Radiotherapy Plan Generation Using Interactive Language Models
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Solution Overview
Problem
Existing radiotherapy treatment planning systems require extensive iterative interactions and complex computational models, necessitating significant user input and training datasets, which are inefficient and costly.
Innovation Solution
A machine learning language processing model is employed to generate radiotherapy treatment plans interactively, reducing repetitive adjustments by allowing clinicians to input patient attributes, which are then processed to optimize treatment plans using a radiotherapy plan optimizer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex computer models are used to generate radiotherapy treatment plans, then the quality and accuracy of treatment plans are improved, but the computational cost and time required are increased
Solution Approach 1:
The system performs preliminary actions by pre-processing patient data (CT scans, MRI images, patient history) and pre-defining treatment protocols and constraints before actual plan generation. This preparation work is done once and reused across multiple plan iterations, reducing the computational burden during interactive planning and accelerating subsequent plan generation while maintaining accuracy
Solution Approach 2:
The system creates and uses templates representing typical treatment plans and patient scenarios. These templates serve as reusable copies that can be quickly adapted to new cases, avoiding the need to generate plans from scratch each time. The templates capture proven treatment approaches that can be replicated and modified, reducing computational time while preserving treatment quality
2Reliability
If extensive training datasets are used to train machine learning models, then the model's performance and reliability are improved, but the training cost and computational resources are increased
Solution Approach 1:
The system extracts and utilizes only the most critical and relevant features from patient data (such as tumor characteristics, organ-at-risk locations, and key anatomical landmarks) rather than processing entire datasets. This feature extraction approach reduces the dimensionality of training data while retaining the essential information needed for reliable plan generation, lowering training costs without sacrificing model performance
Solution Approach 2:
The system transforms complex medical imaging data and clinical parameters into simplified representations that are more efficient for machine learning processing. By changing the parameter representation (e.g., converting detailed images to key landmark coordinates, transforming free-text protocols into structured constraints), the system reduces training data complexity while maintaining the information necessary for reliable treatment planning
3Measurement precision
If multiple iterative interactions are required between clinicians and computer models, then the quality of treatment plans is improved, but the operational complexity and user burden are increased
Solution Approach 1:
The system performs self-correction and self-optimization by automatically adjusting treatment plans based on embedded clinical guidelines, constraints, and quality criteria. When a plan is generated, the system autonomously evaluates it against predefined standards and makes necessary modifications without requiring multiple manual review cycles, reducing user burden while maintaining or improving plan quality
Solution Approach 2:
The system implements automated feedback mechanisms that provide clinicians with immediate, actionable information about plan quality and compliance with treatment goals. Rather than requiring multiple iterative rounds of manual adjustment, the system delivers structured feedback on dose distribution, organ-at-risk exposure, and goal achievement, enabling clinicians to make informed decisions with fewer interactions
4Measurement precision
If specific and exact parameters are required as input, then the precision of treatment plan generation is improved, but the ease of data collection and input is worsened
Solution Approach 1:
The system replaces manual data entry and parameter specification with automated extraction from electronic health records, imaging systems, and treatment protocols. Machine learning algorithms automatically identify and extract relevant parameters (tumor dimensions, organ locations, dose constraints) from unstructured clinical data, converting them into the precise numerical inputs required for treatment planning without requiring manual clinician input
Data Source
AI summary
Embodiments described herein provide for radiotherapy treatment plan generation using a machine learning language processing model. A processor can present a user interface providing an interaction interface between a user and a machine learning language processing model. The processor can receive a first input comprising a first patient attribute of a patient. The processor can execute the machine learning language processing model using the first patient attribute as an input to generate a response requesting a second patient attribute. The processor can present the response requesting the second patient attribute of the patient. The processor can receive a second input comprising the second patient attribute of the patient. The processor can transmit the first patient attribute of the patient and the second patient attribute of the patient to a radiotherapy plan optimizer. The radiotherapy plan optimizer can be configured to generate a radiotherapy treatment plan for the patient.