ReviewTranslational cancer research2025
Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.
Review in Translational cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
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
3 citing papers in PubMed.
- Research trends and emerging themes of magnetic resonance imaging in endometrial cancer: a bibliometric analysis.Abdominal radiology (New York) · 2026Article
- Artificial Intelligence in Gynecologic Oncology: Current Applications, Clinical Challenges, and Future Perspectives.Cureus · 2026Review
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: Gynecological cancer is the most common cancer that affects women's quality of life and well-being. Artificial intelligence (AI) technology enables us to exploit high-dimensional imaging data for precision oncology. Tremendous progress has been made with AI radiomics in cancers such as lung and breast cancers. Herein, we performed a literature review on AI radiomics in the management of gynecological cancer. Methods: A search was performed in the databases of PubMed, Embase, and Web of Science for original articles written in English up to 10 September 2024, using the terms "gynecological cancer", "cervical cancer", "endometrial cancer", "ovarian cancer", AND "artificial intelligence", "AI", AND "radiomics". The included studies mainly focused on the current landscape of AI radiomics in the diagnosis, treatment, and prognosis of gynecological cancer. Key Content and Findings: A total of 128 studies were included, with 86 studies focusing on tumor diagnosis (n=23) and characterization (n=63), 15 on treatment response prediction, and 27 on recurrence and survival prediction. AI radiomics has shown potential value in tumor diagnosis and characterization [tumor staging, histological subtyping, lymph node metastasis (LNM), lymphovascular space invasion (LVSI), myometrial invasion (MI), and other molecular or clinicopathological factors], chemotherapy or chemoradiotherapy response evaluation, and prognosis (disease recurrence or metastasis, and survival) prediction. However, most included studies were single-center and retrospective. There was substantial heterogeneity in methodology and results reporting. Conclusions: AI radiomics has been increasingly adopted in the management of gynecological cancer. Further validation in large-scale datasets is needed before clinical translation.
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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 Socratic graph.