ArticleWorld journal of gastrointestinal oncology2024
Construction and evaluation of a liver cancer risk prediction model based on machine learning.
Article in World journal of gastrointestinal oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Computed tomography radiomics-based machine learning nomogram for preoperative prediction of glypican-3 expression in hepatocellular carcinoma.World journal of radiology · 2026Article
- Protein Induced by Vitamin K Absence or Antagonist II in Primary Liver Cancer: Basic Research Insights and Clinical Applications.Journal of clinical and translational hepatology · 2025Review
- Explainable attention-enhanced heuristic paradigm for multi-view prognostic risk score development in hepatocellular carcinoma.Hepatology international · 2025Article
- Development and Validation of a Nomogram Model for Predicting Non-keratinizing Nasopharyngeal Carcinoma by Combining Sex and Three EB Virus Antibodies.Cancer control : journal of the Moffitt Cancer CenterArticle
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Authors and funding
6 authors.
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Abstract
backgroundLiver cancer is one of the most prevalent malignant tumors worldwide, and its early detection and treatment are crucial for enhancing patient survival rates and quality of life. However, the early symptoms of liver cancer are often not obvious, resulting in a late-stage diagnosis in many patients, which significantly reduces the effectiveness of treatment. Developing a highly targeted, widely applicable, and practical risk prediction model for liver cancer is crucial for enhancing the early diagnosis and long-term survival rates among affected individuals.
aimTo develop a liver cancer risk prediction model by employing machine learning techniques, and subsequently assess its performance.
methodsIn this study, a total of 550 patients were enrolled, with 190 hepatocellular carcinoma (HCC) and 195 cirrhosis patients serving as the training cohort, and 83 HCC and 82 cirrhosis patients forming the validation cohort. Logistic regression (LR), support vector machine (SVM), random forest (RF), and least absolute shrinkage and selection operator (LASSO) regression models were developed in the training cohort. Model performance was assessed in the validation cohort. Additionally, this study conducted a comparative evaluation of the diagnostic efficacy between the ASAP model and the model developed in this study using receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) to determine the optimal predictive model for assessing liver cancer risk.
resultsSix variables including age, white blood cell, red blood cell, platelet counts, alpha-fetoprotein and protein induced by vitamin K absence or antagonist II levels were used to develop LR, SVM, RF, and LASSO regression models. The RF model exhibited superior discrimination, and the area under curve of the training and validation sets was 0.969 and 0.858, respectively. These values significantly surpassed those of the LR (0.850 and 0.827), SVM (0.860 and 0.803), LASSO regression (0.845 and 0.831), and ASAP (0.866 and 0.813) models. Furthermore, calibration and DCA indicated that the RF model exhibited robust calibration and clinical validity.
conclusionThe RF model demonstrated excellent prediction capabilities for HCC and can facilitate early diagnosis of HCC in clinical practice.
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