Progress and current trends in prediction models for the occurrence and prognosis of cancer and cancer-related complications: a bibliometric and visualization analysis
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Summary
This summary is machine-generated.Bibliometric analysis reveals a growing field of cancer prediction models, with machine learning showing rapid advancement. Future research may focus on systematic reviews to guide clinical model selection.
Area Of Science
- Oncology
- Bibliometrics
- Data Science
Background
- Prediction models are crucial in cancer research for estimating disease and outcome probabilities.
- The field of cancer-related prediction models has seen consistent annual growth.
Purpose Of The Study
- To identify research hotspots and future directions in cancer-related prediction models.
- Utilize bibliometric analysis to map the landscape of this research area.
Main Methods
- Conducted a comprehensive literature search in the Science Citation Index Expanded (SCIE).
- Performed co-occurrence analyses of countries, institutions, authors, journals, and keywords using VOSviewer.
- Utilized CiteSpace for keyword clustering, timeline visualization, and burst term analysis.
Main Results
- Analyzed 1,556 publications, showing increasing research output.
- China and the United States are leading contributors, with strong collaborations involving the US, England, and the Netherlands.
- Key terms include nomogram, survival, risk, prognosis, breast cancer, and machine learning, with machine learning clusters showing vigorous growth.
Conclusions
- Cancer-related prediction models, particularly prognostic models, are rapidly evolving.
- Emerging techniques like neural networks and deep learning are expected to be pivotal.
- Systematic reviews of predictive models may emerge as a future research direction to aid clinical application.
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