ArticleMedical & biological engineering & computing2020
Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans.
Article in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review.European radiology · 2023Pooled it
- USSGAN: Unsupervised Spectral and Spatial Attention-Based Generative Adversarial Network for Cholangiocarcinoma Detection.Chemical & biomedical imaging · 2025Article
- A computer vision approach for the classification of multi liver tumor using computed tomography scan.BMC medical imaging · 2025Article
- Multiphase Computed Tomography Scan Findings for Artificial Intelligence Training in the Differentiation of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Interobserver Agreement of Expert Abdominal Radiologists.Diagnostics (Basel, Switzerland) · 2025Article
- Liver biopsy in the modern era: from traditional techniques to artificial intelligence and multi-omics integration.Frontiers in medicine · 2025Review
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- Artificial intelligence in imaging for liver disease diagnosis.Frontiers in medicine · 2025Review
- A flexible deep learning framework for liver tumor diagnosis using variable multi-phase contrast-enhanced CT scans.Journal of cancer research and clinical oncology · 2024Article
- Artificial intelligence in liver cancer - new tools for research and patient management.Nature reviews. Gastroenterology & hepatology · 2024Review
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
- Application of AI on cholangiocarcinoma.Frontiers in oncology · 2024Review
- Differential diagnosis of hepatocellular carcinoma and intrahepatic cholangiocarcinoma by ultrasonography combined with multiphase enhanced computed tomography.Journal of Cancer · 2024Article
- Establishment and validation of a nomogram containing cytokeratin fragment antigen 21-1 for the differential diagnosis of intrahepatic cholangiocarcinoma and hepatocellular carcinoma.Frontiers in oncology · 2024Article
- Lightweight neural network for smart diagnosis of cholangiocarcinoma using histopathological images.Scientific reports · 2023Article
- A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging.Diagnostics (Basel, Switzerland) · 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
- Systematic review with radiomics quality score of cholangiocarcinoma: an EuSoMII Radiomics Auditing Group Initiative.Insights into imaging · 2023Article
- A Deep Learning Workflow for Mass-Forming Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma Classification Based on MRI.Current oncology (Toronto, Ont.) · 2022Article
- The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma.Cancers · 2022Review
- A Narrative Review on LI-RADS Algorithm in Liver Tumors: Prospects and Pitfalls.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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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
Liver and bile duct cancers are leading causes of worldwide cancer death. The most common ones are hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). Influencing factors and prognosis of HCC and ICC are different. Precise classification of these two liver cancers is essential for treatment and prevention plans. The aim of this study is to develop a machine-based method that differentiates between the two types of liver cancers from multi-phase abdominal computerized tomography (CT) scans. The proposed method consists of two major steps. In the first step, the liver is segmented from the original images using a convolutional neural network model, together with task-specific pre-processing and post-processing techniques. In the second step, by looking at the intensity histograms of the segmented images, we extract features from regions that are discriminating between HCC and ICC, and use them as an input for classification using support vector machine model. By testing on a dataset of labeled multi-phase CT scans provided by Maharaj Nakorn Chiang Mai Hospital, Thailand, we have obtained 88% in classification accuracy. Our proposed method has a great potential in helping radiologists diagnosing liver cancer.
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Registered trials
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 OpenQuestion graph.