Rapid SAR optimization for hyperthermic oncology: combining multi-goal optimization and time-multiplexed steering for hotspot suppression.

Healthy tissue hotspots are a main limiting factor in administering deep hyperthermia cancer therapy. We propose an optimization scheme that uses time-multiplexed steering (TMPS) among minimally correlated (nearly) Pareto-optimal solutions to suppress hotspots without reducing tumor heating. Furthermore, tumor heating homogeneity is maximized, thus reducing toxicity and avoiding underexposed tumor regions, which in turn may reduce recurrence. The novel optimization scheme combines random generation of steering parameters with local optimization to efficiently identify the set of (Pareto-) optimal solutions of conflicting optimization goals. To achieve simultaneous suppression of hotspots, multiple steering parameter configurations with minimally correlated hotspots are selected near the Pareto front and combined in TMPS. The performance of the novel scheme was compared with that of a multi-goal Genetic Algorithm for a range of simulated treatment configurations involving a modular applicator heating a generic tumor situated in the bladder, cervix, or pelvic bone. SAR cumulative histograms in tumor and healthy tissue, as well as hotspot volumes are used as metrics. Compared to the non-TMPS optimization, the proposed scheme was able to reduce the peak temperature in healthy tissue by 0.2 °C-1.0 °C (a thermal dose reduction by at least 26%) and, importantly, the hotspot volume above 42 °C in healthy tissue by 41%-86%. At the same time, tumor heating homogeneity was maintained or improved. The extremely rapid optimization (5 s for TMPS part, on a standard PC) permits closed-loop treatment reoptimization during treatment administration, and empowers physicians with a selection of optimal treatment scenarios reflecting different weighting of conflicting treatment goals.