SynthesisFrontiers in oncology2026
Applying transformer-based deep learning models in image-driven cancer diagnosis: a comprehensive bibliometric analysis of global research trends.
Synthesis in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background/objectives: The transformer, introduced in 2017, has revolutionized several fields, including oncology. Its applications in cancer detection, diagnosis, prognosis prediction and treatment have shown significant potential. However, a comprehensive analysis of the global research trends and future directions in image-driven cancer diagnosis is lacking. Methods: We conducted a bibliometric analysis using CiteSpace and VOSviewer to explore the development of transformer applications in image-driven cancer diagnosis(TICD). A total of 2923 papers published between 2017 and 2026 were analyzed, with early access papers from 2026 included. We examined publication trends, international collaboration, and citation patterns to identify research hotspots and emerging directions. Results: The number of publications on transformer applications in image-driven cancer diagnosis has rapidly increased, with a notable surge beginning in 2022. China and the United States are the leading contributors to the field, with high levels of international collaboration. The primary research focuses on the application of transformer-based models for image classification, segmentation, and enhancement. Their development is moving toward lightweight design, interpretability, multimodal fusion, and low annotation dependence. However, while the volume of publications is increasing, the impact (measured by citation counts) varies across countries and institutions. Conclusions: The field of TICD is in a robust growth phase, attracting significant attention from global researchers, particularly from China and the United states. While international collaboration is prevalent, the field faces challenges regarding the generalizability and scalability of research findings. Future research should focus on translating these promising technologies into clinical practice, ensuring that they are adaptable and applicable in diverse oncology contexts.
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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.