Redi Poni, Esra Neufeld, Myles Capstick, Stephan Bodis, and Niels Kuster, International Journal of Hyperthermia 2022, Volume 39, Issue 1, pp. 758–771, online 2 June 2022; doi: 10.1080/02656736.2022.2080284
The generation of hotspots in healthy tissues is a main limiting factor in the administration of deep hyperthermia cancer therapy. In our study, we developed an optimization scheme that uses time-multiplexed steering (TMPS) among minimally correlated Pareto- and nearly Pareto-optimal solutions to suppress hotspots without reducing tumor heating. Furthermore, the new scheme allows tumor heating homogeneity to be maximized, thereby reducing toxicity and avoiding underexposure of tumor regions, which in turn may reduce recurrence of the cancer. The novel optimization scheme combines random generation of steering parameters with local optimization to efficiently identify the set of Pareto- and nearly 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 by means of 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 that heats a generic tumor situated in the bladder, cervix, or pelvic bone. Specific absorption rate (SAR) cumulative histograms in tumor and healthy tissue as well as hotspot volumes were used as metrics for the optimization. Compared to the non-TMPS optimization, the proposed scheme was able to reduce the peak temperature in healthy tissue by 0.2–1.0°C — corresponding to a reduction in the thermal dose of at least 26% — and, importantly, the volume of the hotspot reaching 42°C in healthy tissue was reduced by 41–86%. At the same time, the homogeneity of tumor heating was maintained or improved. Optimization can be performed extremely rapidly — 5 s for the TMPS part on a standard PC — which permits treatment reoptimization to be performed closed-loop during treatment administration, empowering physicians to choose from a selection of optimal treatment scenarios the reflect the different weighting of conflicting treatment goals.
The scientific and technical impact of the study can be summarized as: