Dynamic Carrying Capacity Modeling for Personalized Radiotherapy Response
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Solution Overview
Problem
Current radiation therapy models fail to account for patient-specific tumor-microenvironmental properties, particularly the proliferation saturation index (PSI), which is crucial for predicting individual patient responses and outcomes, limiting the personalization of treatment plans.
Innovation Solution
A method and system using a logistic growth model to estimate patient-specific carrying capacity and tumor volume dynamics, incorporating historical and real-time data to predict future tumor volumes and treatment outcomes, such as locoregional control and disease-free survival, by modeling radiation's impact on the tumor microenvironment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional radiation therapy models are used, then treatment planning is simplified, but patient-specific tumor-microenvironmental properties and proliferation saturation index are not accounted for, reducing prediction accuracy
Solution Approach 1:
The system performs preliminary estimation of patient-specific carrying capacity and proliferation saturation index using logistic growth models before radiation treatment planning. This allows the model to incorporate tumor-microenvironmental properties in advance, improving prediction accuracy without adding complexity during the actual treatment delivery phase
Solution Approach 2:
The patent introduces carrying capacity and proliferation saturation index as intermediary parameters that bridge traditional radiation models and patient-specific tumor dynamics. These intermediaries enable the integration of tumor-microenvironmental properties into existing treatment planning frameworks without requiring complete model replacement
2Adaptability or versatility
If patient-specific carrying capacity and proliferation saturation index are incorporated, then personalized treatment prediction is improved, but data requirements and computational complexity increase
Solution Approach 1:
The system dynamically estimates carrying capacity and proliferation saturation index based on real-time tumor volume measurements during treatment. This dynamic approach allows the model to adapt to individual patient responses while using straightforward logistic growth equations that maintain computational simplicity
Solution Approach 2:
The patent implements feedback loops where tumor volume measurements during radiation treatment are continuously fed back into the logistic growth model to update carrying capacity estimates. This feedback mechanism enables personalization without requiring complex iterative computations, as the model converges quickly with minimal data points
3Loss of time
If early tumor volume measurements are used, then treatment adaptation can occur earlier, but measurement frequency and resource utilization increase
Solution Approach 1:
The system achieves effective treatment adaptation using only one or two early tumor volume measurements during radiation treatment rather than requiring frequent continuous monitoring. This partial action approach provides sufficient information to estimate carrying capacity and predict outcomes without the resource burden of intensive measurement schedules
Solution Approach 2:
The patent treats early tumor volume measurements as disposable data points that, when combined with the logistic growth model, provide sufficient information for treatment adaptation. This approach values early measurements highly for their predictive power while avoiding the need for sustained intensive monitoring resources
Data Source
AI summary
Systems and methods for predicting outcome of radiation therapy is described herein. An example method includes receiving respective values for tumor volume of a target patients tumor at first and second time points, and calculating a change in tumor volume between the first and second time points. The method also includes estimating a patient-specific carrying capacity based on a logistic growth model and the change in tumor volume. Additionally, the method includes predicting a volume of the target patient's tumor at a future time point during radiation treatment based, at least in part, on a historical carrying capacity reduction fraction distribution and the patient-specific carrying capacity. The method further includes predicting a patient-specific outcome of radiation therapy for the target patient based, at least in part, on the predicted volume of the target patients tumor at the future time point.