Adaptive Radiotherapy Planning with Deep Learning Segmentation
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Solution Overview
Problem
Conventional radiotherapy treatment planning is time and labor intensive, requiring skilled professionals for manual segmentation and lacks consensus on structure delineation, leading to uncertainty in target volume and healthy structure radiation dose.
Innovation Solution
Utilizing a deep learning engine with multiple processing pathways to process medical image data at different resolution levels for automatic segmentation, dose prediction, and treatment delivery data generation, thereby automating and improving the efficiency of radiotherapy treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation and treatment planning is performed by skilled professionals, then structure delineation accuracy is improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The system performs preliminary automatic segmentation and treatment plan generation using deep learning models before clinical review. This preliminary action provides a ready-to-review draft plan, significantly reducing the time clinicians need to spend on initial plan creation while maintaining accuracy through subsequent professional verification and adjustment.
Solution Approach 2:
The deep learning-based automatic planning system acts as an intermediary between raw imaging data and final treatment plans. It processes images through multiple processing pathways to generate structure delineations and dose predictions, serving as a bridge that automates routine tasks while allowing clinicians to focus on complex decision-making and final plan approval.
2Adaptability or versatility
If manual treatment planning is used, then flexibility in handling complex cases is maintained, but productivity and throughput decrease
Solution Approach 1:
The system implements a dynamic planning workflow that adapts to case complexity. For standard cases, it operates in fully automatic mode with rapid plan generation. For complex cases, it provides semi-automatic mode with interactive tools allowing clinicians to modify plans. This dynamic approach maintains flexibility while maximizing throughput for routine cases.
Solution Approach 2:
The treatment planning process is segmented into distinct automated and manual components. The deep learning model handles routine segmentation, dose calculation, and plan optimization automatically. Clinicians intervene only for complex structures, unusual anatomies, or plan modifications. This segmentation of tasks enables high throughput for standard cases while preserving flexibility for complex scenarios.
3Reliability
If conventional radiotherapy planning methods are used, then treatment personalization is achieved, but consistency and reproducibility across different planners vary
Solution Approach 1:
The system standardizes treatment planning by controlling and optimizing key parameters such as dose constraints, beam angles, and structure delineation criteria through the deep learning model. This parameter standardization ensures consistent plan quality across different patients and clinicians while maintaining the ability to personalize treatment through parameter adjustment for specific clinical scenarios.
Solution Approach 2:
The system incorporates feedback mechanisms where automatically generated plans are reviewed and adjusted by clinicians, and these corrections are fed back to refine the deep learning model. This feedback loop continuously improves plan consistency and reliability while allowing customization for individual patient needs, reducing variability between different planners over time.
Data Source
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AI summary
A computer system configured to perform adaptive radiotherapy treatment planning using a deep learning engine is disclosed. The computer system comprises: a processor and a non-transitory computer-readable medium having stored thereon instructions that, when executed by the processor, cause the processor to: obtain treatment image data associated with a first imaging modality, wherein the treatment image data is acquired during a treatment phase of a patient, and planning image data associated with a second imaging modality is acquired prior to the treatment phase to generate a treatment plan for the patient; and in response to determination that an update of the treatment plan is required, transform the treatment image data associated with the first imaging modality to generate transformed image data associated with the second imaging modality; and process, using the deep learning engine, the transformed image data to generate output data for updating the treatment plan.