Regional Dose Distribution Modeling for Radiotherapy
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Solution Overview
Problem
Current cancer radiotherapy delivers a homogeneous dose to tumors, which can result in unneeded exposure to sensitive areas and ineffective treatment of resistant parts, due to limited understanding of intra-tumor heterogeneity and spatial resolution of imaging techniques, leading to normal tissue damage.
Innovation Solution
The system uses functional imaging information to model and distribute radiation doses based on the probability of residual disease and radio-sensitivity variations within the tumor, allowing for personalized treatment by redistributing doses to match radio-sensitivity gradients, using machine-learning algorithms to predict treatment responses and adjust doses accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If homogeneous dose is delivered to the entire tumor, then treatment coverage is ensured, but resistant regions are inadequately treated and sensitive regions receive unneeded exposure
Solution Approach 1:
The patent applies local quality by delivering different radiation doses to different regions of the tumor based on their radio-sensitivity characteristics. Regions with high radio-sensitivity receive lower doses while resistant regions receive higher doses, optimizing treatment effectiveness and reducing unnecessary exposure to sensitive areas.
Solution Approach 2:
The tumor is segmented into multiple regions with distinct radio-sensitivity characteristics using functional imaging data. This segmentation allows for customized dose distribution where each region receives an appropriate dose level, preventing both under-treatment of resistant areas and over-treatment of sensitive areas.
2Reliability
If higher dose is delivered to resistant regions, then treatment effectiveness improves, but risk to normal tissue increases
Solution Approach 1:
The patent changes the radiation dose parameter spatially across the tumor volume based on measured radio-sensitivity variations. By adjusting the dose parameter according to local tissue characteristics derived from functional imaging, the system achieves improved tumor control while constraining normal tissue exposure within safe limits.
3Adaptability or versatility
If functional imaging is used to guide dose distribution, then personalized treatment is achieved, but system complexity increases
Solution Approach 1:
The patent uses functional imaging as an intermediary to bridge the gap between tumor heterogeneity and radiation dose planning. The imaging data serves as a mediator that provides quantitative information about radio-sensitivity distribution, enabling automated dose optimization without requiring complex manual planning processes.
Data Source
AI summary
Functional imaging information is used to determine a probability of residual disease given a treatment. The functional imaging information shows different characteristic levels for different regions of the tumor. The probability is output for planning use and/or used to automatically determine dose by region. Using the probability, the dose may be distributed by region so that some regions receive a greater dose than other regions. This distribution by region of dose more likely treats the tumor with a same dose, allows a lesser dose to sufficient treat the tumor, and/or allows a greater dose with a lesser or no increase in risk to normal tissue. The dose plan may account for personalized tumors as each patient may have distinct tumors. Probability of dose application accuracy may also be used, so that a combined treatment probability allows efficient dose planning.