SynthesisFrontiers in oncology2023
Diagnostic value of radiomics in predicting Ki-67 and cytokeratin 19 expression in hepatocellular carcinoma: a systematic review and meta-analysis.
Synthesis in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Effectiveness of Machine Learning in Detecting Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- 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
- Esculentoside A suppresses the malignant progression of hepatocellular carcinoma by promoting RORB.Cell division · 2026Article
- Habitat analysis and other AI technologies for Ki-67 prediction in hepatocellular carcinoma: a multi-center study.BMC cancer · 2026Article
- Ki-67 expression correlates with hepatocellular carcinoma recurrence and is predictable using radiomics features.Abdominal radiology (New York) · 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
- From texture analysis to artificial intelligence: global research landscape and evolutionary trajectory of radiomics in hepatocellular carcinoma.Discover oncology · 2025Article
- Biparametric magnetic resonance imaging-based radiomic and deep learning models for predicting Ki-67 risk stratification in hepatocellular carcinoma.World journal of hepatology · 2025Article
- Cytokeratin expression in breast cancer: from mechanisms, progression, diagnosis, and prognosis to therapeutic implications.Molecular & cellular oncology · 2025Review
- Radiomics and Deep Learning as Important Techniques of Artificial Intelligence - Diagnosing Perspectives in Cytokeratin 19 Positive Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2025Review
- Robust radiomics: a review of guidelines for radiomics in medical imaging.Frontiers in radiology · 2025Review
- Preoperative CT radiomicsZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2024Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Radiomics have been increasingly used in the clinical management of hepatocellular carcinoma (HCC), such as markers prediction. Ki-67 and cytokeratin 19 (CK-19) are important prognostic markers of HCC. Radiomics has been introduced by many researchers in the prediction of these markers expression, but its diagnostic value remains controversial. Therefore, this review aims to assess the diagnostic value of radiomics in predicting Ki-67 and CK-19 expression in HCC. Methods: Original studies were systematically searched in PubMed, EMBASE, Cochrane Library, and Web of Science from inception to May 2023. All included studies were evaluated by the radiomics quality score. The C-index was used as the effect size of the performance of radiomics in predicting Ki-67and CK-19 expression, and the positive cutoff values of Ki-67 label index (LI) were determined by subgroup analysis and meta-regression. Results: We identified 34 eligible studies for Ki-67 (18 studies) and CK-19 (16 studies). The most common radiomics source was magnetic resonance imaging (MRI; 25/34). The pooled C-index of MRI-based models in predicting Ki-67 was 0.89 (95% CI:0.86-0.92) in the training set, and 0.87 (95% CI: 0.82-0.92) in the validation set. The pooled C-index of MRI-based models in predicting CK-19 was 0.86 (95% CI:0.81-0.90) in the training set, and 0.79 (95% CI: 0.73-0.84) in the validation set. Subgroup analysis suggested Ki-67 LI cutoff was a significant source of heterogeneity ( Conclusion: Radiomics shows promising diagnostic value in predicting positive Ki-67 or CK-19 expression. But lacks standardized guidelines, which makes the model and variables selection dependent on researcher experience, leading to study heterogeneity. Therefore, standardized guidelines are warranted for future research. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023427953.
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