ArticleFrontiers in oncology2022
Prediction of portal dosimetry quality assurance results using log files-derived errors and machine learning techniques.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 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
- Evaluation of dose rate and gantry speed variations in volumetric modulated Arc therapy optimization algorithms for a modern linear accelerator.Radiological physics and technology · 2026Article
- Beyond Gamma Analysis: An Accessibility-oriented Framework for Comparative Evaluation of Patient-specific Radiotherapy Quality Assurance.Journal of medical physicsArticle
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6 authors.
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Abstract
Objective: This work aims to use machine learning models to predict gamma passing rate of portal dosimetry quality assurance with log file derived features. This allows daily treatment monitoring for patients and reduce wear and tear on EPID detectors to save cost and prevent downtime. Methods: 578 VMAT trajectory log files selected from prostate, lung and spine SBRT were used in this work. Four machine learning models were explored to identify the best performing regression model for predicting gamma passing rate within each sub-site and the entire unstratified data. Predictors used in these models comprised of hand-crafted log file-derived features as well as modulation complexity score. Cross validation was used to evaluate the model performance in terms of R Result: Using gamma passing rate of 1%/1mm criteria and entire dataset, LASSO regression has a R Conclusion: Log file-derived features can predict gamma passing rate of portal dosimetry with an average error of less than 2% using the 1%/1mm criteria. This model can potentially be applied to predict the patient specific QA results for every treatment fraction.
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