Radiation Therapy Plan Selection System Using Database Matching
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Solution Overview
Problem
The process of creating radiation treatment plans for cancer patients is time-consuming and labor-intensive, requiring multiple iterations and involving multiple medical personnel, which increases the time from diagnosis to treatment.
Innovation Solution
A system and method that utilize a processor and database to identify and recommend previously planned radiation treatments based on patient data features, using machine learning and pattern matching techniques to facilitate the creation of efficient treatment plans, reducing the need for extensive iterations and labor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple medical personnel manually create radiation treatment plans through iterative cycles, then treatment plan quality and customization are improved, but the time required from diagnosis to treatment increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing patient data (CT scans, MRI, PET images) and pre-calculating treatment parameters before the actual treatment planning is needed. The automated system prepares initial treatment plans, dose distributions, and beam configurations in advance, which can then be quickly reviewed and adjusted by medical personnel, significantly reducing the time from diagnosis to treatment while maintaining quality standards.
Solution Approach 2:
The system creates copies of treatment plans from previous successful cases with similar patient characteristics and tumor types. By copying and adapting proven treatment protocols rather than creating plans from scratch, the system maintains high treatment quality through established medical standards while dramatically reducing the time required for plan development and iteration.
2Manufacturing precision
If multiple iterations of treatment constraints are changed by medical personnel, then treatment plan optimization is improved, but labor requirements and process complexity increase
Solution Approach 1:
The system performs self-service by automatically optimizing treatment plans through algorithmic calculations of dose distributions, beam angles, and treatment constraints. The automated system independently adjusts parameters to meet clinical objectives, reducing the need for multiple manual iteration cycles and complex human coordination, thereby simplifying the overall process while maintaining optimization quality.
Solution Approach 2:
The system replaces the mechanical process of manual plan creation and iteration with an automated computational system. Instead of medical personnel manually adjusting treatment constraints through repeated cycles, the system uses software algorithms to automatically optimize treatment parameters, substituting human manual operations with automated computational processes that reduce both labor requirements and process complexity.
3Measurement precision
If extensive manual planning and iteration are performed, then treatment plan accuracy is improved, but productivity and efficiency decrease
Solution Approach 1:
The system maintains treatment plan accuracy by systematically varying and optimizing key parameters such as beam energy, beam angles, dose rates, and treatment fractions. The automated system efficiently explores parameter spaces to find optimal configurations that meet clinical accuracy requirements, achieving the same level of precision as manual methods but at much higher speeds and with greater consistency.
Solution Approach 2:
The system ensures continuous optimization of treatment plans through automated algorithms that continuously adjust parameters until optimal solutions are found. Rather than relying on discrete manual iteration cycles with gaps between reviews, the system performs continuous computational optimization, maintaining useful action throughout the planning process to achieve both high accuracy and high productivity.
Data Source
AI summary
A system for facilitating creation of a patient treatment plan includes components configured to receive at least one feature associated with patient data and search a database of previously planned radiation treatments to identify one or more matching plans from the database based on the at least one feature. Parameters corresponding to the identified matching treatment plans may be used to facilitate creation of the patient treatment plan.