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- W2896109211 abstract "Treatment planning in robotic radiosurgery is accomplished via inverse planning, wherein a set of clinical objectives is specified a priori, and an optimization produces a Pareto-optimal patient-specific treatment plan. The physician subsequently reviews the treatment plan to ascertain whether the result of the optimization is clinically acceptable. Broadly, treatment machine parameters are optimized such that the planning target volume (PTV), which consists of the tumor plus a chosen geometric margin, receives a given prescription dose, and the surrounding organs-at-risk (OARs) receive as low a radiation dose as possible. In the present study, the results obtained with the authors' evolutionary optimizer are compared directly against those obtained with the L-BFGS algorithm employed in the pre-clinical release of the next version of the industry-standard treatment planning software (the software is not yet a commercially available product.) Five distinct patient CT data sets were chosen, and five treatment plans were generated with each algorithm for each of the five sites: prostate, lung, tonsil, prostate plus pelvic nodes, and adrenal gland. Metrics for all treatment plans were compared, and in all instances, the results obtained with evolutionary optimization were either non-inferior or superior to those obtained with L-BFGS optimization." @default.
- W2896109211 created "2018-10-26" @default.
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- W2896109211 date "2018-07-01" @default.
- W2896109211 modified "2023-09-25" @default.
- W2896109211 title "Evolutionary Optimization in Robotic Radiotherapy Treatment Planning: A Comparison of Clinical Results with L-BFGS Optimization" @default.
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- W2896109211 doi "https://doi.org/10.1109/cec.2018.8477651" @default.
- W2896109211 hasPublicationYear "2018" @default.
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