ArticleFrontiers in oncology2024
Advances in ovarian cancer radiomics: a bibliometric analysis from 2010 to 2024.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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Who cites it
4 citing papers in PubMed.
- A bibliometric analysis of artificial intelligence in ovarian cancer research from 2006 to 2025.Discover oncology · 2026Article
- Longitudinal deep learning models for tracking disease progression in ovarian cancer using PET/CT imaging and clinical reports.Physical and engineering sciences in medicine · 2026Article
- Development and Validation of a Site-Specific Tumor Burden Score for Predicting Surgical Outcomes in Advanced Ovarian Cancer.Cancers · 2025Article
- Quantitative and qualitative metrics of tumor stroma in predicting ovarian cancer outcomes and expansion of its study with AI-based tools.Molecular therapy. Oncology · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Ovarian cancer, a leading cause of death among gynecological malignancies, often eludes early detection, leading to diagnoses at advanced stages. The objective of this bibliometric analysis is to map the landscape of ovarian cancer radiomics research from 2010 to 2024, emphasizing its growth, global contributions, and the impact of emerging technologies on early diagnosis and treatment strategies. Methods: A comprehensive search was conducted using the Web of Science Core Collection (WoSCC), focusing on publications related to radiomics and ovarian cancer within the specified period. Analytical tools such as VOSviewer and CiteSpace were employed to visualize trends, collaborations, and key contributions, while the R programming environment offered further statistical insights. Results: From the initial dataset, 149 articles were selected, showing a significant increase in research output, especially in the years 2021-2023. The analysis revealed a dominant contribution from China, with significant inputs from England. Major institutional contributors included the University of Cambridge and GE Healthcare. 'Frontiers in Oncology' emerged as a crucial journal in the field, according to Bradford's Law. Keyword analysis highlighted the focus on advanced imaging techniques and machine learning. Conclusions: The steady growth in ovarian cancer radiomics research reflects its critical role in advancing diagnostic and prognostic methodologies, underscoring the potential of radiomics in the shift towards personalized medicine. Despite some methodological challenges, the field's dynamic evolution suggests a promising future for radiomics in enhancing the accuracy of ovarian cancer diagnosis and treatment, contributing to improved patient care and outcomes.
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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.