ReviewInsights into imaging2021
Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know?
Review in Insights into imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed.
- Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation.World journal of gastrointestinal oncology · 2026Article
- Advances and Emerging Techniques in Transarterial Chemoembolization for Hepatocellular Carcinoma.Cancers · 2026Review
- Review
- Machine learning analysis of survival outcomes in breast cancer patients treated with chemotherapy, hormone therapy, surgery, and radiotherapy.Scientific reports · 2025Article
- Artificial intelligence to predict hepatocellular carcinoma risk in cirrhosis.World journal of gastrointestinal oncology · 2025Review
- Modeling the compressive strength behavior of concrete reinforced with basalt fiber.Scientific reports · 2025Article
- Modeling suction of unsaturated granular soil treated with biochar in plant microbial fuel cell bioelectricity system.Scientific reports · 2025Article
- Assessment of Albumin-Incorporating Scores at Hepatocellular Carcinoma Diagnosis Using Machine Learning Techniques: An Evaluation of Prognostic Relevance.Bioengineering (Basel, Switzerland) · 2024Article
- Prognosis Prediction in Head and Neck Squamous Cell Carcinoma by Radiomics and Clinical Information.Biomedicines · 2024Article
- Radiomics and machine learning based on preoperative MRI for predicting extrahepatic metastasis in hepatocellular carcinoma patients treated with transarterial chemoembolization.European journal of radiology open · 2024Article
- Application of interpretable machine learning algorithms to predict distant metastasis in ovarian clear cell carcinoma.Cancer medicine · 2024Article
- From prediction to prevention: Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring.World journal of gastroenterology · 2024Article
- Article
- Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.Frontiers in artificial intelligence · 2024Review
- Research progress on machine algorithm prediction of liver cancer prognosis after intervention therapy.American journal of cancer research · 2024Review
- From Reductionistic Approach to Systems Immunology Approach for the Understanding of Tumor Microenvironment.International journal of molecular sciences · 2023Review
- Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy.European radiology · 2023Article
- Machine Learning to Predict the Adsorption Capacity of Microplastics.Nanomaterials (Basel, Switzerland) · 2023Article
- Novel machine learning models outperform risk scores in predicting hepatocellular carcinoma in patients with chronic viral hepatitis.JHEP reports : innovation in hepatology · 2022Article
- A Machine Learning Model Based on Health Records for Predicting Recurrence After Microwave Ablation of Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the development of machine learning (ML) algorithms, a growing number of predictive models have been established for predicting the therapeutic outcome of patients with hepatocellular carcinoma (HCC) after various treatment modalities. By using the different combinations of clinical and radiological variables, ML algorithms can simulate human learning to detect hidden patterns within the data and play a critical role in artificial intelligence techniques. Compared to traditional statistical methods, ML methods have greater predictive effects. ML algorithms are widely applied in nearly all steps of model establishment, such as imaging feature extraction, predictive factor classification, and model development. Therefore, this review presents the literature pertaining to ML algorithms and aims to summarize the strengths and limitations of ML, as well as its potential value in prognostic prediction, after various treatment modalities for HCC.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.