Machine learning in ovarian cancer: a bibliometric and visual analysis from 2004 to 2024

  • 0Department of Pharmacy, Affiliated Hospital of Guilin Medical University, Guilin, China.

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Summary

This summary is machine-generated.

Machine learning (ML) shows growing potential for ovarian cancer (OC) early diagnosis and treatment. This study analyzes global trends and hotspots in ML applications for OC research, guiding future directions.

Area Of Science

  • Oncology
  • Medical Informatics
  • Bioinformatics

Background

  • Ovarian cancer (OC) presents significant challenges due to poor prognosis and high mortality rates.
  • Early diagnosis, screening, and prognostic prediction are critical unmet needs in OC management.
  • Machine learning (ML) is emerging as a powerful tool for advancing tumor diagnosis and prediction.

Purpose Of The Study

  • To analyze global development trends and research hotspots in the application of ML for OC.
  • To provide a reference for future research directions in ML for OC.

Main Methods

  • Quantitative bibliometric analysis of publications from 2004-2024.
  • Data sourced from Web of Science Core Collection (WoSCC).
  • Analysis performed using VOSviewer, R software, and CiteSpace.

Main Results

  • A total of 777 articles were retrieved, showing continuous growth in publications over 20 years.
  • China leads in publications, with key journals including Gynecologic Oncology and Nature.
  • Research hotspots include ML for biomarker discovery, personalized treatment, tumor microenvironment analysis, imaging-based diagnosis, and risk stratification.

Conclusions

  • ML application in OC is in a developmental phase with significant future potential.
  • This study offers a systematic overview of research priorities and emerging developments for researchers and clinicians.
  • Understanding these trends can accelerate progress in ML-driven ovarian cancer research.

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