Radiation Planning System Using Biological Effect Knowledge Base
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Solution Overview
Problem
Current radiation treatment planning methods fail to adequately account for biological effects, such as different tissue recovery rates, when calculating dose distributions and fraction doses, leading to inaccurate dose delivery and increased risks to healthy tissues, especially in multiple radiation therapy sessions and advanced treatments like SBRT or SRS.
Innovation Solution
A knowledge-based approach that incorporates biological characteristics and fraction dose information to predict and factor in biological effects during radiation planning, enabling the generation of more accurate dose-volume histograms and probability estimates for target and normal tissues, applicable to various radiation modalities like protons, electrons, and photons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional radiation planning methods are used that express planning metrics as physical quantities without biological context, then the calculation process is simple, but the accuracy of dose delivery is insufficient and risks to healthy tissues increase
Solution Approach 1:
The patent transforms the planning metric from physical quantity (absorbed dose) to biological quantity (biological effect) by applying the LQ model. This parameter change enables accurate summation and comparison of effects across different fraction doses and treatment modalities, directly improving dose delivery accuracy while accounting for biological context.
Solution Approach 2:
The patent introduces the LQ model as an intermediary that converts physical dose quantities into biological effect quantities. This intermediary layer enables the system to handle biological context without requiring complete restructuring of the planning system, thus improving accuracy while managing complexity.
2Reliability
If biological effects are not accounted for in radiation planning, then the planning process is simpler, but the reliability of treatment outcomes deteriorates due to inability to sum and compare effects from multiple fraction doses
Solution Approach 1:
The patent changes the fundamental parameter from physical dose to biological effect using the LQ model, enabling reliable summation and comparison of treatment effects across multiple sessions. This transformation directly addresses the reliability issue by providing a consistent biological metric that accounts for fractionation effects.
3Manufacturing precision
If conventional radiation planning is used without biological context, then the planning process is more straightforward, but the ability to optimize dose distribution for different tissue types deteriorates
Solution Approach 1:
The patent applies the LQ model with tissue-specific parameters (α/β ratios) to different anatomical structures, enabling optimized dose distribution tailored to the biological characteristics of each tissue type. This local quality approach allows different prescription doses and constraints to be applied to different regions based on their specific biological properties.
4Measurement precision
If biological effects are incorporated into radiation planning using the LQ model, then the accuracy of biological effect prediction improves, but the computational complexity increases
Solution Approach 1:
The LQ model serves as a computational intermediary that systematically converts physical dose calculations into biological effect predictions using established formulas. This intermediary approach provides a structured method for incorporating biological complexity without requiring complete redesign of the computational framework.
Data Source
AI summary
Solutions are provided herein that specifically accounts for biological effects of tissue during radiation planning (such as treatment planning). In one or more embodiments, the biological effects may be calculated by accessing a knowledge base to determine reference data comprising at least one biological characteristic corresponding to the at least one organ, predicting a biological effect for the plurality of identified structures based on the biological characteristic corresponding to the at least one organ, and generating or modifying a radiation plan based on the biological effect. By incorporating biological data and fraction dose information, dose-estimation models can be created and trained to more accurately estimate dose absorption and effectiveness. Moreover, existing estimation models may be adapted to create dose estimations that account for the biological efficiency of target structures.