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D Silvern

Publications and source records attributed to D Silvern.

2 recordsLinked to original sources

Postimplantation dosimetric analysis of permanent transperineal prostate implantation: improved dose distributions with an intraoperative computer-optimized conformal planning technique.

PURPOSE: To compare the target coverage and dose to normal tissues after I-125 transperineal permanent implantation (TPI) of the prostate in 90 patients treated with one of three different transperineal techniques. METHODS AND MATERIALS: Detailed postimplant dosimetric evaluations of permanent I-125 implantation procedures were performed on 30 consecutive patients treated between 1995-1996 who underwent TPI using a preplanning CT-based technique, on 30 consecutive patients treated in 1997-1998 who underwent an ultrasound-guided approach with intraoperative determination of seed distribution based on an I-125 nomogram, and on 30 consecutive patients in 1998-1999 who underwent TPI with intraoperative computer-based 3-dimensional conformal optimization. For all three techniques, postimplant CT scans were obtained 4-6 hours after TPI. Dosimetric parameters included V(100), V(90), V(150), D(100), D(90), D(80), as well as maximal and average doses to the urethra and rectal wall. These parameter outcomes are reported as a percentage of the prescription dose. RESULTS: The intraoperative 3D-optimized technique (I-3D) provided superior target coverage with the prescription dose for all dosimetric variables evaluated compared to the other treatment techniques. The median V(100), V(90), and D(90) values for the I-3D technique were 96%, 98%, and 116%, respectively. In contrast, the V(100), V(90), and D(90) values for the CT preplan and ultrasound manual optimization approaches were 86%, 89%, and 88%, respectively and 88%, 92%, and 94%, respectively (I-3D versus other techniques: p < 0.001). The superior target coverage with the I-3D technique was also associated with a higher cumulative implant activity required by the optimization program. A multivariate analysis determined that the treatment technique (I-3D versus other approaches) was an independent predictor of improved target coverage for each parameter analyzed (p < 0.001). In addition, higher cumulative implant activities and smaller prostate target volumes were independent predictors of improved target coverage. The maximum and average urethral doses were significantly lower with the I-3D technique compared to the other techniques; a modest increase in the average rectal dose was also observed with this approach. CONCLUSION: Three-dimensional intraoperative computer optimized TPI consistently provided superior target coverage with the prescription dose and significantly lower urethral doses compared to two other techniques used. These data provide proof-of-principle that improved therapeutic ratios can be achieved with the integration of more sophisticated intraoperative planning for TPI and may potentially have a profound impact on the outcome of patients treated with this modality.

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Treatment planning for brachytherapy: an integer programming model, two computational approaches and experiments with permanent prostate implant planning.

An integer linear programming model is proposed as a framework for optimizing seed placement and dose distribution in brachytherapy treatment planning. The basic model involves using 0/1 indicator variables to describe the placement or non-placement of seeds in a prespecified three-dimensional grid of potential locations. The dose delivered to each point in a discretized representation of the diseased organ and neighbouring healthy tissue can then be modelled as a linear combination of the indicator variables. A system of linear constraints is imposed to attempt to keep the dose level at each point to within specified target bounds. Since it is physically impossible to satisfy all constraints simultaneously, each constraint uses a variable to either record when the target dose level is achieved, or to record the deviation from the desired level. These additional variables are embedded into an objective function to be optimized. Variations on this model are discussed and two computational approaches--a branch-and-bound algorithm and a genetic algorithm--for finding 'optimal' seed placements are described. Results of computational experiments on a collection of prostate cancer cases are reported. The results indicate that both optimization algorithms are capable of producing good solutions within 5 to 15 min, and that small variations in model parameters can have a measurable effect on the dose distribution of the resulting plans.

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