SynthesisFrontiers in oncology2025
Current status and trends of machine learning applied in clinical research of gastric cancer from 2004 to 2023: global bibliometric and visual analysis.
Synthesis in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial Intelligence in Gastric Cancer: Diagnostic, Prognostic, and Predictive Developments, Evidence Maturity, and Translational Challenges.Cancer reports (Hoboken, N.J.) · 2026Review
- Artificial intelligence in endoscopic prediction of submucosal invasion in gastric neoplasms: systematic review and meta-analysis.Clinics (Sao Paulo, Brazil) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Gastric cancer is a serious disease that threatens human life; early diagnosis and treatment have been the focus of many studies. With advancements in imaging evaluation and machine learning, early detection and treatment of gastric cancer have become feasible. This study aimed to explore research trends and hotspots in the field of gastric cancer and machine learning through bibliometric analysis and to provide new insights for related clinical applications. Methods: Literature on gastric cancer and machine learning published from 2004 to 2023 was retrieved from the Web of Science database. Microsoft Excel 2019 was used for statistical analysis of influential articles, journals, authors, organizations, countries (regions), and co-citation references in this research domain. VOSviewer (version 1.6.16) and CiteSpace (version 5.8.R3) were utilized to visualize the corresponding data. Results: We analyzed and evaluated 425 articles authored by 2,899 researchers from 825 organizations across 52 countries (regions). The People's Republic of China, the Chinese Academy of Sciences, and the University of the Chinese Academy of Sciences were identified as leaders in this field. The article "Genome-wide cell-free DNA fragmentation in patients with cancer," published in Conclusions: This study offers a comprehensive visual analysis of research on gastric cancer and machine learning, representing the most detailed bibliometric study in this domain. With the continuous advancement of research, artificial intelligence-assisted early diagnostic methods for gastric cancer and corresponding treatment strategies may emerge as a pivotal direction for future research in this area.
Indexed as
Identifiers
What OpenQuestion holds
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.