Patient-Specific Prognosis Models for Radiation Treatment Planning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current radiation treatment planning systems rely on generic models for tumor control probability (TCP) and normal tissue complications probability (NTCP), which may not accurately reflect individual patient characteristics, leading to suboptimal treatment parameters.
Innovation Solution
The implementation of patient-specific prognosis models that determine survivability and complication probabilities as a function of dose, allowing for individualized treatment planning by inputting these probabilities into inverse treatment planning systems to optimize radiation treatment parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generic models for TCP and NTCP are used in radiation treatment planning, then the treatment planning process is simplified and can be implemented efficiently, but the accuracy of treatment parameters does not reflect individual patient characteristics, leading to suboptimal treatment outcomes
Solution Approach 1:
The patent applies local quality by transitioning from generic population-level TCP/NTCP models to patient-specific prognosis models that incorporate individual characteristics such as age, gender, tumor type, and comorbidities. Each patient receives a customized probability assessment tailored to their unique clinical profile, thereby improving measurement precision without sacrificing planning efficiency through automated model selection and parameter integration.
2Measurement precision
If patient-specific prognosis models are implemented to improve treatment parameter accuracy, then individualized treatment planning is achieved, but the complexity of the treatment planning system increases
Solution Approach 1:
The patent segments the treatment planning system into distinct modular components: (1) data acquisition module for collecting patient-specific characteristics, (2) prognosis model selection module for choosing appropriate TCP/NTCP models, (3) parameter calculation module for computing individualized probabilities, and (4) integration module for incorporating results into treatment planning. This segmentation manages system complexity by organizing functions into independent, manageable units that can be developed and validated separately.
Solution Approach 2:
The patent applies preliminary action by pre-establishing a library of validated prognosis models with known performance characteristics for different patient populations and tumor types. Before actual treatment planning, the system pre-processes patient data to identify relevant characteristics and pre-selects appropriate models, thereby reducing real-time computational complexity and streamlining the interactive planning process.
3Ease of operation
If standardized TCP and NTCP values are used, then the treatment planning process is straightforward and easy to operate, but the treatment parameters do not account for individual patient variability, resulting in suboptimal outcomes
Solution Approach 1:
The patent implements self-service by enabling the system to automatically select appropriate prognosis models and calculate patient-specific TCP/NTCP probabilities without requiring manual intervention or expert judgment. The system autonomously processes patient characteristics, selects from pre-validated models, and generates individualized treatment recommendations, thereby maintaining ease of operation while significantly improving reliability through personalized assessments.
Data Source
AI summary
Automated treatment planning is provided with individual specific consideration. One or more prognosis models indicate survivability as a function of patient specific information for a given dose. By determining survivability for a plurality of doses, the biological model represented by survivability as a function of dose is determined from the specific patient. Similarly, the chances of complications or side effects are determined. The chance of survivability and chance of complication are used as or instead of the tumor control probability and normal tissue complications probability, respectively. The desired tumor dosage and tolerance dosage are selected as a function of the patient specific dose distributions. The selected dosages are input to an inverse treatment planning system for establishing radiation treatment parameters.