Dose-Volume Histogram Anomaly Detection in Radiation Treatment Plans
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Solution Overview
Problem
Existing radiation treatment plans lack comprehensive evaluation methods to ensure optimal discrimination between target volumes and adjacent tissues, organs, or organs-at-risk, leading to potential collateral damage during energy-based treatments.
Innovation Solution
A control circuit is used to generate and assess dose volume histogram information, identifying anomalies in radiation treatment plans by comparing them to reference information, allowing for interactive adjustment of parameters to optimize the plan and minimize adverse effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated optimization processes are used to generate radiation treatment plans, then treatment planning efficiency is improved, but the ability to detect anomalies and ensure optimal discrimination between target volumes and adjacent tissues deteriorates
Solution Approach 1:
The system implements automated anomaly detection that provides feedback on treatment plan quality by comparing DVH parameters against reference ranges and constraints. This feedback mechanism allows the optimization process to be monitored and adjusted, ensuring that automated planning maintains both efficiency and reliability through continuous validation.
Solution Approach 2:
The system performs preliminary anomaly detection and validation before final treatment plan approval. By pre-establishing reference DVH ranges and constraints for different anatomical sites, the system proactively identifies potential issues in automated treatment plans before they are implemented, maintaining reliability while preserving automated efficiency.
2Reliability
If comprehensive evaluation methods are implemented to ensure optimal discrimination between target volumes and adjacent tissues, then treatment safety is improved, but computational complexity and time requirements worsen
Solution Approach 1:
The system extracts and monitors only the most critical DVH parameters and constraints that are essential for treatment safety, rather than evaluating all possible parameters. By focusing on key metrics such as dose to critical organs and target coverage, the system maintains comprehensive safety evaluation while reducing computational time and complexity.
Solution Approach 2:
The system dynamically adjusts evaluation parameters and thresholds based on the specific treatment plan and anatomical site. By adapting reference DVH ranges and constraint values to match the particular clinical scenario, the system achieves comprehensive safety evaluation efficiently without using a fixed, overly complex evaluation framework for all cases.
3Measurement precision
If manual evaluation of treatment plans is performed to ensure optimal discrimination, then detection precision is improved, but productivity deteriorates
Solution Approach 1:
The system implements self-service automated anomaly detection that independently evaluates treatment plans without requiring manual reviewer intervention for every case. The automated system uses pre-established reference ranges and constraints to detect anomalies, maintaining high detection precision while significantly increasing treatment planning throughput by eliminating repetitive manual evaluation.
Solution Approach 2:
The system acts as an intermediary between automated treatment planning and final clinical approval by providing automated anomaly detection and validation. This intermediary layer maintains the precision of manual evaluation by using clinically validated reference ranges while enabling higher productivity by automating the initial screening process, allowing manual reviewers to focus only on complex or borderline cases.
Data Source
AI summary
A control circuit accesses a radiation treatment plan for a given patient. The control circuit then generates dose volume histogram information as a function of the radiation treatment plan and automatically assesses the dose volume histogram information to identify any anomalous results. Generating that information can comprise, at least in part and for example, generating at least one dose volume histogram curve. The latter may comprise generating at least one dose volume histogram curve for each of a plurality of different patient structures (such as one or more treatment volumes and/or one or more organs-at-risk).