ArticlePhysics and imaging in radiation oncology2025
Detection of the failed-tolerance causes of electronic-portal-imaging-device-based
Article in Physics and imaging in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Beyond Couch Correction in Head and Neck Radiotherapy: A Narrative Review of Action-Oriented Positioning Management.Cancers · 2026Review
- Machine Learning-Based Prediction of Elekta MLC Motion with Dosimetric Validation for Virtual Patient-Specific QA.Bioengineering (Basel, Switzerland) · 2025Article
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Authors and funding
9 authors.
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Abstract
Background and Purpose: When electronic-portal-imaging-device (EPID)-based Materials and Methods: Twenty-three prostate VMAT plans were used to recalculate the dose distribution in homogeneous phantom images as no-error (NE) plans. Errors in the randomized multileaf collimator (RMLC) position, monitor unit (MU) variation, lateral position, pitch rotation, and roll rotation were simulated. The IVD results of the NE plans and introduced errors were obtained using EPIgray software. Support vector machines (SVMs) were used to develop ML models for each error. The accuracy percentage, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate models' performances. The models were verified using five additional plans with an Alderson Rando phantom. Results: The models obtained accuracies of over 90% and F1-scores of 0.9 for the RMLC position and MU variation. For lateral position, pitch rotation, and roll rotation errors, the accuracies were 66.1%, 65.2%, and 66.8%, and the F1-scores were 0.66, 0.65, and 0.67, respectively. The AUCs for all the errors were over 0.7. Additionally, the model verification results consistently classified EPIgray data for all the error types. Conclusion: The developed ML models classified the causes of the failed tolerance of the EPID-based IVD.
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