ArticleFrontiers in oncology2021
Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma
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 19 papers, 5 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Magnetic resonance imaging radiomics for predicting hepatocellular carcinoma recurrence following resection or ablation: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Pooled it
- Research progress of MRI-based radiomics in hepatocellular carcinoma.Frontiers in oncology · 2025Pooled it
- Quality of radiomics for predicting microvascular invasion in hepatocellular carcinoma: a systematic review.European radiology · 2023Pooled it
- Radiomics for the Preoperative Evaluation of Microvascular Invasion in Hepatocellular Carcinoma: A Meta-Analysis.Frontiers in oncology · 2022Pooled it
- The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis.Frontiers in oncology · 2022Pooled it
- Development and validation of a nomogram based on tumor margin irregularity and alpha-fetoprotein for predicting microvascular invasion in hepatocellular carcinoma.Frontiers in oncology · 2026Article
- Predictive value of a constructed artificial neural network model for microvascular invasion in hepatocellular carcinoma: A retrospective study.World journal of gastrointestinal oncology · 2025Article
- Exploring a multiparameter MRI-based radiomics approach to predict tumor proliferation status of serous ovarian carcinoma.Insights into imaging · 2024Article
- Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score.World journal of gastroenterology · 2024Review
- Radiogenomics nomogram based on MRI and microRNAs to predict microvascular invasion of hepatocellular carcinoma.Frontiers in oncology · 2024Article
- Radiomics nomogram for the prediction of microvascular invasion of HCC and patients' benefit from postoperative adjuvant TACE: a multi-center study.European radiology · 2023Article
- Evaluation of Preoperative Microvascular Invasion in Hepatocellular Carcinoma Through Multidimensional Parameter Combination Modeling Based on Gd-EOB-DTPA MRI.Journal of clinical and translational hepatology · 2023Article
- Article
- Article
- [Research progress in radiomics based on CT and MRI images for prediction of microvascular invasion in hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2022Article
- Nomogram Based on CT Radiomics Features Combined With Clinical Factors to Predict Ki-67 Expression in Hepatocellular Carcinoma.Frontiers in oncology · 2022Article
- Review
- 2-[Cancers · 2021Article
- Progress of MRI Radiomics in Hepatocellular Carcinoma.Frontiers in oncology · 2021Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo systematically evaluate and compare the predictive capability for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients based on radiomics from multi-parametric MRI (mp-MRI) including six sequences when used individually or combined, and to establish and validate the optimal combined model.
methodsA total of 195 patients confirmed HCC were divided into training (n = 136) and validation (n = 59) datasets. All volumes of interest of tumors were respectively segmented on T
resultsAmong nine radiomics models, the model from all sequences performed best with AUCs 0.889 and 0.822 in the training and validation datasets, respectively. The combined model incorporating radiomics from all sequences and effective clinical features achieved satisfactory preoperative prediction of MVI with AUCs 0.901 and 0.840, respectively, and could identify the higher risk population of MVI (P < 0.001). The Delong test manifested significant differences with P < 0.001 in the training dataset and P = 0.005 in the validation dataset between the combined model and clinical model.
conclusionsThe combined model can preoperatively and noninvasively predict MVI in HCC patients and may act as a usefully clinical tool to guide subsequent individualized treatment.
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