4 research outputs found
Gaps in hepatocellular carcinoma surveillance in a United States cohort of insured patients with cirrhosis
Surveillance for hepatocellular carcinoma (HCC) is known to be underutilized; however, neither the variation of surveillance adherence by cirrhosis etiology nor the patient-side economic burden of surveillance are well understood. To identify potential barriers to HCC surveillance, we assessed utilization patterns and costs among US patients with cirrhosis monitored in routine clinical practice. We conducted a retrospective study of insured adult patients with cirrhosis using national administrative claims data from January 2013 through June 2019. Time up-to-date with recommended surveillance, correlates of surveillance receipt, and surveillance-associated costs were assessed during a ≥ 6-month follow-up. Among 15,543 patients with cirrhosis (mean [SD] age 64.0 [11.1] years, 50.7% male), 45.8% and 58.7% had received any abdominal imaging at 6 and 12 months, respectively. Patients were up-to-date with recommended surveillance for only 31% of a median 1.3-year follow-up. Those with viral hepatitis were more likely to receive surveillance than those with other etiologies (hazard ratio [HR] 1.55, 95% CI 1.11–2.17, p = .010 for patients without a baseline gastroenterologist [GI] visit and 2.69, 95% CI 1.77–4.09, p  HCC surveillance was underutilized and was lowest among patients with nonviral etiologies and those who had not seen a gastroenterologist. Surveillance-related out-of-pocket expenses and lost productivity were substantial. The development of surveillance strategies that reduce patient burden, such as those using blood-based biomarkers, may help improve surveillance adherence and effectiveness.</p
Discovery of Core-Fucosylated Glycopeptides as Diagnostic Biomarkers for Early HCC in Patients with NASH Cirrhosis Using LC-HCD-PRM-MS/MS
Aberrant changes
in site-specific core fucosylation
(CF) of serum proteins contribute to cancer development and progression,
which enables them as potential diagnostic markers of tumors. An optimized
data-dependent acquisition (DDA) workflow involving isobaric tags
for relative and absolute quantitation-labeling and enrichment of
CF peptides by lens culinaris lectin was applied to identify CF of
serum proteins in a test set of patients with nonalcoholic steatohepatitis
(NASH)-related cirrhosis (N = 16) and hepatocellular
carcinoma (HCC, N = 17), respectively. A total of
624 CF peptides from 343 proteins, with 683 CF sites, were identified
in our DDA–mass spectrometry (MS) analysis. Subsequently, 19
candidate CF peptide markers were evaluated by a target parallel reaction-monitoring–MS
workflow in a validation set of 58 patients, including NASH-related
cirrhosis (N = 29), early-stage HCC (N = 21), and late-stage HCC (N = 8). Significant
changes (p < 0.01) were observed in four CF peptides
between cirrhosis and HCC, where peptide LGSFEGLVn160LTFIHLQHNR
from LUM in combination with AFP showed the best diagnostic performance
in discriminating HCC from cirrhosis, with an area under curve (AUC)
of 0.855 compared to AFP only (AUC = 0.717). This peptide in combination
with AFP also significantly improved diagnostic performance in distinguishing
early HCC from cirrhosis, with an AUC of 0.839 compared to AFP only
(AUC = 0.689). Validation of this novel promising biomarker panel
in larger cohorts should be performed
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