ArticleFrontiers in oncology2022
Ensemble learning based on efficient features combination can predict the outcome of recurrence-free survival in patients with hepatocellular carcinoma within three years after surgery.
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 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for the prediction of posthepatectomy recurrence in hepatocellular carcinoma: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- Article
- Explainable artificial intelligence and ensemble learning for hepatocellular carcinoma classification: State of the art, performance, and clinical implications.World journal of hepatology · 2025Review
- Comparative performance of multiple ensemble learning models for preoperative prediction of tumor deposits in rectal cancer based on MR imaging.Scientific reports · 2025Article
- Early prediction of microvascular invasion (MVI) occurrence in hepatocellular carcinoma (HCC) byEuropean journal of medical research · 2024Article
- MRI radiomics based on deep learning automated segmentation to predict early recurrence of hepatocellular carcinoma.Insights into imaging · 2024Article
- Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors.Biomedicines · 2023Review
- A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images.Scientific reports · 2023Article
- Development of ensemble learning models for prognosis of hepatocellular carcinoma patients underwent postoperative adjuvant transarterial chemoembolization.Frontiers in oncology · 2023Article
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7 authors.
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Abstract
Preoperative prediction of recurrence outcome in hepatocellular carcinoma (HCC) facilitates physicians' clinical decision-making. Preoperative imaging and related clinical baseline data of patients are valuable for evaluating prognosis. With the widespread application of machine learning techniques, the present study proposed the ensemble learning method based on efficient feature representations to predict recurrence outcomes within three years after surgery. Radiomics features during arterial phase (AP) and clinical data were selected for training the ensemble models. In order to improve the efficiency of the process, the lesion area was automatically segmented by 3D U-Net. It was found that the mIoU of the segmentation model was 0.8874, and the Light Gradient Boosting Machine (LightGBM) was the most superior, with an average accuracy of 0.7600, a recall of 0.7673, a F
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