Treatment Planning System Using Estimation Models for Quality Scores
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Solution Overview
Problem
Current treatment planning for tumors is time-consuming and inefficient, as physicians often generate multiple treatment plans before determining that the initial plan is not suitable, leading to prolonged processes and potential suboptimal treatment choices.
Innovation Solution
A system and method that utilize estimation models to predict the quality of different treatment plans based on patient data, allowing for early determination of the most suitable treatment strategy by calculating quality scores for various treatment types, such as photon and proton radiation therapy, chemotherapy, and surgery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple treatment plans are generated and evaluated manually, then treatment quality can be improved, but time consumption increases significantly
Solution Approach 1:
The system performs preliminary evaluation of multiple treatment plans using estimation models before the physician makes a final decision. By pre-calculating quality metrics and predicting treatment outcomes for different options (photon, proton, chemotherapy, surgery), the system enables early identification of suitable treatment types, avoiding time-consuming manual evaluation of inadequate plans later in the process
Solution Approach 2:
An intermediary computational system acts as a bridge between the physician's requirements and treatment plan evaluation. This system automatically generates multiple treatment plans, estimates their quality using trained models, and presents ranked options to the physician, thereby reducing the physician's direct time investment while maintaining high treatment quality through systematic evaluation
2Measurement precision
If treatment plans are evaluated only after generation, then comprehensive assessment is achieved, but decision-making is delayed
Solution Approach 1:
The system implements continuous feedback through automated quality estimation models that evaluate treatment plans as they are generated. These models provide real-time quality metrics and predictions about treatment outcomes, allowing physicians to receive feedback on multiple treatment options simultaneously rather than waiting for sequential manual evaluation, thus accelerating decision-making while maintaining comprehensive assessment
Solution Approach 2:
The estimation models perform preliminary assessment of treatment plan quality before the physician commits to a final decision. By pre-evaluating multiple treatment options and ranking them based on predicted outcomes, the system enables early identification of promising treatment types, allowing physicians to focus their detailed review on only the most suitable options
3Productivity
If physicians rely on prior experience for treatment selection, then decision speed improves, but treatment optimization decreases
Solution Approach 1:
An intermediary computational system mediates between the physician's expertise and treatment optimization by automatically generating and evaluating multiple treatment plans using advanced algorithms. The system processes physician requirements and patient data to produce optimized treatment options that the physician would not easily conceive manually, while the physician retains final decision authority, thus combining computational optimization with clinical judgment
Solution Approach 2:
The system changes the parameters of treatment plan evaluation by using automated estimation models that calculate quality metrics based on multiple factors simultaneously (dose distribution, organ sparing, treatment constraints). This enables comprehensive optimization across many parameters that would be difficult for a physician to evaluate manually, while maintaining decision speed through automated parallel processing of multiple treatment options
Data Source
AI summary
Patient data can be used to determine input values to different estimation functions for different treatment types. The estimation functions can each be used to estimate one or more outcome values for the respective treatment. A quality score can be determined using the outcome value(s). A first treatment plan having an optimal quality score can be identified, e.g., by displaying the treatment plans with the quality scores, which may correspond to the outcome values.