ReviewSeminars in radiation oncology2022
Advances in Automated Treatment Planning.
Review in Seminars in radiation oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Automated planning architectures for online adaptive radiotherapy: A narrative review of the ethos and unity/Monaco platforms.Journal of applied clinical medical physics · 2026Review
- Efficient deep learning-driven workflows for accelerated single-fraction radiation therapy planning of a solitary tumour in the lung.Physics and imaging in radiation oncology · 2026Article
- Parallel/Opposed: Automated treatment planning systems should be designed to minimize human intervention.Journal of applied clinical medical physics · 2026Article
- Feasibility of longitudinal relaxation rate mapping with non-Cartesian sampling and compressed sensing on a 1.5 T magnetic resonance linear accelerator.Physics and imaging in radiation oncology · 2026Article
- A deep learning-driven automated treatment planning framework for cervical cancer patients treated with volumetric modulated arc therapy.Radiation oncology (London, England) · 2026Article
- Automated fluence optimization in breast cancer radiotherapy: balancing dosimetric quality, organ-at-risk sparing, and workflow efficiency.Radiation oncology (London, England) · 2026Article
- Article
- Multicenter study on the versatility and adoption of AI-driven automated radiotherapy planning across cancer types.Nature communications · 2025Article
- Article
- AI in Radiation Oncology: A Comprehensive Review of Current Applications and Future Directions.Cureus · 2025Review
- NRG Oncology Assessment of Artificial Intelligence for Automatic Treatment Planning in Radiation Therapy Clinical Trials: Present and Future.International journal of radiation oncology, biology, physics · 2025Article
- A comprehensive multifaceted technical evaluation framework for implementation of auto-segmentation models in radiotherapy.Communications medicine · 2025Article
- Implementing Remote Radiotherapy Planning to Increase Patient Flow at a Johannesburg Academic Hospital, South Africa: Protocol for a Prospective Feasibility Study.JMIR research protocols · 2025Article
- Compressed radiotherapy treatment planning: A new paradigm for rapid and high-quality treatment planning optimization.Medical physics · 2025Article
- Feasibility and impact of knowledge-based automated radiotherapy treatment planning in low- and middle-income countries.Ecancermedicalscience · 2025Article
- The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma.Frontiers in medicine · 2025Review
- Article
- Pencil beam scanning proton lattice radiotherapy: single-field versus multi-field optimization.Frontiers in oncology · 2025Article
- Remote radiotherapy treatment planning system: An efficiency tool for increasing patient flow in cancer treatment in South Africa.Annals of medicine and surgery (2012) · 2024Article
- Development and validation of an automated Tomotherapy planning method for cervical cancer.Radiation oncology (London, England) · 2024Article
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
Abstract
Treatment planning in radiation therapy has progressed enormously over the past several decades. Such advancements came in the form of innovative hardware and algorithms, giving rise to modalities such as intensity-modulated radiation therapy and volume modulated arc therapy, greatly improving patient outcome and quality of life. While these developments have improved the overall plan quality, they have also given rise to higher treatment planning complexity. This has resulted in increased treatment planning time and higher variability in the final approved plan quality. Radiation oncology, as an already technologically advanced field, has much research and implementation involving the use of AI. The field has begun to show the efficacy of using such technologies in many of its sub-areas, such as in diagnosis, imaging, segmentation, treatment planning, quality assurance, treatment delivery, and follow-up. Some AI technologies have already been clinically implemented by commercial systems. In this article, we will provide an overview to methods involved with treatment planning in radiation therapy. In particular, we will review the recent research and literature related to automation of the treatment planning process, leading to potentially higher efficiency and higher quality plans. We will then present the current and future challenges, as well as some future perspectives.
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.