ArticleScientific reports2019
Machine-learning Approach for the Development of a Novel Predictive Model for the Diagnosis of Hepatocellular Carcinoma.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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Who cites it
58 citing papers in PubMed.
- A novel predictive model of microvascular invasion in hepatocellular carcinoma based on differential protein expression.BMC gastroenterology · 2023Trial
- Innovative fusion models: elevating preoperative gross ETE prediction in thyroid cancer patients.Scientific reports · 2026Article
- An intelligent healthcare framework for hepatocellular carcinoma diagnosis based on aggregated learners from biomedical data utilising explainable artificial intelligence.Scientific reports · 2026Article
- Leveraging AI and Natural Compounds: Innovative Approaches in the Diagnosis and Treatment of Hepatocellular Carcinoma.Current pharmaceutical design · 2026Review
- Minimal clinical predictors enable machine learning detection of hepatocellular carcinoma in a Filipino cohort.Scientific reports · 2025Article
- Review
- Clinical validation of AI assisted animal ultrasound models for diagnosis of early liver trauma.Scientific reports · 2025Article
- Artificial intelligence to predict hepatocellular carcinoma risk in cirrhosis.World journal of gastrointestinal oncology · 2025Review
- Comparison of Intratumoral and Peritumoral Deep Learning, Radiomics, and Fusion Models for Predicting KRAS Gene Mutations in Rectal Cancer Based on Endorectal Ultrasound Imaging.Annals of surgical oncology · 2025Article
- Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study.BMC infectious diseases · 2025Article
- Multi-parameter MRI-Based Machine Learning Model to Evaluate the Efficacy of STA-MCA Bypass Surgery for Moyamoya Disease: A Pilot Study.Journal of imaging informatics in medicine · 2025Article
- Artificial intelligence in hepatocellular carcinoma screening: applications and challenges.Frontiers in medicine · 2025Review
- The tumor immune microenvironment: implications for cancer immunotherapy, treatment strategies, and monitoring approaches.Frontiers in immunology · 2025Review
- Current and new strategies for hepatocellular carcinoma surveillance.Gastroenterology report · 2025Review
- Harnessing multi-output machine learning approach and dynamical observables from network structure to optimize COVID-19 intervention strategies.Biology methods & protocols · 2025Article
- AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease.Journal of clinical medicine · 2024Review
- Analysis of four long non-coding RNAs for hepatocellular carcinoma screening and prognosis by the aid of machine learning techniques.Scientific reports · 2024Article
- Serum Fusion Transcripts to Assess the Risk of Hepatocellular Carcinoma and the Impact of Cancer Treatment through Machine Learning.The American journal of pathology · 2024Article
- Alpha-fetoprotein: Past, present, and future.Hepatology communications · 2024Review
- Development of a transformer model for predicting the prognosis of patients with hepatocellular carcinoma after radiofrequency ablation.Hepatology international · 2024Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Because of its multifactorial nature, predicting the presence of cancer using a single biomarker is difficult. We aimed to establish a novel machine-learning model for predicting hepatocellular carcinoma (HCC) using real-world data obtained during clinical practice. To establish a predictive model, we developed a machine-learning framework which developed optimized classifiers and their respective hyperparameter, depending on the nature of the data, using a grid-search method. We applied the current framework to 539 and 1043 patients with and without HCC to develop a predictive model for the diagnosis of HCC. Using the optimal hyperparameter, gradient boosting provided the highest predictive accuracy for the presence of HCC (87.34%) and produced an area under the curve (AUC) of 0.940. Using cut-offs of 200 ng/mL for AFP, 40 mAu/mL for DCP, and 15% for AFP-L3, the accuracies of AFP, DCP, and AFP-L3 for predicting HCC were 70.67% (AUC, 0.766), 74.91% (AUC, 0.644), and 71.05% (AUC, 0.683), respectively. A novel predictive model using a machine-learning approach reduced the misclassification rate by about half compared with a single tumor marker. The framework used in the current study can be applied to various kinds of data, thus potentially become a translational mechanism between academic research and clinical practice.
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