ReviewCancers2021
Performance of Ultrasound Techniques and the Potential of Artificial Intelligence in the Evaluation of Hepatocellular Carcinoma and Non-Alcoholic Fatty Liver Disease.
Review in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 34 citations in OpenAlex.
- Artificial Intelligence in Surveillance of Hepatocellular Carcinoma: Hype or Reality?Journal of gastroenterology and hepatology · 2026Article
- Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches.World journal of gastroenterology · 2026Review
- HCC in the Era of Emerging MASH: The Role of Ultrasound in Surveillance and New Sonographic Features in Diagnosis.Cancers · 2025Review
- Management of metabolic dysfunction-associated steatotic liver disease (MASLD)-An expert consensus statement from Indian diabetologists' perspective.Diabetes, obesity & metabolism · 2025Review
- FasNet: a hybrid deep learning model with attention mechanisms and uncertainty estimation for liver tumor segmentation on LiTS17.Scientific reports · 2025Article
- Characterization of non-alcoholic fatty liver disease-related hepatocellular carcinoma on contrast-enhanced ultrasound with Sonazoid.Ultrasonography (Seoul, Korea) · 2025Article
- Contrast-Enhanced Sonography of the Liver: How to Avoid Artifacts.Diagnostics (Basel, Switzerland) · 2024Review
- Current status of ultrasonography in national cancer surveillance program for hepatocellular carcinoma in South Korea: a large-scale multicenter study.Journal of liver cancer · 2023Article
- Clinical-radiomic analysis for non-invasive prediction of liver steatosis on non-contrast CT: A pilot study.Frontiers in genetics · 2023Article
- Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review.Bioengineering (Basel, Switzerland) · 2022Review
- The Emerging Factors and Treatment Options for NAFLD-Related Hepatocellular Carcinoma.Cancers · 2021Review
- Noninvasive Assessment of Hepatitis C Virus Infected Patients Using Vibration-Controlled Transient Elastography.Journal of clinical medicine · 2021Review
- Ultrasound Methods in the Evaluation of Atherosclerosis: From Pathophysiology to Clinic.Biomedicines · 2021Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Global statistics show an increasing percentage of patients that develop non-alcoholic fatty liver disease (NAFLD) and NAFLD-related hepatocellular carcinoma (HCC), even in the absence of cirrhosis. In the present review, we analyzed the diagnostic performance of ultrasonography (US) in the non-invasive evaluation of NAFLD and NAFLD-related HCC, as well as possibilities of optimizing US diagnosis with the help of artificial intelligence (AI) assistance. To date, US is the first-line examination recommended in the screening of patients with clinical suspicion of NAFLD, as it is readily available and leads to a better disease-specific surveillance. However, the conventional US presents limitations that significantly hamper its applicability in quantifying NAFLD and accurately characterizing a given focal liver lesion (FLL). Ultrasound contrast agents (UCAs) are an essential add-on to the conventional B-mode US and to the Doppler US that further empower this method, allowing the evaluation of the enhancement properties and the vascular architecture of FLLs, in comparison to the background parenchyma. The current paper also explores the new universe of AI and the various implications of deep learning algorithms in the evaluation of NAFLD and NAFLD-related HCC through US methods, concluding that it could potentially be a game changer for patient care.
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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.