Radiotherapy Treatment Selection System for Palliative Care
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Solution Overview
Problem
Current palliative radiotherapy treatment plans for advanced cancer patients are suboptimal due to limited clinician accuracy in predicting life expectancy and survival, leading to over-treatment or inadequate therapy, with existing guidelines failing to account for individual patient variability in physical, psychological, and emotional states, resulting in premature treatment discontinuation and increased side effects.
Innovation Solution
A computer-based system that calculates initial and final scores for radiotherapy treatment options based on patient-specific physiological and emotional state data, optimizing treatment plans to predict discontinuation likelihood and select the most beneficial regimen, reducing the number of treatment sessions and improving survival likelihood.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If general guidelines based on tumor stage and physical condition are used for treatment selection, then treatment decision process is simplified, but treatment accuracy and individualization deteriorate
Solution Approach 1:
The system transforms the treatment selection process by changing from static guideline-based parameters to dynamic, multi-dimensional parameters including physiological state, emotional state, life expectancy predictions, and toxicity metrics. This allows personalized treatment selection that maintains simplicity while improving accuracy through comprehensive patient-specific parameter assessment.
Solution Approach 2:
The computational system acts as an intermediary between general guidelines and individual patient characteristics. It processes multiple patient-specific parameters and provides personalized treatment recommendations, bridging the gap between simplified general guidelines and highly individualized treatment needs without requiring clinicians to manually integrate complex factors.
2Reliability
If more aggressive radiotherapy regimens are used to improve treatment outcomes, then tumor control may improve, but side effects severity and duration increase
Solution Approach 1:
The system incorporates toxicity metrics and quality of life assessments as feedback parameters in the treatment selection process. By evaluating predicted toxicities and patient-specific factors before treatment initiation, the system can select regimens that balance tumor control with acceptable side effect profiles, adjusting treatment intensity based on individual patient tolerance and response predictions.
Solution Approach 2:
The system applies different treatment intensities and approaches tailored to specific patient subgroups based on their physiological and emotional characteristics. Rather than applying uniform aggressive treatment to all patients, it customizes treatment quality and intensity to match individual patient needs, capabilities, and predicted responses.
3Device complexity
If clinician predictions of life expectancy are used for patient selection, then treatment planning is simplified, but prediction accuracy deteriorates
Solution Approach 1:
The system integrates multiple functions including life expectancy prediction, toxicity assessment, quality of life evaluation, and treatment optimization into a single comprehensive platform. This multi-functional approach maintains planning simplicity while improving prediction accuracy by combining multiple data sources and analytical methods rather than relying on single clinician estimates.
Data Source
AI summary
A non-transitory computer readable medium (26) stores instructions executable by at least one electronic processor (20) to perform a radiation therapy (RT) treatment decision method (100) that includes: calculating (102) an initial score (30) for a pre-interventional patient for each RT option of a set of RT options (32), wherein the initial score for each RT option is indicative of likelihood of discontinuation of RT in accordance with that RT option; displaying (104) the initial scores and, via a user interface (28), receiving a selection of at least one RT option from the set of RT options; for each selected RT option: optimizing (106) a RT plan (34) for the patient in accordance with the selected RT treatment option; computing (108) one or more toxicity metrics (36) for the optimized RT plan, and calculating (110) a final score (38) based on the one or more toxicity metrics for the optimized RT plan, the final score being indicative of likelihood of discontinuation of the RT treatment in accordance with the optimized RT plan; and displaying (112) the final score for the at least one selected RT option and, via the user interface, receiving a selection of a final RT option.