ReviewFrontiers in artificial intelligence2020
Integration of AI and Machine Learning in Radiotherapy QA.
Review in Frontiers in artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 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
47 citing papers in PubMed.
- Prediction of patient-specific quality assurance for volumetric modulated arc therapy using machine log files.Quantitative imaging in medicine and surgery · 2026Article
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of CADRA.Journal of applied clinical medical physics · 2026Review
- Review
- Improving patient treatment accuracy using transit dosimetry with Electronic Portal Imaging Device images and deep learning.Physics and imaging in radiation oncology · 2026Article
- A feasibility study on a machine-learning-based quality assurance tool for spot-scanning proton therapy using delivery log files and treatment plans.Physics and imaging in radiation oncology · 2026Article
- Research on real-time detection of radiotherapy setup errors and intelligent quality control methods based on artificial intelligence and big data.Frontiers in oncology · 2026Article
- Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions.Frontiers in immunology · 2026Review
- Trustworthy artificial intelligence in radiation oncology: cross-industry lessons for development, validation, and deployment.Frontiers in oncology · 2026Review
- AI in Radiation Oncology: A Comprehensive Review of Current Applications and Future Directions.Cureus · 2025Review
- A Time-Series Approach for Machine Learning-Based Patient-Specific Quality Assurance of Radiosurgery Plans.Bioengineering (Basel, Switzerland) · 2025Article
- New Approaches in Radiotherapy.Cancers · 2025Review
- Detection of the failed-tolerance causes of electronic-portal-imaging-device-basedPhysics and imaging in radiation oncology · 2025Article
- Fully automatic reconstruction of prostate high-dose-rate brachytherapy interstitial needles using two-phase deep learning-based segmentation and object tracking algorithms.Clinical and translational radiation oncology · 2025Article
- The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma.Frontiers in medicine · 2025Review
- Innovative patient-specific delivered-dose prediction for volumetric modulated arc therapy using lightweight Swin-Transformer.Frontiers in oncology · 2025Article
- Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy for Non-Small Cell Lung Cancer Using Varian Ethos Therapy System.Current oncology (Toronto, Ont.) · 2024Article
- Technical note: Visual, rapid, scintillation point dosimetry for in vivo MV photon beam radiotherapy treatments.Medical physics · 2024Article
- Applications of artificial intelligence for machine- and patient-specific quality assurance in radiation therapy: current status and future directions.Journal of radiation research · 2024Review
- Adaptive Radiotherapy: Next-Generation Radiotherapy.Cancers · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The use of machine learning and other sophisticated models to aid in prediction and decision making has become widely popular across a breadth of disciplines. Within the greater diagnostic radiology, radiation oncology, and medical physics communities promising work is being performed in tissue classification and cancer staging, outcome prediction, automated segmentation, treatment planning, and quality assurance as well as other areas. In this article, machine learning approaches are explored, highlighting specific applications in machine and patient-specific quality assurance (QA). Machine learning can analyze multiple elements of a delivery system on its performance over time including the multileaf collimator (MLC), imaging system, mechanical and dosimetric parameters. Virtual Intensity-Modulated Radiation Therapy (IMRT) QA can predict passing rates using different measurement techniques, different treatment planning systems, and different treatment delivery machines across multiple institutions. Prediction of QA passing rates and other metrics can have profound implications on the current IMRT process. Here we cover general concepts of machine learning in dosimetry and various methods used in virtual IMRT QA, as well as their clinical applications.
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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.