Images from a 64-year-old male patient who suffered from intermittent claudication for 8 years and rest pain for 1 month.

<p>Consistent with CE-MRA and DSA, FSD-MRA revealed an occlusive lesion on the right anterior tibial artery (white arrow) and right posterior tibial artery (white arrowheads) with collateral artery formation.</p

Images from a 37-year-old male patient who had been suffering from intermittent claudication for 2 weeks.

<p>Consistent with CE-MRA and DSA, FSD-MRA demonstrated an occlusive lesion on the right tibiofibular trunk (white arrow). Due to inhomogeneous background suppression, the distal right posterior tibial artery and right fibular artery are not shown clearly in the FSD-MRA image.</p

Images from an 81-year-old female patient with a 20-year history of diabetes suffered intermittent claudication for 1 year without rest pain.

<p>Consistent with CE-MRA and DSA, FSD-MRA revealed an occlusive lesion on the left posterior tibial artery (white arrowheads) and multiple significant stenosis lesions on the left tibiofibular trunk and left fibular artery. Venous contamination mimicked the left anterior tibial artery in FSD-MRA and CE-MRA, however an occlusion was apparent at that location in DSA (white arrow).</p

Comparison of image quality ratings and inter-observer agreement (Kappa).

<p>Comparison of image quality ratings and inter-observer agreement (Kappa).</p

Patient characteristics.

<p>Patient characteristics.</p

Diagnostic accuracy of FSD-MRA, CE-MRA, and CE+FSD-MRA for detection of clinically significant stenosis, relative to DSA, with a stenosis degree cut-off value of 2.

<p>Diagnostic accuracy of FSD-MRA, CE-MRA, and CE+FSD-MRA for detection of clinically significant stenosis, relative to DSA, with a stenosis degree cut-off value of 2.</p

Diagnostic accuracy (95%CI) of FSD-MRA, relative to CE-MRA, for detection of clinically significant (≥50%) stenosis.

<p>Diagnostic accuracy (95%CI) of FSD-MRA, relative to CE-MRA, for detection of clinically significant (≥50%) stenosis.</p

ROC curves of stenosis degree detection in FSD-MRA, CE-MRA, and CE+FSD-MRA with DSA as the standard reference.

<p>The area under the curve was largest for CE+FSD MRA (yellow, 0.929, <i>p</i> < 0.01), and was slightly larger for CE-MRA (blue, 0.915, <i>p</i> < 0.01) than for FSD-MRA (green, 0.903, <i>p</i> < 0.01).</p

Table_1_Artificial intelligence in liver cancer research: a scientometrics analysis of trends and topics.docx

Background and aimsWith the rapid growth of artificial intelligence (AI) applications in various fields, understanding its impact on liver cancer research is paramount. This scientometrics project aims to investigate publication trends and topics in AI-related publications in liver cancer.Materials and MethodsWe employed a search strategy to identify AI-related publications in liver cancer using Scopus database. We analyzed the number of publications, author affiliations, and journals that publish AI-related publications in liver cancer. Finally, the publications were grouped based on intended application.ResultsWe identified 3950 eligible publications (2695 articles, 366 reviews, and 889 other document types) from 1968 to August 3, 2023. There was a 12.7-fold increase in AI-related publications from 2013 to 2022. By comparison, the number of total publications on liver cancer increased by 1.7-fold. Our analysis revealed a significant shift in trends of AI-related publications on liver cancer in 2019. We also found a statistically significant consistent increase in numbers of AI-related publications over time (tau = 0.756, p ConclusionOur study reveals increasing interest in AI for liver cancer research, evidenced by a 12.7-fold growth in related publications over the past decade. A common application of AI is in medical imaging analysis for various purposes. China, the US, and Germany are leading contributors.</p

Image_1_Artificial intelligence in liver cancer research: a scientometrics analysis of trends and topics.pdf

Background and aimsWith the rapid growth of artificial intelligence (AI) applications in various fields, understanding its impact on liver cancer research is paramount. This scientometrics project aims to investigate publication trends and topics in AI-related publications in liver cancer.Materials and MethodsWe employed a search strategy to identify AI-related publications in liver cancer using Scopus database. We analyzed the number of publications, author affiliations, and journals that publish AI-related publications in liver cancer. Finally, the publications were grouped based on intended application.ResultsWe identified 3950 eligible publications (2695 articles, 366 reviews, and 889 other document types) from 1968 to August 3, 2023. There was a 12.7-fold increase in AI-related publications from 2013 to 2022. By comparison, the number of total publications on liver cancer increased by 1.7-fold. Our analysis revealed a significant shift in trends of AI-related publications on liver cancer in 2019. We also found a statistically significant consistent increase in numbers of AI-related publications over time (tau = 0.756, p ConclusionOur study reveals increasing interest in AI for liver cancer research, evidenced by a 12.7-fold growth in related publications over the past decade. A common application of AI is in medical imaging analysis for various purposes. China, the US, and Germany are leading contributors.</p