Identifying predictive parameters for failure of DQA in patient using helical tomotherapy planning
Keywords:
Helical tomotherapy, pre-treatment delivery quality assurance (DQA), intensity-modulated radiation therapy (IMRT) planning, predictive model for DQAAbstract
Background: Radiation therapy using helical tomotherapy requires pre-treatment delivery quality assurance (DQA) to verify treatment accuracy. However, in certain urgent situations such as patients requiring radiation to stop bleeding, those receiving radiation for pain relief, or those with a strictly scheduled treatment course where DQA cannot be performed immediate treatment planning and delivery are necessary without DQA verification.
Objective: To identify predictors associated with DQA failure and to develop a predictive model for pre-treatment DQA in helical tomotherapy.
Materials and Methods: Data were retrospectively collected from treatment plans stored in the Accuray Precision system. The data included the following parameters: treatment sites (Head and Neck, Brain, Chest, Abdomen, and Pelvis), fraction dose, target volume, field width (1 cm, 2.5 cm, 5 cm), pitch, modulation factor (planned and actual), gantry rotations, gantry period, beam-on time, couch travel, couch speed, fraction monitor units (MUs), and leaf open time parameters (maximum, minimum, mean, mode, and standard deviation). All data were obtained from the Radiation Oncology Unit, Department of Radiology, Buddhachinaraj Hospital, Phitsanulok, between January 2020 and December 2023. Predictive factors for delivery quality assurance (DQA) failure prior to treatment delivery were analyzed using odds ratios (ORs) derived from logistic regression analysis with stepwise backward selection.
Results: Predictors significantly associated with DQA failure requiring treatment re-planning included: pelvic treatment sites (OR 2.91, 95% CI 1.52-5.57), field width of 2.5 cm (OR 0.25, 95% CI 0.07-0.91), beam on time (OR 0.99, 95% CI 0.99-0.99), couch speed (mm/sec) (OR 0.14, 95% CI 0.32-0.60), leaf open time (mode) (OR 0.99, 95% CI 0.99-1.00), and leaf open time (std) (OR 1.02, 95% CI 1.01-1.04).
Conclusion: The predictors obtained from this study can be applied to improve the efficiency of treatment planning, reduce the necessity of repeating pre-treatment quality assurance, and shorten the waiting time in cases where re-planning is required, especially in urgent situations where quality assurance cannot be performed. Therefore, they serve as tools to support clinical decision making more rapidly and accurately.
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