Radiation Treatment Planning Model for Clinical Goal Integration
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Solution Overview
Problem
Current radiation treatment planning methods face challenges in accurately defining clinical goals, particularly in combining knowledge-based and clinical goal approaches, leading to potential misjudgments and difficulties in adapting to new patient geometries.
Innovation Solution
A treatment planning technique that combines knowledge-based and clinical goal approaches by using a modeler to obtain a model definition with quality metrics, a model trainer to train models based on existing treatment plans, and an optimizer to generate a cost function for determining optimal treatment plans, allowing for flexible goal definition and adaptation to new patient geometries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the knowledge based approach is used to generate cost functions, then the burden of explicit goal determination is removed, but misjudgments of the training set may occur and ad hoc changes to clinical practice are difficult
Solution Approach 1:
The system incorporates feedback mechanisms where the trained model's predictions are evaluated against actual clinical outcomes. The model can be retrained with updated training sets that reflect new clinical practices, allowing the system to learn from experience and correct misjudgments while maintaining ease of operation.
Solution Approach 2:
The knowledge-based model is designed to be dynamic and adaptable. The training set can be updated with new treatment plans and clinical practices, allowing the model to evolve and adapt to changing clinical guidelines without requiring complete reconfiguration, thus maintaining both ease of operation and reliability.
2Adaptability or versatility
If the direct clinical goal approach is used, then flexible goal definition is enabled, but anything not defined as a goal may be treated as ambivalent in the optimization
Solution Approach 1:
The system merges the direct clinical goal approach with the knowledge-based approach. User-defined clinical goals are combined with predictions from the trained knowledge-based model, allowing flexible goal definition while ensuring that unspecified factors are not treated as ambivalent but rather guided by the trained model's understanding of optimal treatment patterns.
3Measurement precision
If clinical goals are combined with knowledge based models, then accurate and flexible treatment planning is achieved, but the complexity of properly combining these approaches increases
Solution Approach 1:
The system uses an intermediary trained model that acts as a bridge between clinical goals and treatment optimization. The model translates clinical goals into predictive dosimetric outcomes, simplifying the combination process by providing a standardized interface between user-defined goals and the optimization algorithm, thereby reducing the perceived complexity while maintaining accuracy.
Data Source
AI summary
A treatment planning apparatus includes: a modeler configured to obtain a model definition, wherein the model definition comprises a first quality metric of a first clinical goal; and a treatment planner having: a model trainer configured to obtain a set of existing treatment plans following desired clinical practice, and to perform model training to obtain a trained model based on the existing treatment plans and the first quality metric of the first clinical goal; an objective generator configured to generate a cost function based on the trained model; and an optimizer configured to determine a treatment plan based on the cost function.