ArticleDiscover oncology2025
Machine learning in ovarian cancer: a bibliometric and visual analysis from 2004 to 2024.
Article in Discover oncology, 2025. 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
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
4 citing papers in PubMed.
- A bibliometric analysis of artificial intelligence in ovarian cancer research from 2006 to 2025.Discover oncology · 2026Article
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- Multimodal AI in high-grade serous ovarian cancer: integrated prediction and clinical decision-making.Frontiers in oncology · 2026Review
- Machine Learning Models for Predicting Gynecological Cancers: Advances, Challenges, and Future Directions.Cancers · 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
No grant is acknowledged in the PubMed record.
Abstract
objectiveOvarian cancer (OC) is a common malignant tumor in women, with poor prognosis and high mortality rates. Early diagnosis, screening, and prognostic prediction of OC have long been focal points and challenges in this field. In recent years, machine learning (ML) has gradually demonstrated its unique advantages in the early diagnosis, screening, and prognostic prediction of tumors, including OC.This study aims to analyze global development trends and research hotspots in the application of ML for OC, thereby providing a reference for future research directions.
methodsWe searched the Web of Science Core Collection (WoSCC) for all publications related to OC and ML from 2004 to 2024, conducting a quantitative analysis using VOSviewer, R software, and CiteSpace.
resultsA total of 777 articles were retrieved.The number of publications related to ML and OC has grown continuously over the past 20 years.China led with 254 articles.The most prominent journals include Gynecologic Oncology, Nature, Clinical Cancer Research, Cancer Research, and Journal of Clinical Oncology.Research hotspots are: (a) ML-driven OC biomarker discovery and personalized treatment; (b) ML in tumor microenvironment analysis and resistance prediction; (c) ML in imaging-based diagnosis and risk stratification; (d) ML in multicenter OC studies.
conclusionML in OC is currently in a developmental phase and shows promising potential for the future. This study provides researchers and clinicians with a more systematic understanding of research priorities and forthcoming developments in this area.
Indexed as
Identifiers
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.