ArticleTechnology in cancer research & treatment
Statistical Analysis of Treatment Planning Parameters for Prediction of Delivery Quality Assurance Failure for Helical Tomotherapy.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Association of planned-recorded sinogram difference metrics with treatment planning parameters and gamma pass rates in helical tomotherapy.Radiological physics and technology · 2026Article
- Quality assurance of craniospinal irradiation in helical tomotherapy using a dose reconstruction tool based on leaf open time.Journal of applied clinical medical physics · 2025Article
- Evaluation of dose delivery accuracy of the lung with the respiratory technique of the Radixact Synchrony real-time tumor tracking system.Technical innovations & patient support in radiation oncology · 2025Article
- Epidemiology, pathophysiology and clinical aspects of Hepatocellular Carcinoma in MAFLD patients.Hepatology international · 2024Review
- Comprehensive clinical evaluation of TomoEQA for patient-specific pre-treatment quality assurance in helical tomotherapy.Radiation oncology (London, England) · 2022Article
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aimed to investigate the parameters with a significant impact on delivery quality assurance (DQA) failure and analyze the planning parameters as possible predictors of DQA failure for helical tomotherapy.
methodsIn total, 212 patients who passed or failed DQA measurements were retrospectively included in this study. Brain (n = 43), head and neck (n = 37), spinal (n = 12), prostate (n = 36), rectal (n = 36), pelvis (n = 13), cranial spinal irradiation and a treatment field including lymph nodes (n = 24), and other types of cancer (n = 11) were selected. The correlation between DQA results and treatment planning parameters were analyzed using logistic regression analysis. Receiver operating characteristic (ROC) curves, areas under the curves (AUCs), and the Classification and Regression Tree (CART) algorithm were used to analyze treatment planning parameters as possible predictors for DQA failure.
resultsThe AUC for leaf open time (LOT) was 0.70, and its cut-off point was approximately 30%. The ROC curve for the predicted probability calculated when the multivariate variable model was applied showed an AUC of 0.815. We confirmed that total monitor units, total dose, and LOT were significant predictors for DQA failure using the CART.
conclusionsThe probability of DQA failure was higher when the percentage of LOT below 100 ms was higher than 30%. The percentage of LOT below 100 ms should be considered in the treatment planning process. The findings from this study may assist in the prediction of DQA failure in the future.
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