ArticleWorld journal of gastrointestinal oncology2025
Prediction of Ki-67 expression in hepatocellular carcinoma with machine learning models based on intratumoral and peritumoral radiomic features.
Article in World journal of gastrointestinal oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Habitat analysis and other AI technologies for Ki-67 prediction in hepatocellular carcinoma: a multi-center study.BMC cancer · 2026Article
- Machine learning model-based approach using cellular proliferation marker expression for preoperative clinical decision-making in patients with hepatocellular carcinoma.World journal of gastrointestinal oncology · 2025Article
- Integrating radiomics and machine learning for the diagnosis and prognosis of hepatocellular carcinoma.World journal of gastrointestinal oncology · 2025Article
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Authors and funding
7 authors.
Funding
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Abstract
backgroundHepatocellular carcinoma (HCC) is one of the most common malignant tumours of the digestive system worldwide. The expression of Ki-67 is crucial for the diagnosis, treatment, and prognostic evaluation of HCC.
aimTo construct a machine learning model for the preoperative evaluation of Ki-67 expression in HCC and to assist in clinical decision-making.
methodsThis study included 164 pathologically confirmed HCC patients. Radiomic features were extracted from the computed tomography images reconstructed by superresolution of the intratumoral and peritumoral regions. Features were selected
resultsIn total, 164 HCC patients, namely, 104 patients with high Ki-67 expression and 60 with low Ki-67 expression, were included. Compared with the models in which only intratumoral or peritumoral features were used, the fusion model in which intratumoral and peritumoral features were combined demonstrated stronger predictive ability. Moreover, the clinical-radiomics model including the Radscore and clinical features had higher predictive performance than did the fusion model (area under the ROC curve = 0.848
conclusionA machine learning model based on the radiomic features of the intratumoral and peritumoral regions on superresolution computed tomography in conjunction with clinical factors can accurately evaluate Ki-67 expression. The model provides valuable assistance in selecting treatment strategies for HCC patients and contributes to research on neoadjuvant therapy for liver cancer.
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