ArticleWorld journal of oncology2024
Radiomics of Preoperative Multi-Sequence Magnetic Resonance Imaging Can Improve the Predictive Performance of Microvascular Invasion in Hepatocellular Carcinoma.
Article in World journal of oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Radiologic exposomics: imaging the environmental imprint on cancer for precision oncology.La Radiologia medica · 2026Review
- 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
- Multiphase MRI radiomics model for predicting microvascular invasion in HCC: Development and clinical validation.iLIVER · 2025Article
- Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models to predict microvascular invasion in hepatocellular carcinoma: a multicenter study.BMC medical imaging · 2025Article
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12 authors.
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Abstract
Background: The aim of the study is to demonstrate that radiomics of preoperative multi-sequence magnetic resonance imaging (MRI) can indeed improve the predictive performance of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods: A total of 206 patients with pathologically confirmed HCC who underwent preoperative enhanced MRI were retrospectively recruited. Univariate and multivariate logistic regression analysis identified the independent clinicoradiologic predictors of MVI present and constituted the clinicoradiologic model. Recursive feature elimination (RFE) was applied to select radiomics features (extracted from six sequence images) and constructed the radiomics model. Clinicoradiologic model plus radiomics model formed the clinicoradiomics model. Five-fold cross-validation was used to validate the three models. Discrimination, calibration, and clinical utility were used to evaluate the performance. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to compare the prediction accuracy between models. Results: The clinicoradiologic model contained alpha-fetoprotein (AFP)_lg10, radiological capsule enhancement, enhancement pattern and arterial peritumoral enhancement, which were independent risk factors of MVI. There were 18 radiomics features related to MVI constructed the radiomics model. The mean area under the receiver operating curve (AUC) of clinicoradiologic, radiomics and clinicoradiomics model were 0.849, 0.925 and 0.950 in the training cohort and 0.846, 0.907 and 0.933 in the validation cohort, respectively. The three models' calibration curves fitted well, and decision curve analysis (DCA) confirmed the clinical usefulness. Compared with the clinicoradiologic model, the NRI of radiomics and clinicoradiomics model increased significantly by 0.575 and 0.825, respectively, and the IDI increased significantly by 0.280 and 0.398, respectively. Conclusions: Radiomics of preoperative multi-sequence MRI can improve the predictive performance of MVI in HCC.
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