A genetic algorithm for the optimization of prostate implants.
A genetic algorithm (GA) is presented for the optimization of template- and ultrasound-guided prostate implants. The end points for optimization are incorporated in an objective function of separable cardinal utility terms. As an application of the GA, the minimum 103Pd total source strength required to deliver a given dose was correlated with the average dimension for prostate implants carried out under the current template and seed spacing protocols. Significant improvements in quality were observed, in terms of both the minimum peripheral dose and tumor cell surviving fractions, when GA-optimized implants were compared to the corresponding unoptimized implants for given target volumes. In addition, numerical simulation of source displacements indicates that the dosimetric and radiobiologic advantages of GA optimization can tolerate a reasonable level of seed placement uncertainties observed clinically. In summary, the GA application provides an automated design strategy for prostate implant planning, and at the same time affords the potential for systematic optimization of a set of end points that can sustain practical variations.
Yu
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Department of Radiation Oncology, University of Rochester Medical Center, New York 14642-8674, USA.
Schell
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MC
D000465:Algorithms; D055592:Biophysical Phenomena; D001703:Biophysics; D001918:Brachytherapy / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008954:Models, Biological; D008957:Models, Genetic; D010165:Palladium / Q000627:therapeutic use; D011471:Prostatic Neoplasms / Q000532:radiotherapy*; D011868:Radioisotopes / Q000627:therapeutic use; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data
D011868:Radioisotopes; D010165:Palladium