The diagnostic performance of contrast-enhanced CT versus extracellular contrast agent-enhanced MRI in detecting hepatocellular carcinoma: direct comparison and a meta-analysis

Chen, Xi; Li, Mingkai; Guo, Ruomi; Liu, Weimin; Li, Jianwen; Zong, Xiaodan; Chen, Qilong; Wang, Jin

doi:10.1007/s00261-022-03484-7

Cited by 7 publications

(4 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The European Association for the Study of the Liver guidelines has recommended the use of CEUS, contrast-enhanced CT, or MRI as the diagnostic modalities for HCC surveillance ( 19 ). Many studies have suggested that MRI has a higher diagnostic value than CEUS or CT, particularly for small HCC, because of its higher sensitivity and accuracy ( 20 , 21 ). Our study also demonstrated that “MRI” was the only imaging modality included in the top 10 diagnosis-related MeSH terms, and its frequency of appearance incidence has increased steadily.…”

Section: Discussionmentioning

confidence: 99%

A systematic review of progress on hepatocellular carcinoma research over the past 30 years: a machine-learning-based bibliometric analysis

Lee,

Hwang,

Sohn

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

IntroductionResearch on hepatocellular carcinoma (HCC) has grown significantly, and researchers cannot access the vast amount of literature. This study aimed to explore the research progress in studying HCC over the past 30 years using a machine learning-based bibliometric analysis and to suggest future research directions.MethodsComprehensive research was conducted between 1991 and 2020 in the public version of the PubMed database using the MeSH term “hepatocellular carcinoma.” The complete records of the collected results were downloaded in Extensible Markup Language format, and the metadata of each publication, such as the publication year, the type of research, the corresponding author’s country, the title, the abstract, and the MeSH terms, were analyzed. We adopted a latent Dirichlet allocation topic modeling method on the Python platform to analyze the research topics of the scientific publications.ResultsIn the last 30 years, there has been significant and constant growth in the annual publications about HCC (annual percentage growth rate: 7.34%). Overall, 62,856 articles related to HCC from the past 30 years were searched and finally included in this study. Among the diagnosis-related terms, “Liver Cirrhosis” was the most studied. However, in the 2010s, “Biomarkers, Tumor” began to outpace “Liver Cirrhosis.” Regarding the treatment-related MeSH terms, “Hepatectomy” was the most studied; however, recent studies related to “Antineoplastic Agents” showed a tendency to supersede hepatectomy. Regarding basic research, the study of “Cell Lines, Tumors,’’ appeared after 2000 and has been the most studied among these terms.ConclusionThis was the first machine learning-based bibliometric study to analyze more than 60,000 publications about HCC over the past 30 years. Despite significant efforts in analyzing the literature on basic research, its connection with the clinical field is still lacking. Therefore, more efforts are needed to convert and apply basic research results to clinical treatment. Additionally, it was found that microRNAs have potential as diagnostic and therapeutic targets for HCC.

show abstract

Section: Discussionmentioning

confidence: 99%

A systematic review of progress on hepatocellular carcinoma research over the past 30 years: a machine-learning-based bibliometric analysis

Lee,

Hwang,

Sohn

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…10 A previous meta-analysis revealed that GBCA-enhanced MRI demonstrated higher sensitivity and accuracy than contrast-enhanced CT in detecting hepatocellular carcinoma (HCC). 11 Moreover, GBCA-enhanced MRI demonstrated similar sensitivity to contrast-enhanced CT when detecting liver metastases from cancers of the gastrointestinal tract. 12 SPIO is a liver-specific contrast agent that is taken up by Kupffer cells and was introduced in Japan in 1998.…”

