ReviewFrontiers in oncology2025
Artificial intelligence in high-dose-rate brachytherapy treatment planning for cervical cancer: a review.
Review in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- A bibliometric analysis of global research in cervical cancer radiotherapy (2015-2024): trends, hotspots, and future directions.Translational cancer research · 2026Article
- Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18 Malignancies.Cancers · 2026Review
- Trends in brachytherapy utilization in Canada from 2011 to 2020.Physics and imaging in radiation oncology · 2026Article
- Artificial Intelligence in Organoid-Based Disease Modeling: A New Frontier in Precision Medicine.Biomimetics (Basel, Switzerland) · 2025Review
- Dosimetry evaluation and uncertainty analysis of Cobalt-60 HDR brachytherapy for cervical cancer in resource-limited settings.Journal of cancer research and clinical oncology · 2025Article
- The Role of Brachytherapy in the Management of Oral Squamous Cell Carcinoma: A Systematic Review.Journal of clinical medicine · 2025Review
- Article
- MDA-TransUNet: A Deep Learning-Based Automatic Segmentation Method for Cervical Cancer Brachytherapy.Technology in cancer research & treatmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cervical cancer remains a significant global health concern, characterized by high morbidity and mortality rates. High-dose-rate brachytherapy (HDR-BT) is a critical component of cervical cancer treatment, requiring precise and efficient treatment planning. However, the process is labor-intensive, heavily reliant on operator expertise, and prone to variability due to factors such as applicator shifts and organ filling changes. Recent advancements in artificial intelligence (AI), particularly in medical image processing, offer significant potential for automating and standardizing treatment planning in HDR-BT. This review examines the progress and challenge of AI applications in HDR-BT treatment planning, focusing on automatic segmentation, applicator reconstruction, dose calculation, and plan optimization. By addressing current limitations and exploring future directions, this paper aims to guide the integration of AI into clinical practice, ultimately improving treatment accuracy, reducing preparation time, and enhancing patient outcomes.
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.