ArticleRadiation oncology (London, England)2019
Prediction of VMAT delivery accuracy with textural features calculated from fluence maps.
Article in Radiation oncology (London, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Explainable machine learning for patient-specific quality assurance in intensity-modulated radiotherapy based on anatomical structures.Journal of applied clinical medical physics · 2026Article
- CT imaging is a feasible method for the preoperative prediction of the main pathologic response after neoadjuvant therapy for locally advanced esophageal squamous cell carcinoma.Updates in surgery · 2026Article
- 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
- Progressive resolution optimizer (PRO) predominates over photon optimizer (PO) in sparing of spinal cord for spine SABR VMAT plans.BMC cancer · 2023Article
- Virtual patient-specific QA with DVH-based metrics.Journal of applied clinical medical physics · 2022Article
- Machine learning models to predict the delivered positions of Elekta multileaf collimator leaves for volumetric modulated arc therapy.Journal of applied clinical medical physics · 2022Article
- Predicting gamma evaluation results of patient-specific head and neck volumetric-modulated arc therapy quality assurance based on multileaf collimator patterns and fluence map features: A feasibility study.Journal of applied clinical medical physics · 2022Article
- Comparison of dosimetric effects of MLC positional errors on VMAT and IMRT plans for SBRT radiotherapy in non-small cell lung cancer.PloS one · 2022Article
- Improvement Using Planomics Features on Prediction and Classification of Patient-Specific Quality Assurance Using Head and Neck Volumetric Modulated Arc Therapy Plan.Frontiers in neuroscience · 2021Article
- Patient-specific quality assurance using machine log files analysis for stereotactic body radiation therapy (SBRT).Journal of applied clinical medical physics · 2020Article
- Plan Complexity Metric Based Patient Specific Quality Assurance Outcome Prediction Across Two Machines.Journal of medical physicsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundComprehensively textural feature performance test from volumetric modulated arc therapy (VMAT) fluences to predict plan delivery accuracy.
methodsA total of 240 VMAT plans for various treatment sites were analyzed, with Trilogy and TrueBeam STx systems. Fluence maps superposed fluences at each control point per plan. The textural features were the angular second moment (ASM), inverse difference moment (IDM), contrast, variance, correlation, and entropy, calculated from fluence maps using three displacement distances. Correlation analysis of textural feature performance as predictors of VMAT delivery accuracy used global gamma passing rates with MapCHECK2 and ArcCHECK dosimeters, and mechanical delivery errors calculated from machine log files.
resultsSpearman's rank correlation coefficients (r) of the ASM (d = 10) to the gamma passing rates with 1%/2 mm using the MapCHECK2 were 0.358 and 0.519, respectively (p < 0.001). For the ArcCHECK, they were 0.273 (p = 0.001) and 0.259 (p = 0.009), respectively. The r-values of the ASM (d = 10) to the Trilogy and TrueBeam STx MLC errors were - 0.843 and - 0.859, respectively (p < 0.001), and those to the MU delivery errors were - 0.482 and - 0.589, respectively (p < 0.001). The ASM (d = 10) showed better performance in predicting VMAT delivery accuracy.
conclusionsThe ASM (d = 10) calculated from VMAT plan fluence maps were strongly correlated with global gamma passing rates and MLC delivery errors, and can predict VMAT delivery accuracy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.