ArticleFrontiers in oncology2022
Nomogram Based on CT Radiomics Features Combined With Clinical Factors to Predict Ki-67 Expression in Hepatocellular Carcinoma.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Diagnostic value of radiomics in predicting Ki-67 and cytokeratin 19 expression in hepatocellular carcinoma: a systematic review and meta-analysis.Frontiers in oncology · 2023Pooled it
- Ki-67 expression correlates with hepatocellular carcinoma recurrence and is predictable using radiomics features.Abdominal radiology (New York) · 2026Article
- A web-based prognostic nomogram incorporating VETC-associated imaging markers for predicting postoperative recurrence in hepatocellular carcinoma.BMC cancer · 2026Article
- CT-based habitat imaging integrated with radiomics and clinicopathology for noninvasive prediction of microvascular invasion in hepatocellular carcinoma.Frontiers in oncology · 2026Article
- Noninvasive prediction of Glypican-3 expression in hepatocellular carcinoma using Habitat-based and peritumoral CT radiomics: a nomogram approach.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Evaluation and prediction of Ki-67 expression in hepatocellular carcinoma.World journal of gastrointestinal oncology · 2025Article
- CT-based machine learning radiomics predicts Ki-67 expression level and its relationship with overall survival in resectable pancreatic ductal adenocarcinoma.Abdominal radiology (New York) · 2025Article
- MRI proton density fat fraction for estimation of biological characteristics in hepatocellular carcinoma.BMC medical imaging · 2025Article
- Prediction of Ki-67 expression in hepatocellular carcinoma with machine learning models based on intratumoral and peritumoral radiomic features.World journal of gastrointestinal oncology · 2025Article
- Preoperative assessment of Ki-67 expression in hepatocellular carcinoma using a multi-parametric spectral CT approach.Quantitative imaging in medicine and surgery · 2025Article
- Preoperative multiparameter MRI-based prediction of Ki-67 expression in primary central nervous system lymphoma.Precision radiation oncology · 2025Article
- Imaging-Based Prediction of Ki-67 Expression in Hepatocellular Carcinoma: A Retrospective Study.Cancer medicine · 2025Article
- Clinical value of intratumoral and peritumoral CT radiomics models for discriminating benign and malignant parotid gland tumors.Frontiers in oncology · 2025Article
- Multi-parameter MRI-based model for the prediction of early recurrence of hepatitis B-associated hepatocellular carcinoma after microwave ablation.Frontiers in cellular and infection microbiology · 2025Article
- Nomogram based on the neutrophil-to-lymphocyte ratio and MR diffusion quantitative parameters for predicting Ki67 expression in hepatocellular carcinoma from a prospective study.Scientific reports · 2024Article
- Radiomics predicting immunohistochemical markers in primary hepatic carcinoma: Current status and challenges.Heliyon · 2024Review
- Role of the Clinical Features and MRI Parameters on Ki-67 Expression in Hepatocellular Carcinoma Patients: Development of a Predictive Nomogram.Journal of gastrointestinal cancer · 2024Article
- Radiomics nomogram based on CT radiomics features and clinical factors for prediction of Ki-67 expression and prognosis in clear cell renal cell carcinoma: a two-center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Development of a CT radiomics nomogram for preoperative prediction of Ki-67 index in pancreatic ductal adenocarcinoma: a two-center retrospective study.European radiology · 2024Article
- Predicting Ki-67 expression in hepatocellular carcinoma: nomogram based on clinical factors and contrast-enhanced ultrasound radiomics signatures.Abdominal radiology (New York) · 2024Article
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8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: The study developed and validated a radiomics nomogram based on a combination of computed tomography (CT) radiomics signature and clinical factors and explored the ability of radiomics for individualized prediction of Ki-67 expression in hepatocellular carcinoma (HCC). Methods: First-order, second-order, and high-order radiomics features were extracted from preoperative enhanced CT images of 172 HCC patients, and the radiomics features with predictive value for high Ki-67 expression were extracted to construct the radiomic signature prediction model. Based on the training group, the radiomics nomogram was constructed based on a combination of radiomic signature and clinical factors that showed an independent association with Ki-67 expression. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram. Results: Sixteen higher-order radiomic features that were associated with Ki-67 expression were used to construct the radiomics signature (AUC: training group, 0.854; validation group, 0.744). In multivariate logistic regression, alfa-fetoprotein (AFP) and Edmondson grades were identified as independent predictors of Ki-67 expression. Thus, the radiomics signature was combined with AFP and Edmondson grades to construct the radiomics nomogram (AUC: training group, 0.884; validation group, 0.819). The calibration curve and DCA showed good clinical application of the nomogram. Conclusion: The radiomics nomogram developed in this study based on the high-order features of CT images can accurately predict high Ki-67 expression and provide individualized guidance for the treatment and clinical monitoring of HCC patients.
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