ArticlePhysics and imaging in radiation oncology2024
Machine learning and lean six sigma for targeted patient-specific quality assurance of volumetric modulated arc therapy plans.
Article in Physics and imaging in radiation oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Review
- Prediction of VMAT gamma passing rates using 3D CNNs based on leaf position analysis and gradient class activation mapping for plan complexity evaluation.Medical physics · 2026Article
- A pathway away from measurement-based patient-specific QA: A review of contemporary quality assurance for external-beam radiotherapy sub-systems.Journal of applied clinical medical physics · 2025Review
- A Time-Series Approach for Machine Learning-Based Patient-Specific Quality Assurance of Radiosurgery Plans.Bioengineering (Basel, Switzerland) · 2025Article
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Authors and funding
18 authors.
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Abstract
Background and purpose: Radiotherapy plans with excessive complexity exhibit higher uncertainties and worse patient-specific quality assurance (PSQA) results, while the workload of measurement-based PSQA can impact the efficiency of the radiotherapy workflow. Machine Learning (ML) and Lean Six Sigma, a process optimization method, were implemented to adopt a targeted PSQA approach, aiming to reduce workload, risk of failures, and monitor complexity. Materials and methods: Lean Six Sigma was applied using DMAIC (define, measure, analyze, improve, and control) steps. Ten complexity metrics were computed for 69,811 volumetric modulated arc therapy (VMAT) arcs from 28,612 plans delivered in our Institute (2013-2021). Outlier complexities were defined as >95th-percentile of the historical distributions, stratified by treatment. An ML model was trained to predict the gamma passing rate (GPR-3 %/1mm) of an arc given its complexity. A decision support system was developed to monitor the complexity and expected GPR. Plans at risk of PSQA failure, either extremely complex or with average GPR <90 %, were identified. The tool's impact was assessed after nine months of clinical use. Results: Among 1722 VMAT plans monitored prospectively, 29 (1.7 %) were found at risk of failure. Planners reacted by performing PSQA measurement and re-optimizing the plan. Occurrences of outlier complexities remained stable within 5 %. The expected GPR increased from a median of 97.4 % to 98.2 % (Mann-Whitney p < 0.05) due to plan re-optimization. Conclusions: ML and Lean Six Sigma have been implemented in clinical practice enabling a targeted measurement-based PSQA approach for plans at risk of failure to improve overall quality and patient safety.
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