Purpose: A 90Sr/Y applicator has been used as a ‐source for postoperative irradiation after pterygium excision. As an alternative to 90Sr/Y irradiation, we proposed treatments with 32P. This study aims to provide the dosimetry for this new applicator. Method and Materials: In order to optimize the design and materials of 32P ophthalmic applicators, Monte Carlo simulations were performed. The absorbed dose at the surface of a sealed beta source is often measured by using an extrapolation ionization chamber. Radiochromic film (RCF) was used to measure depth dose distributions and dose profiles at various depths. A micro‐MOSFET detector was used for depth dose measurements. Results: The absorbed dose rates to the reference point were 0.238 ± 0.012 cGy/s for an extrapolation ionization chamber, 0.280 ± 0.001 cGy/s for radiochromic films, and 0.257 ± 0.020 cGy/s for MOSFET. The axial depth dose rate was reduced into approximately 1/10 as 32P betas penetrate every 2 mm depth. Measured data sets in depths of 1 mm to 3.5 mm agreed with Monte Carlo data. Due to non‐uniform absorption of 32P into an absorbent disk, the dose at the center of transaxial plane were 2%–4% less than the peak dose around the periphery. We confirmed no leakage of 32P activities and negligible exposure rate around the hand grip of the applicator. Conclusions: The 32P applicator can deliver uniform therapeutic doses to the surface of the conjunctiva, while sparing the lens better than 90Sr/Y applicators. The doses at any points from the 32P applicator can be calculated by using these measured data sets. The safety of 32P applicator was confirmed. However, prior to the clinical application of every new applicator, safety, dose uniformity, and absorbed dose rate at the reference point should be carefully evaluated by the method developed in this study.
Purpose:
To quantify and predict the magnitude of multi‐leaf collimator (MLC) positional errors in volumetric modulated arc therapy (VMAT) plans using statistical learning techniques to allow more accurate representation of the dose distribution expected to be delivered.
Methods:
A total of 74 VMAT plans used for patient treatments from three separate institutions were acquired. All plans were delivered using a Varian Millennium 120 MLC. The plans were split into training (N=3), validation (N=6) and testing (N=65) sets. From these, numerical features such as individual leaf position and velocity, and categorical features such as whether the leaf was moving towards or away from the isocenter, the bank the leaf was a part of, and the control point (CP) at which the error occurred were extracted. The differences between planned and delivered leaf positions in the training data were used as a target response for the development of a linear regression model, a decision tree model, and a random forest model. Optimized model parameters were found using cross‐validation on the validation set. Performance of each model in predicting the positional errors was assessed using mean absolute error (MAE) and root mean square error (RMSE) on the held‐out test set.
Results:
The MAE between planned and delivered positions for moving MLCs was 1.27 mm (RMSE = 1.60 mm). The decision tree model had the best performance, the predictions of which had MAE for moving MLCs of 0.27 mm (RMSE = 0.39 mm). Leaf velocity significantly predicted position errors, (β = 0.128, p<0.0001), and explained a significant amount of the variance (r2=0.90, p<0.0001).
Conclusion:
The decision tree model accurately predicted actual MLC leaf positions during delivery. Incorporating predicted errors into the planned MLC positions leads to a more realistic representation of the leaf locations which can be expected during treatment delivery to the patient.
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