ArticleAbdominal radiology (New York)2020
Deep learning assisted differentiation of hepatocellular carcinoma from focal liver lesions: choice of four-phase and three-phase CT imaging protocol.
Article in Abdominal radiology (New York), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis.Journal of imaging informatics in medicine · 2024Pooled it
- Deep learning and machine learning in image-based hepatocellular carcinoma detection: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Review
- Artificial Intelligence in Hepatocellular Carcinoma: Current Applications, Clinical Performance, and Barriers to Implementation.Journal of clinical medicine · 2026Review
- Global scientific trends on hepatocellular carcinoma research from 2004 to 2023: A bibliometric and visualized analysis.World journal of gastrointestinal oncology · 2025Article
- Artificial intelligence for detection and characterization of focal hepatic lesions: a review.Abdominal radiology (New York) · 2025Review
- 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
- Comparison of MRI and CT based deep learning radiomics analyses and their combination for diagnosing intrahepatic cholangiocarcinoma.Scientific reports · 2025Article
- Automatic localization and deep convolutional generative adversarial network-based classification of focal liver lesions in computed tomography images: A preliminary study.Journal of gastroenterology and hepatology · 2025Article
- Liver biopsy in the modern era: from traditional techniques to artificial intelligence and multi-omics integration.Frontiers in medicine · 2025Review
- Artificial intelligence in imaging for liver disease diagnosis.Frontiers in medicine · 2025Review
- Application of a deep learning algorithm for the diagnosis of HCC.JHEP reports : innovation in hepatology · 2025Article
- 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
- Four-phase CT lesion recognition based on multi-phase information fusion framework and spatiotemporal prediction module.Biomedical engineering online · 2024Article
- The power of deep learning in simplifying feature selection for hepatocellular carcinoma: a review.BMC medical informatics and decision making · 2024Review
- A multicenter clinical AI system study for detection and diagnosis of focal liver lesions.Nature communications · 2024Article
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
- Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis.Cancers · 2023Review
- Deep learning-assisted LI-RADS grading and distinguishing hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT: a two-center study.European radiology · 2023Article
- Application of artificial intelligence in the diagnosis of hepatocellular carcinoma.eGastroenterology · 2023Review
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15 authors.
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Abstract
purposeTo evaluate whether a three-phase dynamic contrast-enhanced CT protocol, when combined with a deep learning model, has similar accuracy in differentiating hepatocellular carcinoma (HCC) from other focal liver lesions (FLLs) compared with a four-phase protocol.
methodsThree hundred and forty-two patients (mean age 49.1 ± 10.5 years, range 19-86 years, 65.8% male) scanned with a four-phase CT protocol (precontrast, arterial, portal-venous and delayed phases) were retrospectively enrolled. A total of 449 FLLs were categorized into HCC and non-HCC groups based on the best available reference standard. Three convolutional dense networks (CDNs) with the input of four-phase CT images (model A), three-phase images without portal-venous phase (model B) and three-phase images without precontrast phase (model C) were trained on 80% of lesions and evaluated in the other 20% by receiver operating characteristics (ROC) and confusion matrix analysis. The DeLong test was performed to compare the areas under the ROC curves (AUCs) of A with B, B with C, and A with C.
resultsThe diagnostic accuracy in differentiating HCC from other FLLs on test sets was 83.3% for model A, 81.1% for model B and 85.6% for model C, and the AUCs were 0.925, 0.862 and 0.920, respectively. The AUCs of models A and C did not differ significantly (p = 0.765), but the AUCs of models A and B (p = 0.038) and of models B and C (p = 0.028) did.
conclusionsWhen combined with a CDN, a three-phase CT protocol without precontrast showed similar diagnostic accuracy as a four-phase protocol in differentiating HCC from other FLLs, suggesting that the multiphase CT protocol for HCC diagnosis might be optimized by removing the precontrast phase to reduce radiation dose.
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