Rapid SAR optimization for hyperthermic oncology: combining multi-goal optimization and time-multiplexed steering for
Redi Poni1,2, Esra Neufeld1,2, Myles Capstick2
1Department of Information Technology and Electrical Engineering, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland.
Summary
This study introduces a new method for deep hyperthermia cancer therapy that suppresses dangerous hotspots in healthy tissue while maintaining effective tumor heating. This approach improves treatment safety and efficacy, potentially reducing cancer recurrence.
Area of Science:
- Oncology
- Medical Physics
- Biomedical Engineering
Background:
- Deep hyperthermia cancer therapy is limited by hotspots in healthy tissues.
- Minimizing these hotspots is crucial for safe and effective treatment delivery.
- Tumor heating homogeneity is key to reducing toxicity and preventing recurrence.
Purpose of the Study:
- To propose and evaluate a novel optimization scheme for deep hyperthermia.
- To suppress healthy tissue hotspots without compromising tumor heating.
- To maximize tumor heating homogeneity and reduce treatment toxicity.
Main Methods:
- Developed a time-multiplexed steering (TMPS) optimization scheme.
- Combined random parameter generation with local optimization to find Pareto-optimal solutions.
- Compared TMPS with a multi-goal Genetic Algorithm using simulated treatment scenarios.
Main Results:
- TMPS reduced peak temperatures in healthy tissue by 0.2-1.0 °C.
- Significantly decreased hotspot volume (>42 °C) in healthy tissue by 41%-86%.
- Maintained or improved tumor heating homogeneity during treatment.
Conclusions:
- The TMPS scheme effectively suppresses healthy tissue hotspots in hyperthermia.
- Rapid optimization allows for closed-loop reoptimization during treatment.
- Physicians can select optimal treatment scenarios based on goal weighting.
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