Section: Detection Of Focal Liver Lesionsmentioning

confidence: 95%

Clinical Significance of Liver MR Imaging

Ichikawa

Goshima

2023

magn reson med sci

View full text Add to dashboard Cite

MRI is widely used in clinical practice for detecting liver diseases. Since the introduction of gadoxetic acid, MRI has become the most effective modality for the detection and characterization of focal liver lesions. According to previous meta-analyses, the area under the receiver operating characteristic curve (AUROC) was 0.97–0.99 for the diagnosis of small hepatocellular carcinoma (≥ 2 cm) by gadoxetic-acid-enhanced MRI. Moreover, the AUROC for the diagnosis of colorectal liver metastases was significantly high (0.98). Despite gadoxetic acid’s drawbacks, its clinical utility outweighs them, making it the contrast agent of choice in routine liver MRIs. Moreover, clinically, liver MRI has become more prevalent for a quantitative assessment. Liver fibrosis can be evaluated using MR elastography; whereas, hepatic steatosis and iron overload can be evaluated using proton density fat fraction, with high accuracy and reproducibility. This article reviewed the usefulness of liver MRI, which can be a comprehensive imaging modality in clinical practice.

show abstract

“…However, it has been shown that the sensitivity and specificity of both multiphasic CT-scan and MR is not optimal, and can be affected by many patients-, lesion-, technical-, and operator-related factors. For example, a meta-analysis by Chen et al (23) showed that MR and CT-scan sensitivity is respectively 77% and 63% and that their specificity is 93% and 94% in patients at risk for HCC, but that sensitivity decreases to 69% and 60% when considering ≤ 20mm diameter liver lesions only.…”

Section: Imaging Modelsmentioning

confidence: 99%

Applications of Artificial Intelligence in liver cancer: a scoping review – part I, screening and diagnosis. (Preprint)

Chierici,

Lareyre,

Iannelli

et al. 2024

Preprint

View full text Add to dashboard Cite

BACKGROUND Artificial intelligence (AI) has emerged as a powerful tool in various fields, including medicine, offering the potential to revolutionize the way diseases are diagnosed and treated. OBJECTIVE This scoping review explores the applications of AI in primary liver cancer, focusing on screening and diagnosis. Liver cancer, particularly hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), presents significant challenges due to late-stage diagnoses and limited treatment options. METHODS A systematic review was performed on PubMed, Embase, Scopus, and Web of Science databases including research published between January the 1st 2020 and September the 30th 2023. RESULTS AI-driven models have been developed to enhance screening efforts, utilizing machine learning (ML) algorithms trained on clinical, biochemical, and radiological data to identify high-risk patients. These models demonstrate promising results in early HCC detection, especially in populations with chronic hepatitis B virus (HBV) infection or metabolic dysfunction-associated fatty liver disease (MAFLD). Additionally, AI applications in liver imaging, utilizing deep learning (DL) algorithms such as convolutional neural networks (CNN), have shown remarkable accuracy in segmenting and classifying liver lesions on CT and MR images. However, challenges remain in model validation, standardization, and reproducibility, with many studies lacking external validation and consistency in reporting performance metrics. Furthermore, the transition from model development to real-world implementation poses significant hurdles, highlighting the need for a more rigorous and transparent approach in AI model development. CONCLUSIONS AI-driven models hold immense potential to improve early detection and diagnosis of primary liver cancer, ultimately leading to better patient outcomes. Further research and collaboration are warranted to address the current limitations and facilitate the integration of AI into clinical practice for liver cancer management. CLINICALTRIAL

show abstract

The diagnostic performance of contrast-enhanced CT versus extracellular contrast agent-enhanced MRI in detecting hepatocellular carcinoma: direct comparison and a meta-analysis

Cited by 7 publications

References 42 publications

A systematic review of progress on hepatocellular carcinoma research over the past 30 years: a machine-learning-based bibliometric analysis

A systematic review of progress on hepatocellular carcinoma research over the past 30 years: a machine-learning-based bibliometric analysis

Clinical Significance of Liver MR Imaging

Applications of Artificial Intelligence in liver cancer: a scoping review – part I, screening and diagnosis. (Preprint)

Contact Info

Product

Resources

About