Knowledge-Based Radiation Treatment Planning With DVH Estimation
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Solution Overview
Problem
Existing radiation treatment plans often fail to discriminate between target areas and adjacent healthy tissues, requiring careful administration to minimize harm to healthy organs, and optimization techniques rely heavily on user expertise, leading to sub-optimal results.
Innovation Solution
A control circuit trains on a model using anonymized and formatted pre-existing vetted radiation treatment plans to develop estimates for a personalized treatment plan, incorporating DVH estimation models to optimize radiation delivery, with user-adjustable objectives and validation checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional optimization techniques are used, then treatment plans can be generated with user expertise, but the results are often sub-optimal and require repeated interaction
Solution Approach 1:
The system enables self-service by allowing the optimization algorithm to automatically learn from and adapt to user preferences through machine learning. The system serves itself by continuously improving its optimization capabilities without requiring manual reconfiguration, thereby reducing the need for repeated user interactions while maintaining or improving treatment plan quality.
Solution Approach 2:
The system implements feedback mechanisms where user corrections and adjustments to treatment plans are captured and used to retrain the machine learning model. This feedback loop allows the system to learn from user expertise and improve its future recommendations, reducing the need for repeated manual adjustments while enhancing treatment plan reliability.
2Productivity
If automated incremental optimization is used, then treatment plans can be generated efficiently, but the optimization may not achieve the best possible results
Solution Approach 1:
The system performs preliminary action by pre-training the machine learning model on extensive datasets of treatment plans and optimization outcomes before actual use. This preliminary training enables the system to make high-quality optimization decisions more quickly during actual treatment planning, balancing speed and quality by doing the heavy computational lifting in advance.
Solution Approach 2:
The system replaces the traditional mechanical incremental optimization process with a machine learning-based intelligent system. Instead of relying on step-by-step automated adjustments, the ML model directly predicts optimal treatment parameters, significantly improving both the speed and quality of optimization by substituting computational intelligence for brute-force mechanical iteration.
3Reliability
If experienced users set optimization objectives, then treatment plans can be optimized, but the process is tethered to iterative interaction and user availability
Solution Approach 1:
The system creates a digital copy of user expertise by training the machine learning model on historical data representing experienced users' optimization decisions and preferences. This copy encapsulates user knowledge and can be deployed to generate treatment plans without requiring the actual users to be present, eliminating iterative interaction delays while maintaining the accuracy of expert-level objective setting.
Solution Approach 2:
The system enables self-service by allowing the machine learning model to automatically set optimization objectives based on learned patterns from training data. The system serves itself by autonomously determining treatment plan parameters without requiring continuous user intervention, thereby eliminating time losses associated with iterative user interaction while maintaining reliable objective setting through embedded expert knowledge.
Data Source
AI summary
A control circuit accesses information regarding a plurality of pre-existing vetted radiation treatment plans for a variety of patients and uses that information to train at least one model (such as a dose volume histogram estimation model). The control circuit then uses that model to develop estimates for a radiation treatment plan for a particular patient. The control circuit can then use those estimates to develop a candidate radiation treatment plan.