Dose Estimation Model for Radiotherapy Planning
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Solution Overview
Problem
Current radiotherapy treatment planning faces challenges in accurately delivering radiation doses to tumors while minimizing exposure to healthy structures, due to the complexity of tumor and organ proximity, leading to inaccuracies in dose estimation and potential health complications.
Innovation Solution
A dose estimation model is developed that incorporates treatment planning trade-offs and sub-optimal characteristic identification, using machine learning algorithms to transform patient geometry and treatment planning data into radiation dose predictions, allowing for more flexible and accurate treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional radiation dose delivery methods are used, then the radiation can be administered to the tumor, but the radiation cannot effectively discriminate between tumor and proximal healthy structures, leading to inaccurate dose estimation
Solution Approach 1:
The patent introduces a dose estimation model as an intermediary component that bridges the gap between treatment plan parameters and actual radiation dose delivery. This model transforms treatment plan data into accurate dose predictions without requiring direct complex measurements, thereby improving dose estimation accuracy while managing system complexity through a dedicated predictive layer
Solution Approach 2:
The patent replaces traditional mechanical/physical measurement systems with a computational dose estimation model. Instead of relying on complex physical dosimetry equipment and manual calculations, the system uses machine learning algorithms to predict radiation dose distribution, substituting mechanical measurement approaches with intelligent computational methods
2Reliability
If radiation dose is increased to ensure tumor control, then tumor control improves, but the dose to proximal healthy structures also increases, causing potential health complications
Solution Approach 1:
The patent applies local quality by training the dose estimation model to predict dose distribution with different accuracy requirements for different regions. The model learns to provide high-prediction accuracy for tumor regions to ensure reliable tumor control, while simultaneously providing accurate dose predictions for healthy structures to minimize radiation exposure, allowing different quality levels for different spatial locations
Solution Approach 2:
The patent uses partial action by focusing the dose estimation model's predictive capability on critical regions only. Rather than attempting to optimize every aspect of the treatment plan, the model concentrates computational resources on accurately predicting dose in the tumor and immediately adjacent healthy structures, achieving sufficient accuracy for clinical decision-making without requiring complete optimization of all treatment parameters
3Adaptability or versatility
If training data includes sub-optimal treatment plans, then the model can learn from diverse scenarios, but the dose estimation accuracy deteriorates due to inclusion of poor quality treatment plans
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the training data selection criteria based on the specific treatment scenario being modeled. The system modifies parameters such as data sampling rates, model complexity, and training data composition to match the characteristics of different treatment scenarios, allowing the model to maintain high accuracy across diverse applications while adapting to specific clinical contexts
Data Source
AI summary
Example methods for radiotherapy treatment planning are provided. One example method may include obtaining training data that includes multiple treatment plans associated with respective multiple past patients; and processing the training data to determine, from each of the multiple treatment plans, at least one of the following: first data associated with a particular past patient or a radiotherapy system for delivering radiotherapy treatment to the particular past patient, second data associated with treatment planning trade-off selected for the particular past patient and third data associated with radiation dose for delivery to the particular past patient. The method may also comprise: based on at least one of the first data, the second data and the third data, identifying one or more sub-optimal characteristics associated with the training data, obtaining improved training data and generating a dose estimation model based on the improved training data.