Iterative Contour Optimization for Radiation Toxicity Mitigation
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Solution Overview
Problem
Current radiation therapy techniques face challenges in minimizing toxicity to nearby organs at risk (OARs) during treatment, as existing methods are time-intensive and often rely on manual trial and error, making it difficult to optimize radiation dosage distribution effectively.
Innovation Solution
A computer-based system that predicts radiation toxicity to OARs by iteratively modifying the treatment region's contour until the predicted toxicity falls below a threshold, using geometrical representations and prior patient data to optimize radiation dose distribution and minimize toxicity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual trial and error methods are used to optimize radiation dosage distribution, then treatment planning can be performed, but the process becomes time-intensive and inefficient
Solution Approach 1:
The system performs automated iterative optimization of radiation dosage distribution without requiring manual intervention. The computer automatically adjusts treatment parameters, evaluates toxicity predictions, and refines the dosage distribution until optimization criteria are met, enabling the system to serve itself rather than relying on manual trial and error methods
Solution Approach 2:
The patent replaces manual mechanical optimization processes with computer-based automated algorithms. The system uses computational methods to iteratively calculate and adjust radiation dosage distributions, substituting the mechanical trial-and-error approach with an automated computational system that rapidly evaluates multiple scenarios and converges on optimal solutions
2Reliability
If radiation dosage is increased to improve treatment effectiveness, then tumor treatment efficacy improves, but toxicity to neighboring organs at risk increases
Solution Approach 1:
The system applies different radiation dosage levels to different spatial regions, delivering high doses to tumor regions while maintaining lower doses in areas containing organs at risk. The automated optimization iteratively adjusts local dosage parameters to achieve maximum tumor control while respecting toxicity constraints for neighboring healthy tissues
Solution Approach 2:
The system incorporates feedback mechanisms where toxicity predictions are continuously evaluated against threshold criteria during the iterative optimization process. When predicted toxicity to organs at risk exceeds acceptable thresholds, the system automatically adjusts the dosage distribution and re-evaluates, creating a closed-loop feedback system that balances treatment effectiveness with safety constraints
Data Source
AI summary
An apparatus for determining a contour of a treatment region in a patient includes a computer processor to receive input regarding a contour of at least one organ-at-risk (OAR) adjacent to the treatment region; receive input regarding an initial contour of the treatment region; predict a radiation toxicity to the at least one OAR based on the contour of the at least one OAR, the initial contour of the treatment region, and a radiation treatment regimen; determine whether the predicted radiation toxicity exceeds a threshold; and determine a contour of the treatment region by iteratively modifying the initial contour of the treatment region, and any subsequent modified contours of the treatment region, until a stopping condition is satisfied. The stopping condition can be a preselected number of iterations or that the predicted radiation toxicity using the contour in place of the initial contour is first calculated is below said threshold.