Predictive Dose-Volume Modeling for Radiotherapy Planning
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Solution Overview
Problem
Current radiation therapy planning for intensity modulated radiation therapy (IMRT) is subjective and time-consuming, relying on manual adjustments and lacking objective criteria for optimizing dose distribution to tumors while sparing nearby healthy tissues, leading to variability in treatment quality based on planner experience and time.
Innovation Solution
A system and method that develop predictive dose-volume relationships using three-dimensional representations of planning target volumes and organs-at-risk, based on correlations between dose-volume relationships and boundary distance vectors, to automate the optimization process and improve treatment planning accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional treatment planning software is used with manual optimization, then the treatment plan can be customized to individual patient needs, but the process is subjective and time-consuming with variability based on planner experience
Solution Approach 1:
The patent creates a digital twin or virtual model of the patient's anatomy using 3-D imaging data, allowing the treatment plan to be simulated and optimized in the virtual model before implementation. This copying approach enables automated optimization algorithms to work on the digital representation, reducing manual planning time while maintaining dosimetric accuracy through iterative dose distribution calculations.
Solution Approach 2:
The patent replaces manual mechanical optimization processes with automated computer-based optimization algorithms. The system uses computational methods to automatically adjust beam angles, intensities, and shapes to achieve optimal dose distribution, eliminating the subjectivity and time consumption associated with manual planner adjustments while maintaining or improving dosimetric precision.
2Reliability
If manual optimization is used to spare organs-at-risk, then the planner can apply clinical judgment, but the process lacks objective criteria and depends on planner experience
Solution Approach 1:
The patent implements feedback mechanisms where the optimization system continuously monitors dose distribution to organs-at-risk and automatically adjusts treatment parameters to meet predefined dosimetric constraints. The system provides real-time feedback on dose accumulation and constraint violations, enabling automated iterative optimization that improves reliability and consistency of organ sparing without requiring complex manual judgment at each step.
Solution Approach 2:
The patent transforms the optimization process from subjective manual parameter adjustment to automated parameter optimization based on predefined dosimetric objectives and constraints. The system automatically modifies beam parameters (angles, intensities, shapes) to satisfy mathematical optimization criteria, providing objective and consistent organ-at-risk sparing that eliminates variability based on individual planner experience or judgment.
3Productivity
If case-by-case optimization is performed, then each patient receives a customized plan, but the process requires significant planner time and resources
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing optimal beam parameters and dose distribution patterns from the optimization algorithm before final treatment delivery. The system pre-optimizes the treatment plan in the virtual model, establishing dose-volume relationships and beam configurations that can be directly implemented, thereby improving productivity by reducing the time needed for final plan verification and preparation while maintaining dosimetric precision.
Data Source
AI summary
Embodiments develop a predictive dose-volume relationships for a radiation therapy treatment is provided. A system includes a memory area for storing data corresponding to a plurality of patients, wherein the data comprises a three-dimensional representation of the planning target volume and one or more organs-at-risk. The data further comprises an amount of radiation delivered to the planning target volume and the one or more organs-at-risk. The system further includes one or more processors programmed to access, from the memory area, the data and to develop a model that predicts dose-volume relationships using the three-dimensional representations of the planning target volume and the one or more organs-at-risk. The model is being derived from correlations between dose-volume relationships and calculated minimum distance vectors between discrete volume elements of the one or more organs-at-risk and a boundary surface of the planning target volume.