ArticleComputational and mathematical methods in medicine2022
Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Radiomic Analysis.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 39 citations in OpenAlex.
- Deep learning and machine learning in image-based hepatocellular carcinoma detection: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Review
- Radiomics for Detection and Differentiation of Intrahepatic Cholangiocarcinoma: A Systematic Review and Meta-Analysis.Cancers · 2026Review
- Artificial Intelligence in the Diagnosis and Prognostic Stratification of Hepatocellular Carcinoma: Current Evidence, Clinical Applications, and Future Perspectives.Biomedicines · 2026Review
- Interpretable ADC-based radiomics models for differentiating hepatocellular carcinoma and intrahepatic cholangiocarcinoma.Frontiers in oncology · 2026Article
- Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Article
- Fusion of shallow and deep features fromQuantitative imaging in medicine and surgery · 2024Article
- Applications of artificial intelligence in biliary tract cancers.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2024Review
- Artificial Intelligence in Liver Diseases: Recent Advances.Advances in therapy · 2024Review
- Application of AI on cholangiocarcinoma.Frontiers in oncology · 2024Review
- Differentiation of Hepatocellular Carcinoma from Intrahepatic Cholangiocarcinoma through MRI Radiomics.Cancers · 2023Article
- Lightweight neural network for smart diagnosis of cholangiocarcinoma using histopathological images.Scientific reports · 2023Article
- Application of artificial intelligence in the diagnosis of hepatocellular carcinoma.eGastroenterology · 2023Review
- Machine learning based on gadoxetic acid-enhanced MRI for differentiating atypical intrahepatic mass-forming cholangiocarcinoma from poorly differentiated hepatocellular carcinoma.Abdominal radiology (New York) · 2023Article
- Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma.Diagnostics (Basel, Switzerland) · 2023Review
- Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence.Biology · 2023Review
- Editorial: The use of data mining in radiological-pathological images for personal medicine.Frontiers in genetics · 2023Article
- Personalized intrahepatic cholangiocarcinoma prognosis prediction using radiomics: Application and development trend.Frontiers in oncology · 2023Review
- Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review.Journal of clinical medicine · 2022Review
- Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism.BMC medical informatics and decision making · 2022Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionConsidering the narrow window of surgery, early diagnosis of liver cancer is still a fundamental issue to explore. Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICCA) are considered as two different types of liver cancer because of their distinct pathogenesis, pathological features, prognosis, and responses to adjuvant therapies. Qualitative analysis of image is not enough to make a discrimination of liver cancer, especially early-stage HCC or ICCA.
methodsThis retrospective study developed a radiomic-based model in a training cohort of 122 patients. Radiomic features were extracted from computed tomography (CT) scans. Feature selection was operated with the least absolute shrinkage and operator (LASSO) logistic method. The support vector machine (SVM) was selected to build a model. An internal validation was conducted in 89 patients.
resultsIn the training set, the AUC of the evaluation of the radiomics was 0.855 higher than for radiologists at 0.689. In the valuation cohorts, the AUC of the evaluation was 0.847 and the validation was 0.659, which indicated that the established model has a significantly better performance in distinguishing the HCC from ICCA.
conclusionWe developed a radiomic diagnosis model based on CT image that can quickly distinguish HCC from ICCA, which may facilitate the differential diagnosis of HCC and ICCA in the future.
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