Treatable Beam Sectors for Faster Radiation Therapy Planning
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Solution Overview
Problem
Conventional radiation therapy planning systems are inefficient and computationally intensive, often violating clinical best practices due to a lack of contextual consideration in beam geometry optimization, leading to time-consuming iterations and suboptimal treatment plans.
Innovation Solution
A system utilizing a machine learning model to predict treatable sectors based on anatomical site, laterality, and tumor location, limiting the search space for plan optimizers to achieve efficient and clinically acceptable radiation therapy treatment plans without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional plan optimizers use iterative trial-and-error process to optimize radiation parameters, then the radiation therapy treatment plan meets predefined dose criteria, but the computing resources required and time to produce results increase substantially
Solution Approach 1:
The system performs preliminary segmentation of the patient's anatomy into treatable sectors before the optimization process. By pre-identifying safe beam entry regions based on anatomical structures and organs at risk, the system eliminates the need for iterative trials of unsafe configurations, directly improving both reliability and productivity.
Solution Approach 2:
The patient's anatomical volume is segmented into multiple treatable sectors with distinct entry/exit boundaries. This segmentation allows the optimization algorithm to work with predefined safe regions rather than searching through all possible beam paths, reducing computational burden while maintaining dose criteria compliance.
2Reliability
If beam geometry optimization algorithm places beams through regions that minimize the objective function mathematically, then the dose distribution satisfies clinical goal limits, but the treatment plan violates clinical best practices
Solution Approach 1:
The system preemptively identifies and marks regions that would be clinically unacceptable (such as beam paths through healthy organs) before the optimization process begins. By pre-defining treatable sectors that exclude these harmful paths, the optimization algorithm is constrained to search only within clinically acceptable regions, preventing violations of best practices while maintaining mathematical optimality.
Solution Approach 2:
Different regions of the patient's anatomy are assigned different qualities or properties - specifically, certain angular sectors are marked as treatable while others are marked as non-treatable based on local anatomical considerations. This allows the optimization to achieve mathematical goals while respecting local clinical constraints in each anatomical region.
3Ease of operation
If the algorithm iterates to revise the treatment plan after finding mathematically optimized but medically unacceptable beam paths, then clinically acceptable plans are achieved, but the process becomes time-consuming and computationally inefficient
Solution Approach 1:
By performing the clinically critical action of identifying safe beam paths before the mathematical optimization begins, the system eliminates the need for subsequent iterative revisions. The preliminary segmentation into treatable sectors ensures that all subsequent optimization iterations remain within clinically acceptable bounds, preventing time-wasting revision cycles.
Data Source
AI summary
Disclosed herein are methods and systems for calculating radiation therapy treatment plan (RTTP) including a method that comprises monitoring, by a processor, a treatment plan optimizer computer model to identify a set of treatment plans, where the treatment plan optimizer computer model ingests a set of medical images and predicts each treatment plan comprising a respective range of angles for treatment beam entry; generating, by the processor, a training dataset comprising the monitored data; and training, by the processor, a machine learning model using the training dataset to predict a new range of angles for treatment beam entry for a new patient by ingesting new patient data of the new patient.