ArticleWorld journal of hepatology2025
Biparametric magnetic resonance imaging-based radiomic and deep learning models for predicting Ki-67 risk stratification in hepatocellular carcinoma.
Article in World journal of hepatology, 2025. 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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4 citing papers in PubMed.
- Magnetic resonance imaging R2* mapping for evaluating Ki-67 proliferation index and cytokeratin 19 expression in solitary iron-sparing hepatocellular carcinoma: a retrospective single-center study.Quantitative imaging in medicine and surgery · 2026Article
- Multi-scale deep learning models based on MRI for predicting pathological differentiation and evaluating its association with recurrence-free survival in hepatocellular carcinoma: an explainable machine learning study.Journal of gastrointestinal oncology · 2026Article
- Predictive Efficacy of a Combined Triphasic CT Radiomics and Clinical Feature Model for Ki-67 Expression in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
- Advancing precision in hepatocellular carcinoma prognostication: The promise of biparametric magnetic resonance imaging-based multimodal models.World journal of hepatology · 2025Article
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Abstract
backgroundHepatocellular carcinoma (HCC) is a prevalent and life-threatening cancer with increasing incidence worldwide. High Ki-67 risk stratification is closely associated with higher recurrence rates and worse outcomes following curative therapies in patients with HCC. However, the performance of radiomic and deep transfer learning (DTL) models derived from biparametric magnetic resonance imaging (bpMRI) in predicting Ki-67 risk stratification and recurrence-free survival (RFS) in patients with HCC remains limited.
aimTo develop a nomogram model integrating bpMRI-based radiomic and DTL signatures for predicting Ki-67 risk stratification and RFS in patients with HCC.
methodsThis study included 198 patients with histopathologically confirmed HCC who underwent preoperative bpMRI. Ki-67 risk stratification was categorized as high (> 20%) or low (≤ 20%) according to immunohistochemical staining. Radiomic and DTL signatures were extracted from the T2-weighted and arterial-phase images and combined through a random forest algorithm to establish radiomic and DTL models, respectively. Multivariate regression analysis identified clinical risk factors for high Ki-67 risk stratification, and a predictive nomogram model was developed.
resultsA nonsmooth margin and the absence of an enhanced capsule were independent factors for high Ki-67 risk stratification. The area under the curve (AUC) of the clinical model was 0.77, while those of the radiomic and DTL models were 0.81 and 0.87, respectively, for the prediction of high Ki-67 risk stratification, and the nomogram model achieved a better AUC of 0.92. The median RFS times for patients with high and low Ki-67 risk stratification were 33.00 months and 66.73 months, respectively (
conclusionOur developed nomogram model demonstrated good performance in predicting Ki-67 risk stratification and predicting survival outcomes in patients with HCC.
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