Biomedical subjects

Jake Van Dyk

Publications and source records attributed to Jake Van Dyk.

23 records · Page 2Linked to original sources

PubMed2003

A systematic study of imaging uncertainties and their impact on 125I prostate brachytherapy dose evaluation.

In order to calculate the dose distribution delivered by a prostate brachytherapy implant, the seed positions and prostate volume are normally identified on post-implant CT images. We have systematically considered the impact of uncertainties in contouring the prostate, seed localization, and visualization of all the seeds on the calculated dose distributions, dose-volume histograms, and predicted radiobiological outcome. This study was done for a collection of 27 clinical 125I prostate brachytherapy implants, performed at the London Regional Cancer Centre during our early adoption of this technique. For these clinical dose distributions, the median D90 was 76% of the prescription dose, or 110 Gy, and the median V90 was 80%. We calculated the changes in these dosimetric indices (D90 and V90) and radiobiological outcome (SF2 TCP) as a function of contouring uncertainty, seed localization uncertainty, inability to localize all of the seeds, and binary combinations of these three. The results are presented for a range of uncertainties, which allows the possible application of these results to a variety of imaging modalities that have differing spatial resolutions. We found that both contouring uncertainties and seed localization uncertainties had a large impact on the predicted radiobiological outcome, but that seed localization uncertainties of 6 mm had the largest impact on the dosimetric indices. We also found that the variability in both the predicted radiobiological and dosimetric outcome was largest for contouring uncertainties of 4-8 mm. We conclude that accounting for contouring uncertainties is crucial in accurately deducing the DVHs for post-implant prostate brachytherapy, and hence enabling valid correlation with ultimate clinical outcome.

Brachytherapy↗

PubMed2003

Limitations of a convolution method for modeling geometric uncertainties in radiation therapy. I. The effect of shift invariance.

Convolution methods have been used to model the effect of geometric uncertainties on dose delivery in radiation therapy. Convolution assumes shift invariance of the dose distribution. Internal inhomogeneities and surface curvature lead to violations of this assumption. The magnitude of the error resulting from violation of shift invariance is not well documented. This issue is addressed by comparing dose distributions calculated using the Convolution method with dose distributions obtained by Direct Simulation. A comparison of conventional Static dose distributions was also made with Direct Simulation. This analysis was performed for phantom geometries and several clinical tumor sites. A modification to the Convolution method to correct for some of the inherent errors is proposed and tested using example phantoms and patients. We refer to this modified method as the Corrected Convolution. The average maximum dose error in the calculated volume (averaged over different beam arrangements in the various phantom examples) was 21% with the Static dose calculation, 9% with Convolution, and reduced to 5% with the Corrected Convolution. The average maximum dose error in the calculated volume (averaged over four clinical examples) was 9% for the Static method, 13% for Convolution, and 3% for Corrected Convolution. While Convolution can provide a superior estimate of the dose delivered when geometric uncertainties are present, the violation of shift invariance can result in substantial errors near the surface of the patient. The proposed Corrected Convolution modification reduces errors near the surface to 3% or less.

Algorithms↗

PubMed2003

Limitations of a convolution method for modeling geometric uncertainties in radiation therapy. II. The effect of a finite number of fractions.

Convolution methods can be used to model the effect of geometric uncertainties on the planned dose distribution in radiation therapy. This requires several assumptions, including that the patient is treated with an infinite number of fractions, each delivering an infinitesimally small dose. The error resulting from this assumption has not been thoroughly quantified. This is investigated by comparing dose distributions calculated using the Convolution method with the result of Stochastic simulations of the treatment. Additionally, the dose calculated using the conventional Static method, a Corrected Convolution method, and a Direct Simulation are compared to the Stochastic result. This analysis is performed for single beam, parallel opposed pair, and four-field box techniques in a cubic water phantom. Treatment plans for a simple and a complex idealized anatomy were similarly analyzed. The average maximum error using the Static method for a 30 fraction simulation for the three techniques in phantoms was 23%, 11% for Convolution, 10% for Corrected Convolution, and 10% for Direct Simulation. In the two anatomical examples, the mean error in tumor control probability for Static and Convolution methods was 7% and 2%, respectively, of the result with no uncertainty, and 35% and 9%, respectively, for normal tissue complication probabilities. Convolution provides superior estimates of the delivered dose when compared to the Static method. In the range of fractions used clinically, considerable dosimetric variations will exist solely because of the random nature of the geometric uncertainties. However, the effect of finite fractionation appears to have a greater impact on the dose distribution than plan evaluation parameters.

Algorithms↗

PubMed2003

Comparison of dose calculation algorithms with Monte Carlo methods for photon arcs.

The objective of this study is to seek an accurate and efficient method to calculate the dose distribution of a photon arc. The algorithms tested include Monte Carlo, pencil beam kernel (PK), and collapsed cone convolution (CCC). For the Monte Carlo dose calculation, EGS4/DOSXYZ was used. The SRCXYZ source code associated with the DOSXYZ was modified so that the gantry angle of a photon beam would be sampled uniformly within the arc range about an isocenter to simulate a photon arc. Specifically, photon beams (6/18 MV, 4 x 4 and 10 x 10 cm2) described by a phase space file generated by BEAM (MCPHS), or by two point sources with different photon energy spectra (MCDIV) were used. These methods were used to calculate three-dimensional (3-D) distributions in a PMMA phantom, a cylindrical water phantom, and a phantom with lung inhomogeneity. A commercial treatment planning system was also used to calculate dose distributions in these phantoms using equivalent tissue air ratio (ETAR), PK and CCC algorithms for inhomogeneity corrections. Dose distributions for a photon arc in these phantoms were measured using a RK ion chamber and radiographic films. For homogeneous phantoms, the measured results agreed well (approximately 2% error) with predictions by the Monte Carlo simulations (MCPHS and MCDIV) and the treatment planning system for the 180 degrees and 360 degrees photon arcs. For the dose distribution in the phantom with lung inhomogeneity with a 90 degrees photon arc, the Monte Carlo calculations agreed with the measurements within 2%, while the treatment planning system using ETAR, PK and CCC underestimated or overestimated the dose inside the lung inhomogeneity from 6% to 12%.

Algorithms↗

PubMed2002

Improving the consistency in cervical esophageal target volume definition by special training.

PURPOSE: Three-dimensional conformal radiation therapy requires the precise definition of the target volume. Its potential benefits could be offset by the inconsistency in target definition by radiation oncologists. In a previous survey of radiation oncologists, a large degree of variation in target volume definition of cervical esophageal cancer was noted for the boost phase of radiotherapy. The present study evaluated whether special training could improve the consistency in target volume definitions. METHODS AND MATERIALS: A pre-training survey was performed to establish baseline values. This was followed by a special one-on-one training session on treatment planning based on the RTOG 94-05 protocol to 12 radiation oncologists. Target volumes were redrawn immediately and at 1-2 months later. Post-training vs. pre-training target volumes were compared. RESULTS: There was less variability in the longitudinal positions of the target volumes post-training compared to pre-training (p < 0.05 in 5 of 6 comparisons). One case had more variability due to the lack of a visible gross tumor on CT scans. Transverse contours of target volumes did not show any significant difference pre- or post-training. CONCLUSION: For cervical esophageal cancer, this study suggests that special training on protocol guidelines may improve consistency in target volume definition. Explicit protocol directions are required for situations where the gross tumor is not easily visible on CT scans. This may be particularly important for multicenter clinical trials, to reduce the occurrences of protocol violations.

Education, Medical, Continuing↗