ArticleEClinicalMedicine2025
Development and validation of an ultrasound-based interpretable machine learning model for the classification of ≤3 cm hepatocellular carcinoma: a multicentre retrospective diagnostic study.
Article in EClinicalMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Explainable artificial intelligence in medical ultrasound: a WoSCC-based bibliometric analysis, evidence map, and Scopus concordance assessment.Frontiers in medicine · 2026Pooled it
- Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis.Frontiers in oncology · 2025Pooled it
- From black-box prediction to transparent insight: the status quo and paradigm shift of explainable artificial intelligence in hepatocellular carcinoma research.Journal of the Egyptian National Cancer Institute · 2026Review
- [Application and prospects of artificial intelligence from tool empowerment to paradigm reconstruction for whole-course diagnosis and treatment of liver diseases by ultrasound].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026Review
- Systematic evaluation of machine learning models for clinical risk prediction on real-world hospital datasets.iScience · 2026Article
- Global Advances in Hepatocellular Carcinoma Research and Therapy in 2025.Cancer innovation · 2026Review
- Artificial Intelligence in Hepatocellular Carcinoma: Current Applications, Clinical Performance, and Barriers to Implementation.Journal of clinical medicine · 2026Review
- Response to letter-Machine learning prediction of rapid HBsAg seroclearance at week 24 in inactive carriers treated with pegylated interferon.Hepatology international · 2026Article
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- Multimodal artificial intelligence technology in the precision diagnosis and treatment of gastroenterology and hepatology: Innovative applications and challenges.World journal of gastroenterology · 2025Review
- Machine Learning and Shapley Additive Explanations Value Integration for Predicting the Prognostic of Anti-N-Methyl-D-Aspartate Receptor Encephalitis: Model Development and Evaluation Study.JMIR medical informatics · 2025Article
- Review
- [New progress in the technology of non-invasive diagnosis for small hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Review
- Development of a machine learning model to predict overall survival for large hepatocellular carcinoma at BCLC stage A or B after curative hepatectomy.Frontiers in immunology · 2025Article
- Development of a weighted Alpha-Fetoprotein tumor burden score-integrated nomogram for predicting overall survival in locally ablated hepatocellular carcinoma patients.Frontiers in oncology · 2025Article
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Authors and funding
14 authors.
Funding
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Abstract
Background: Our study aimed to develop a machine learning (ML) model utilizing grayscale ultrasound (US) to distinguish ≤3 cm small hepatocellular carcinoma (sHCC) from non-HCC lesions. Methods: A total of 1052 patients with 1058 liver lesions ≤3 cm from 55 hospitals were collected between May 2017 and June 2021, and 756 liver lesions were randomly allocated into train and internal validation cohorts at a 8:2 ratio for the development and evaluation of ML models based on multilayer perceptron (MLP) and extreme gradient boosting (XGBoost) methods (Model Findings: Model Interpretation: A diagnostic model for sHCC was developed and validated using ML and grayscale US from large cohorts. This model significantly improved the diagnostic performance of grayscale US for sHCC compared with experts. Funding: This work was supported by National Key Research and Development Program of China (2022YFC2405500), Major Research Program of the National Natural Science Foundation of China (92159305), National Science Fund for Distinguished Young Scholars (82325027), Key project of National Natural Science Foundation of China (82030047), Military Fund for Geriatric Diseases (20BJZ42), National Natural Science Foundation of China Special Program (82441011). National Natural Science Foundation of China (82402280), National Natural Science Foundation of China (32171363), Key Research and Development Program for Social Development of Yunnan Science and Technology Department (202403AC100014).
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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 Socratic graph.