ReviewCancer control : journal of the Moffitt Cancer Center
The Evolving Landscape of Deep Learning in Breast Cancer Imaging: A Bibliometric Study of Segmentation, Detection, and Diagnostic Research From 2006 to 2025.
Review in Cancer control : journal of the Moffitt Cancer Center. 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
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
IntroductionDeep learning has rapidly reshaped breast cancer imaging, but the evolution of segmentation, detection, and diagnostic research remains insufficiently characterized. This bibliometric review mapped global trends, collaboration patterns, thematic evolution, and emerging frontiers from 2006 to 2025.MethodsThis bibliometric study retrieved publications on deep learning in breast cancer imaging from the Web of Science Core Collection and Scopus. English-language articles and reviews published between 2006 and 2025 were included. After deduplication, Bibliometrix, VOSviewer, and CiteSpace analyzed publication trends, country contributions, collaboration patterns, keyword co-occurrence, temporal topic evolution, and citation bursts.ResultsA total of 3,568 publications were included. Annual output remained limited before 2016 but increased markedly thereafter, with especially rapid growth after 2020, indicating the transition of this field from an exploratory stage to accelerated development. China ranked first in corresponding-author publications, whereas the USA and the United Kingdom showed stronger citation impact, reflecting differences between publication scale and academic influence. Keyword analysis showed that the field was primarily structured around deep learning, breast cancer, mammography, segmentation, detection, and computer-aided diagnosis. Temporal analyses further indicated a shift from early computer-aided diagnosis frameworks and conventional neural-network approaches toward more advanced and clinically relevant themes, including explainable artificial intelligence, nomogram, self-attention, transformers, neoadjuvant therapy, and axillary lymph node metastasis.ConclusionDeep learning in breast cancer imaging has evolved into a rapidly expanding and increasingly sophisticated field centered on segmentation, detection, and clinically relevant diagnostic research. Future progress will depend on improved interpretability, robust validation, and stronger clinical integration.
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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.