ArticleBreast cancer research : BCR2025
Decoding breast cancer imaging trends: the role of AI and radiomics through bibliometric insights.
Article in Breast cancer research : BCR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deciphering the biological underpinnings behind prognostic MRI-based imaging signatures in breast cancer: a systematic review.Journal of translational medicine · 2025Pooled it
- Mapping the research landscape of photoacoustic imaging in breast cancer: a text-based bibliometric analysis.Gland surgery · 2026Article
- Bibliometric analysis reveals the current status and future trends of cancer associated photodynamic therapy in breast cancer.Discover oncology · 2026Article
- Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.Breast cancer (Dove Medical Press) · 2026Review
- Article
- Precision Oncology Through Dialogue: AI-HOPE-RTK-RAS Integrates Clinical and Genomic Insights into RTK-RAS Alterations in Colorectal Cancer.Biomedicines · 2025Article
- Early and hereditary breast cancer: advances in risk stratification and imaging approaches.Therapeutic advances in medical oncology · 2025Review
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
Abstract
backgroundRadiomics and AI have been widely used in breast cancer imaging, but a comprehensive systematic analysis is lacking. Therefore, this study aims to conduct a bibliometrics analysis in this field to discuss its research status and frontier hotspots and provide a reference for subsequent research.
methodsPublications related to AI, radiomics, and breast cancer imaging were searched in the Web of Science Core Collection. CiteSpace plotted the relevant co-occurrence network according to authors and keywords. VOSviewer and Pajek were used to draw relevant co-occurrence maps according to country and institution. In addition, R was used to conduct bibliometric analysis of relevant authors, countries/regions, journals, keywords, and annual publications and citations based on the collected information.
resultsA total of 2,701 Web of Science Core Collection publications were retrieved, including 2,486 articles (92.04%) and 215 reviews (7.96%). The number of publications increased rapidly after 2018. The United States of America (n = 17,762) leads in citations, while China (n = 902) leads in the number of publications. Sun Yat-sen University (n = 75) had the largest number of publications. Bin Zheng (n = 28) was the most published author. Nico Karssemeijer (n = 72.1429) was the author with the highest average citations. "Frontiers in Oncology" was the journal with the most publications, and "Radiology" had the highest IF. The keywords with the most frequent occurrence were "breast cancer", "deep learning", and "classification". The topic trends in recent years were "explainable AI", "neoadjuvant chemotherapy", and "lymphovascular invasion".
conclusionThe application of radiomics and AI in breast cancer imaging has received extensive attention. Future research hotspots may mainly focus on the progress of explainable AI in the technical field and the prediction of lymphovascular invasion and neoadjuvant chemotherapy efficacy in clinical application.
Indexed as
Identifiers
What Socratic 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 Socratic graph.