Radiation Treatment Planning Optimization for Immune Response
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Solution Overview
Problem
Current radiation therapy (RT) protocols do not optimize treatment planning to minimize Radiation-Induced Immune Suppression (RIIS) or leverage immunomodulatory effects, such as the abscopal effect, to enhance tumor control and patient outcomes.
Innovation Solution
The use of machine learning algorithms to predict the immune modulatory effects of RT, including RIIS and the generation of anti-tumor T cells, allowing for the optimization of treatment plans to minimize immune suppression and maximize beneficial immune responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current RT protocols are used, then tumor cells are killed at target sites, but Radiation-Induced Immune Suppression occurs and lymphocyte counts decrease
Solution Approach 1:
The patent changes the optimization parameters from traditional dosimetric parameters (maximum dose, mean dose) to immune-specific parameters (lymphocyte kill level, immune suppression score). This allows the treatment planning system to evaluate and optimize plans based on their impact on immune cells, enabling simultaneous achievement of tumor control and immune preservation through parameter transformation.
Solution Approach 2:
The patent implements feedback mechanisms by predicting immune effects (lymphocyte counts, immune suppression levels) as outcomes of treatment plans. These predictions are fed back into the optimization process to adjust and refine treatment parameters, creating an iterative system that learns from predicted immune responses to optimize future treatment plans.
2Reliability
If treatment planning focuses on tumor targets, then tumor control is improved, but immune system response factors are neglected
Solution Approach 1:
The patent makes the treatment planning system multi-functional by enabling it to optimize for both traditional tumor control parameters and immune response parameters simultaneously. The system can evaluate multiple objectives (tumor kill, immune preservation, abscopal effect potential) and generate plans that balance these competing goals, transforming a single-objective system into a multi-objective optimization platform.
Solution Approach 2:
The patent adds new dimensions to the treatment planning optimization by incorporating immune-specific parameters (lymphocyte dose, immune suppression score, time-dependent immune effects) alongside traditional dosimetric parameters. This dimensional expansion allows the system to navigate a higher-dimensional optimization space that accounts for both tumor control and immune system impacts.
3Reliability
If machine learning algorithms are introduced to predict immune effects, then treatment plan optimization is improved, but system complexity increases
Solution Approach 1:
The patent introduces machine learning algorithms as intermediary components that bridge the gap between treatment plan parameters and immune system outcomes. These algorithms act as predictive mediators that translate dosimetric data into immune effect predictions, enabling optimization without requiring direct modeling of complex biological immune responses.
Solution Approach 2:
The patent replaces complex biological modeling of immune responses with machine learning algorithms that learn patterns from data. Instead of mechanically modeling every biological interaction in the immune system, the system uses ML models that capture essential relationships through training on patient data, simplifying the overall system architecture while maintaining predictive accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the generation of treatment plans that reduce RIIS and enhance the generation of tumor-reactive immune cells, potentially leading to improved tumor control, reduced side effects, and enhanced patient outcomes.
Implementation Method 1
Ionizing radiation is generally used as a powerful tool to kill cancer cells at target sites by DNA damage (hard kill)
Implementation Method 2
RT can sometimes cause a relatively rare effect known as an abscopal effect... RT sometimes causes tumor cells to die in a way that releases tumor antigens, which may generate tumor-reactive effector T cells
Data Source
AI summary
Systems and methods are provided for estimating immune response effected by patient specific and plan specific radiation treatments (such as, e.g., SBRT). The systems and methods may take into account radiation impact on circulating immune blood cell types or sub-types, such as T lymphocytes, B lymphocytes, natural killer cells, erythrocytes, and/or neutrophils, and predict time dependent fractional blood count and cell kill following radiation therapy treatment. Additionally, the system, method, and computer readable medium provide parameters such as a dose dependent lymphocyte kill function and average net release rate of new lymphocytes into circulating blood (including promotion of cytotoxic T cells and suppression of lymphocytes in blood), which may be used for optimization of RT treatment plans.