ReviewFrontiers in oncology2020
A Review on Application of Deep Learning Algorithms in External Beam Radiotherapy Automated Treatment Planning.
Review in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 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.
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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.
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Who cites it
71 citing papers in PubMed.
- The illusory correlation between geometrical similarity and physician satisfaction in heart auto-segmentation revealed by a physician-blind test.Journal of applied clinical medical physics · 2026Article
- Deep Reinforcement Learning-Based Automated Treatment Planning for a Rotating Gamma System.Cureus · 2026Article
- Noisy probing dose facilitated dose prediction for pencil beam scanning proton therapy: Physics enhances generalizability.Medical physics · 2026Article
- A deep learning-driven automated treatment planning framework for cervical cancer patients treated with volumetric modulated arc therapy.Radiation oncology (London, England) · 2026Article
- Prescription‑dose stratification improves deep learning‑based VMAT dose prediction in locally advanced NSCLC.Scientific reports · 2026Article
- Deep learning [Clinical and translational radiation oncology · 2026Article
- Empowering photodynamic therapy with artificial intelligence: current trends and future directions.Frontiers in oncology · 2026Review
- Signal-aware deep learning-based respiratory motion prediction for lung tumor management.Frontiers in oncology · 2026Article
- Ovarian dose prediction after ovarian transposition in cervical cancer: comparing three models and the impact of geometric distance on predictive performance.Frontiers in oncology · 2026Article
- Fully automated intensity-modulated radiotherapy plans for rectal cancer based on deep learning predictions of three-dimensional dose distributions.Quantitative imaging in medicine and surgery · 2025Article
- Development of automated whole breast 3D planning with tangential fields.Journal of applied clinical medical physics · 2025Article
- 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
- Opportunities for Artificial Intelligence in Oncology: From the Lens of Clinicians and Patients.JCO oncology practice · 2025Review
- Deep learning-based applicator selection between Syed and T&O in high-dose-rate brachytherapy for locally advanced cervical cancer: a retrospective study.Physics in medicine and biology · 2025Article
- Hallmarks of artificial intelligence contributions to precision oncology.Nature cancer · 2025Review
- MR-linac: role of artificial intelligence and automation.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Attention 3D UNET for dose distribution prediction of high-dose-rate brachytherapy of cervical cancer: Intracavitary applicators.Journal of applied clinical medical physics · 2025Article
- A new deep learning model for predicting IMRT dose distributions for lung cancer with dose masks.Frontiers in oncology · 2025Article
- Replacing manual planning with automatic iterative planning for locally advanced rectal cancer VMAT treatment.Journal of applied clinical medical physics · 2025Article
- Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features.Radiation oncology (London, England) · 2024Article
11 more citing papers are in PubMed but not listed here.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Treatment planning plays an important role in the process of radiotherapy (RT). The quality of the treatment plan directly and significantly affects patient treatment outcomes. In the past decades, technological advances in computer and software have promoted the development of RT treatment planning systems with sophisticated dose calculation and optimization algorithms. Treatment planners now have greater flexibility in designing highly complex RT treatment plans in order to mitigate the damage to healthy tissues better while maximizing radiation dose to tumor targets. Nevertheless, treatment planning is still largely a time-inefficient and labor-intensive process in current clinical practice. Artificial intelligence, including machine learning (ML) and deep learning (DL), has been recently used to automate RT treatment planning and has gained enormous attention in the RT community due to its great promises in improving treatment planning quality and efficiency. In this article, we reviewed the historical advancement, strengths, and weaknesses of various DL-based automated RT treatment planning techniques. We have also discussed the challenges, issues, and potential research directions of DL-based automated RT treatment planning techniques.
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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 OpenQuestion graph.