Regression Model for DQA Pass Rate Prediction
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Solution Overview
Problem
Current dose delivery quality assurance (DQA) methods for high-precision radiation treatments, such as Tomotherapy, lack efficiency in predicting the pass rate based on treatment plan parameters, leading to repetitive measurements and potential re-treatment plans.
Innovation Solution
A method using regression analysis to derive a correlation between treatment plan parameters and DQA pass rates, enabling the creation of a prediction model that calculates a predicted pass rate, allowing for pre-emptive determination of successful DQA and reducing unnecessary measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If DQA is performed for every treatment plan using gamma analysis, then measurement accuracy is ensured, but time consumption and measurement capacity increase significantly
Solution Approach 1:
The system performs preliminary analysis by extracting treatment plan parameters (beam angle, MLC leaf settings, dose distribution characteristics) and inputting them into a prediction model before actual DQA measurement. This preliminary action predicts the likely pass rate, allowing the system to determine whether full DQA measurement is necessary, thereby reducing unnecessary measurement time while maintaining accuracy for cases that need it.
Solution Approach 2:
Instead of performing physical DQA measurements for all treatment plans, the system creates a predictive copy of the DQA result using a machine learning model trained on historical DQA data. This virtual copy provides pass rate predictions based on treatment plan parameters, reducing the need for actual physical measurements while maintaining measurement accuracy for predicted failing cases.
2Reliability
If DQA measurement is performed for all treatment plans, then quality assurance reliability is maintained, but productivity and treatment efficiency decrease
Solution Approach 1:
The system uses a prediction model to create virtual DQA results based on treatment plan parameters, replacing full physical measurements for cases predicted to pass. This copying approach maintains reliability by still performing actual measurements on predicted failing cases while significantly increasing productivity by eliminating unnecessary measurements.
Solution Approach 2:
The prediction model serves the quality assurance function autonomously by analyzing treatment plan parameters and predicting pass rates without requiring manual intervention or full measurement for every case. The system self-determines which cases need actual DQA measurement, improving both reliability and productivity.
3Reliability
If repetitive DQA measurements are performed without prediction, then measurement thoroughness is ensured, but resource capacity and measurement load increase
Solution Approach 1:
The system performs preliminary prediction using treatment plan parameters before committing measurement resources. This preliminary action identifies cases unlikely to pass DQA, allowing resources to be concentrated on those cases, thereby maintaining thoroughness while reducing overall resource capacity requirements.
Solution Approach 2:
The prediction model creates virtual assessments of treatment plans, providing a thorough preliminary evaluation that reduces the need for repeated physical measurements. This copying approach maintains measurement thoroughness for critical cases while significantly reducing the total measurement load on resources.
Data Source
AI summary
The present disclosure relates to a method for carrying out dose delivery quality assurance for high-precision radiation treatment, in which parameters affecting a pass rate of dose delivery quality assurance can be derived through regression analysis, which is a known statistical analysis method, and a pass rate prediction model capable of predicting each parameter and the pass rate of dose delivery quality assurance can be derived, and accordingly, it can be predicted in advance whether dose delivery quality assurance will be passed according to the parameters through the above prediction model, without repeatedly carrying out dose delivery quality assurance according to a patient's treatment plan, and as a result, the efficiency of dose delivery quality assurance can be enhanced, and the time or capacity required for such quality assurance is reduced, such that radiation treatment for an actual patient can be quickly and precisely carried out.