ArticleScientific reports2022
Automated machine learning for differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma on multiphasic MRI.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancing Diagnostic Accuracy in Liver Cancer: A Systematic Review of Artificial Intelligence Applications in Hepatocellular Carcinoma and Cholangiocarcinoma Detection Using Abdominal CT Imaging.Asian Pacific journal of cancer prevention : APJCP · 2026Pooled it
- Deep learning and machine learning in image-based hepatocellular carcinoma detection: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Review
- Integrated multi-task learning framework for hepatocellular carcinoma segmentation and histological grading using fused multi-phase MRI.Abdominal radiology (New York) · 2026Article
- Radiomics for Detection and Differentiation of Intrahepatic Cholangiocarcinoma: A Systematic Review and Meta-Analysis.Cancers · 2026Review
- LR-M for CT/MRI on LI-RADS v2018: a review of imaging criteria, performance, challenges and future directions from an end-user perspective.Abdominal radiology (New York) · 2026Review
- Interpretable ADC-based radiomics models for differentiating hepatocellular carcinoma and intrahepatic cholangiocarcinoma.Frontiers in oncology · 2026Article
- Artificial intelligence in the diagnosis and prognosis of intrahepatic cholangiocarcinoma: Applications and challenges.World journal of gastrointestinal oncology · 2025Review
- Advances in Endoscopic Diagnosis and Management of Cholangiocarcinoma.Journal of clinical medicine · 2025Review
- Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Advancements in Artificial Intelligence-Enhanced Imaging Diagnostics for the Management of Liver Disease-Applications and Challenges in Personalized Care.Bioengineering (Basel, Switzerland) · 2024Review
- Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- Applications of artificial intelligence in biliary tract cancers.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2024Review
- Review
- Research trends and hotspots evolution of artificial intelligence for cholangiocarcinoma over the past 10 years: a bibliometric analysis.Frontiers in oncology · 2024Review
- Wearable sensors in patient acuity assessment in critical care.Frontiers in neurology · 2024Article
- Application of AI on cholangiocarcinoma.Frontiers in oncology · 2024Review
- Computerized Diagnosis of Liver Tumors From CT Scans Using a Deep Neural Network Approach.IEEE journal of biomedical and health informatics · 2023Article
- Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma.Diagnostics (Basel, Switzerland) · 2023Review
- Radiomics-based differentiation of hepatocellular carcinoma and dysplastic nodules using noncontrast abbreviated MRI.Digital healthArticle
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Authors and funding
8 authors.
Funding
Abstract
With modern management of primary liver cancer shifting towards non-invasive diagnostics, accurate tumor classification on medical imaging is increasingly critical for disease surveillance and appropriate targeting of therapy. Recent advancements in machine learning raise the possibility of automated tools that can accelerate workflow, enhance performance, and increase the accessibility of artificial intelligence to clinical researchers. We explore the use of an automated Tree-Based Optimization Tool that leverages a genetic programming algorithm for differentiation of the two common primary liver cancers on multiphasic MRI. Manual and automated analyses were performed to select an optimal machine learning model, with an accuracy of 73-75% (95% CI 0.59-0.85), sensitivity of 70-75% (95% CI 0.48-0.89), and specificity of 71-79% (95% CI 0.52-0.90) on manual optimization, and an accuracy of 73-75% (95% CI 0.59-0.85), sensitivity of 65-75% (95% CI 0.43-0.89) and specificity of 75-79% (95% CI 0.56-0.90) for automated machine learning. We found that automated machine learning performance was similar to that of manual optimization, and it could classify hepatocellular carcinoma and intrahepatic cholangiocarcinoma with an sensitivity and specificity comparable to that of radiologists. However, automated machine learning performance was poor on a subset of scans that met LI-RADS criteria for LR-M. Exploration of additional feature selection and classifier methods with automated machine learning to improve performance on LR-M cases as well as prospective validation in the clinical setting are needed prior to implementation.
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