ReviewJournal of applied clinical medical physics2021
Applications of machine and deep learning to patient-specific IMRT/VMAT quality assurance.
Review in Journal of applied clinical medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 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
- Explainable machine learning for patient-specific quality assurance in intensity-modulated radiotherapy based on anatomical structures.Journal of applied clinical medical physics · 2026Article
- A multimodal multi-scale transformer for virtual pretreatment patient-specific QA of SBRT using portal-dosimetry fluence maps.Scientific reports · 2026Article
- Machine Learning-Based Prediction of Elekta MLC Motion with Dosimetric Validation for Virtual Patient-Specific QA.Bioengineering (Basel, Switzerland) · 2025Article
- A Time-Series Approach for Machine Learning-Based Patient-Specific Quality Assurance of Radiosurgery Plans.Bioengineering (Basel, Switzerland) · 2025Article
- Detection of the failed-tolerance causes of electronic-portal-imaging-device-basedPhysics and imaging in radiation oncology · 2025Article
- MR-linac: role of artificial intelligence and automation.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma.Frontiers in medicine · 2025Review
- Reduction of patient specific quality assurance through plan complexity metrics for VMAT plans with an open-source TPS script.Zeitschrift fur medizinische Physik · 2024Article
- Error detection for radiotherapy planning validation based on deep learning networks.Journal of applied clinical medical physics · 2024Article
- Improving the performance of deep learning models in predicting and classifying gamma passing rates with discriminative features and a class balancing technique: a retrospective cohort study.Radiation oncology (London, England) · 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
- Machine learning and lean six sigma for targeted patient-specific quality assurance of volumetric modulated arc therapy plans.Physics and imaging in radiation oncology · 2024Article
- Detecting outliers beyond tolerance limits derived from statistical process control in patient-specific quality assurance.Journal of applied clinical medical physics · 2024Article
- Prediction of patient-specific quality assurance for volumetric modulated arc therapy using radiomics-based machine learning with dose distribution.Journal of applied clinical medical physics · 2024Article
- Deep learning-based detection and classification of multi-leaf collimator modeling errors in volumetric modulated radiation therapy.Journal of applied clinical medical physics · 2023Article
- Prediction models as decision-support tools for virtual patient-specific quality assurance of helical tomotherapy plans.Physics and imaging in radiation oncology · 2023Article
- Machine Learning Based Prediction of Gamma Passing Rate for VMAT Radiotherapy Plans.Journal of personalized medicine · 2022Article
- Clinical Application of Early Warning Scoring Based on BiLSTM-Attention in Emergency Obstetric Preexamination and Triage.Journal of healthcare engineering · 2022Article
- Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
In order to deliver accurate and safe treatment to cancer patients in radiation therapy using advanced techniques such as intensity modulated radiation therapy (IMRT) and volumetric-arc radiation therapy (VMAT), patient specific quality assurance (QA) should be performed before treatment. IMRT/VMAT dose measurements in a phantom using various devices have been clinically adopted as standard method for QA. This approach allows the verification of the accuracy of the dose calculation, data transfer, and the delivery system. However, patient-specific QA procedures are expensive and require significant time and effort by the physicists. Over the past 5 years, machine learning (ML) and deep learning (DL) algorithms for predictions of IMRT/VMAT QA outcome have been investigated. Various ML and DL models have shown promising prediction accuracy and a high potential as time-efficient virtual QA tool. In this paper, we review the ML and DL based models that were developed for patient specific IMRT and VMAT QA outcome predictions from algorithmic and clinical applicability perspectives. We focus on comparing the algorithms, the dataset sizes, the input parameters and features, the QA outcome prediction approaches, the validation, the performance, the clinical applicability, and the potential clinical impact. In addition, we discuss the present challenges as well as the future directions in the implementation of these models. To the best of our knowledge, this is the first review on the application of ML and DL based models in IMRT/VMAT QA predictions.
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