ArticleBreast (Edinburgh, Scotland)2020
Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer.
Article in Breast (Edinburgh, Scotland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 44 citations in OpenAlex.
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Automatic segmentation of clinical target volume for radiation therapy in breast-conserving patients and exploration of clinical factors influential to its performance.The British journal of radiology · 2026Article
- BreastWatch: A Varian Eclipse script tool for Community-Based automatic evaluation of breast treatment plans.Journal of applied clinical medical physics · 2025Article
- Development and external multicentric validation of a deep learning-based clinical target volume segmentation model for whole-breast radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Male Breast Cancer: Epidemiology, Diagnosis, Molecular Mechanisms, Therapeutics, and Future Prospective.Oncology research · 2025Review
- Review
- Artificial Intelligence-Based Autosegmentation: Advantages in Delineation, Absorbed Dose-Distribution, and Logistics.Advances in radiation oncology · 2024Article
- Synthesizing Efficiency Tools in Radiotherapy to Increase Patient Flow: A Comprehensive Literature Review.Clinical Medicine Insights. Oncology · 2024Review
- Comparison of the output of a deep learning segmentation model for locoregional breast cancer radiotherapy trained on 2 different datasets.Technical innovations & patient support in radiation oncology · 2023Article
- Implementation of automated personalised breast radiotherapy planning techniques with scripting in Raystation.The British journal of radiology · 2023Article
- Adaptive radiotherapy for breast cancer.Clinical and translational radiation oncology · 2023Review
- Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy.Nature communications · 2022Article
- Radiation-Induced Toxicity Risks in Photon Versus Proton Therapy for Synchronous Bilateral Breast Cancer.International journal of particle therapy · 2022Article
- Applications and challenges of artificial intelligence in diagnostic and interventional radiology.Polish journal of radiology · 2022Review
- Artificial intelligence (AI) in breast cancer care - Leveraging multidisciplinary skills to improve care.Breast (Edinburgh, Scotland) · 2021Article
- Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery.Radiation oncology (London, England) · 2021Article
- Clinical Validation of Siemens' Syngo.via Automatic Contouring System.Advances in radiation oncologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence demonstrated its value for automated contouring of organs at risk and target volumes as well as for auto-planning of radiation dose distributions in terms of saving time, increasing consistency, and improving dose-volumes parameters. Future developments include incorporating dose/outcome data to optimise dose distributions with optimal coverage of the high-risk areas, while at the same time limiting doses to low-risk areas. An infinite gradient of volumes and doses to deliver spatially-adjusted radiation can be generated, allowing to avoid unnecessary radiation to organs at risk. Therefore, data about patient-, tumour-, and treatment-related factors have to be combined with dose distributions and outcome-containing databases.
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.