ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2021
Commissioning and clinical implementation of an Autoencoder based Classification-Regression model for VMAT patient-specific QA in a multi-institution scenario.
Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- General versus site specific quantile random forest models for virtual patient-specific quality assurance of VMAT plans.Journal of applied clinical medical physics · 2026Article
- Comparative dosimetric evaluation of independent verification systems: ArcherQA versus AccuCheck for volumetric modulated Arc therapy plan validation.Radiation oncology (London, England) · 2026Article
- A multimodal multi-scale transformer for virtual pretreatment patient-specific QA of SBRT using portal-dosimetry fluence maps.Scientific reports · 2026Article
- Simultaneous synthesis of perfusion and ventilation images from CT using a dual-decoder residual attention network for lung disease diagnosis.Journal of applied clinical medical physics · 2026Article
- Evaluation of an interpretable deep-learning model for the automated plan review of intensity-modulated radiation therapy.Frontiers in oncology · 2026Article
- Unifying gamma passing rates in patient-specific QA for VMAT lung cancer treatment based on data assimilation.Physical and engineering sciences in medicine · 2024Article
- In vivo EPID-based daily treatment error identification for volumetric-modulated arc therapy in head and neck cancers with a hierarchical convolutional neural network: a feasibility study.Physical and engineering sciences in medicine · 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
- Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine.Diagnostics (Basel, Switzerland) · 2023Article
- A hybrid method to improve efficiency of patient specific SRS and SBRT QA using 3D secondary dose verification.Journal of applied clinical medical physics · 2023Article
- Do we need patient-specific QA for adaptively generated plans? Retrospective evaluation of delivered online adaptive treatment plans on Varian Ethos.Journal of applied clinical medical physics · 2023Article
- Anomaly detection in radiotherapy plans using deep autoencoder networks.Frontiers in oncology · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors.
Funding
Abstract
background and purposeTo commission and implement an Autoencoder based Classification-Regression (ACLR) model for VMAT patient-specific quality assurance (PSQA) in a multi-institution scenario. MATERIALS AND
methods1835 VMAT plans from seven institutions were collected for the ACLR model commissioning and multi-institutional validation. We established three scenarios to validate the gamma passing rates (GPRs) prediction and classification accuracy with the ACLR model for different delivery equipment, QA devices, and treatment planning systems (TPS). The prediction performance of the ACLR model was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The classification performance was evaluated using sensitivity and specificity. An independent end-to-end test (E2E) and routine QA of the ACLR model were performed to validate the clinical use of the model.
resultsFor multi-institution validations, the MAEs were 1.30-2.80% and 2.42-4.60% at 3%/3 mm and 3%/2 mm, respectively, and RMSEs were 1.55-2.98% and 2.83-4.95% at 3%/3 mm and 3%/2 mm, respectively, with different delivery equipment, QA devices, and TPS, while the sensitivity was 90% and specificity was 70.1% at 3%/2 mm. For the E2E, the deviations between the predicted and measured results were within 3%, and the model passed the consistency check for clinical implementation. The predicted results of the model were the same in daily QA, while the deviations between the repeated monthly measured GPRs were all within 2%.
conclusionsThe performance of the ACLR model in multi-institution scenarios was validated on a large scale. Routine QA of the ACLR model was established and the model could be used for VMAT PSQA clinically.
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