Adaptive Patient-Specific Margins in EBRT Planning
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Solution Overview
Problem
Current external beam radiation therapy (EBRT) methods rely on generic population-based treatment margins, which can lead to overdosing of organs at risk due to tissue motion during and between treatment sessions, resulting in inaccurate dose delivery and potential damage to surrounding tissues.
Innovation Solution
A treatment planning system that uses motion data from target surrogates to calculate a motion-compensated dose distribution, allowing for adjustment of treatment margins based on dosimetric differences between planned and actual dose delivery, thereby improving the accuracy of radiation delivery to tumors and reducing normal tissue damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generic population-based treatment margins are used to ensure complete tumor coverage, then tumor irradiation completeness is improved, but organs at risk receive excessive radiation dose
Solution Approach 1:
The patent applies local quality by transitioning from uniform population-based margins to patient-specific margins tailored to individual motion characteristics. Each patient receives customized margins based on their unique motion patterns, allowing optimized radiation delivery that provides adequate tumor coverage while minimizing exposure to organs at risk for each specific patient case.
Solution Approach 2:
The patent changes the parameter of treatment margins from fixed population-based values to dynamic patient-specific values. By incorporating motion data and dosimetric analysis, the system adjusts margin parameters individually for each patient, enabling precise control over radiation delivery parameters to balance tumor coverage and organ sparing.
2Object-affected harmful factors
If treatment margins are reduced to spare organs at risk, then radiation damage to surrounding tissues is reduced, but tumor coverage becomes insufficient due to tissue motion
Solution Approach 1:
The patent implements feedback by incorporating dosimetric analysis of motion-compensated dose distributions into the margin adjustment process. The system uses actual dose delivery data and motion information to feedback-adjust treatment margins, ensuring that reduced margins do not compromise tumor coverage while still providing adequate sparing of organs at risk.
Solution Approach 2:
The patent applies preliminary action by calculating motion-compensated dose distributions before finalizing treatment margins. This preliminary dosimetric evaluation allows the system to predict the impact of motion on dose delivery and adjust margins proactively, ensuring adequate tumor coverage is maintained even when reducing margins to spare organs at risk.
3Measurement precision
If motion data is collected and processed to create patient-specific margins, then dosimetric accuracy is improved, but treatment planning complexity increases
Solution Approach 1:
The patent uses an intermediary approach by introducing motion-compensated dose distribution calculations as a bridge between motion data and final margin determination. This intermediary step systematically processes motion information and translates it into dosimetric adjustments, making the complex interaction between motion, dose calculation, and margin optimization more manageable and structured.
Data Source
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AI summary
A treatment planning system (106) for generating patient-specific treatment margins. The system (106) includes one or more processors (142). The processors (142) are programmed to receive a radiation treatment plan (RTP) for irradiating a target (122) over the course of one or more treatment fractions. The RTP including one or more treatment margins around the target (122) and a planned dose distribution for the target (122). The processors (142) are further programmed to receive motion data for at least one of the treatment fractions of the RTP from one or more target surrogates (124), calculate a motion-compensated dose distribution for the target (122) using the motion data and the planned dose distribution, compare the motion-compensated dose distribution to the planned dose distribution, and adjust the treatment margins based on dosimetric differences between the motion-compensated dose distribution and the planned dose distribution.