocellular Carcinoma ≥3 cm using CT radiomics: Development and external validation.BMC medical imaging · 2025Article
- CT-based radiomics deep learning signatures for non-invasive prediction of metastatic potential in pheochromocytoma and paraganglioma: a multicohort study.Insights into imaging · 2025Article
- Computed tomography radiomics combined with clinical parameters for hepatocellular carcinoma differentiation: a machine learning investigation.Polish journal of radiology · 2025Article
- MRI-based radiomics machine learning model to differentiate non-clear cell renal cell carcinoma from benign renal tumors.European journal of radiology open · 2024Article
- Interpretable machine learning based on CT-derived extracellular volume fraction to predict pathological grading of hepatocellular carcinoma.Abdominal radiology (New York) · 2024Article
- Feasibility analysis of arterial CT radiomics model to predict the risk of local and metastatic recurrence after radical cystectomy for bladder cancer.Discover oncology · 2024Article
- Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score.World journal of gastroenterology · 2024Review
- CT-based radiomics for predicting pathological grade in hepatocellular carcinoma.Frontiers in oncology · 2024Article
- Five machine learning-based radiomics models for preoperative prediction of histological grade in hepatocellular carcinoma.Journal of cancer research and clinical oncology · 2023Article
- Dual segmentation models for poorly and well-differentiated hepatocellular carcinoma using two-step transfer deep learning on dynamic contrast-enhanced CT images.Physical and engineering sciences in medicine · 2023Article
- Current status and future perspectives of radiomics in hepatocellular carcinoma.World journal of gastroenterology · 2023Review
- Radiomics-Based Prediction of Future Portal Vein Tumor Infiltration in Patients with HCC-A Proof-of-Concept Study.Cancers · 2022Article
- Role of MRI-Derived Radiomics Features in Determining Degree of Tumor Differentiation of Hepatocellular Carcinoma.Diagnostics (Basel, Switzerland) · 2022Article
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8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo investigate the value of contrast-enhanced computer tomography (CT)-based on radiomics in discriminating high-grade and low-grade hepatocellular carcinoma (HCC) before surgery.
methodsThe retrospective study including 161 consecutive subjects with HCC which was approved by the institutional review board, and the patients were divided into a training group (n = 112) and test group (n = 49) from January 2013 to January 2018. The least absolute shrinkage and selection operator (LASSO) was used to select the most valuable features to build a support vector machine (SVM) model. The performance of the predictive model was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity.
resultsThe SVM model showed an acceptable ability to differentiate high-grade from low-grade HCC, with an AUC of 0.904 in the training dataset and 0.937 in the test dataset, accuracy (92.2% versus 95.7%), sensitivity(82.5% versus 88.0%), and specificity (92.7% versus 95.8%), respectively.
conclusionThe machine learning-based radiomics reflects a better evaluating performance in differentiating HCC between low-grade and high-grade, which may contribute to personalized treatment.
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