Dosimetric characterization of the iBEAM evo carbon fiber couch for radiotherapy.

PURPOSE: This study characterizes the dosimetric properties of the iBEAM evo carbon fiber couch manufactured by Medical Intelligence and examines the accuracy of the CMS XiO and Nucletron Oncentra Masterplan (OMP) treatment planning systems for calculating beam attenuation due to the presence of the couch. METHODS: To assess the homogeneity of the couch, it was CT scanned at isocentric height and a number of signal intensity profiles were generated and analyzed. To simplify experimental procedures, surface dose and central axis depth dose measurements were performed in a solid water slab phantom using Gafchromic film for 6 and 10 MV photon beams at gantry angles of 0 degree (normal incidence), 30 degrees, and 60 degrees with an inverted iBEAM couch placed on top of the phantom. Attenuation measurements were performed in a cylindrical solid water phantom with an ionization chamber positioned at the isocenter. Measurements were taken for gantry angles from 0 degree to 90 degrees in 10 degrees increments for both 6 and 10 MV photon beams. This setup was replicated in the XiO and OMP treatment planning systems. Dose was calculated using the pencil beam, collapsed cone, convolution, and superposition algorithms. RESULTS: The CT scan of the couch showed that it was uniformly constructed. Surface dose increased by (510 +/-0)% for a 6 MV beam and (600 +/- 20)% for a 10 MV beam passing through the couch at normal incidence. Obliquely incident beams resulted in a higher surface dose compared to normally incident beams for both open fields and fields with the couch present. Depth dose curves showed that the presence of the couch resulted in an increase in dose in the build up region. For 6 and 10 MV beams incident at 60 degrees, nearly all skin sparing was lost. Attenuation measurements derived using the ionization chamber varied from 2.7% (0 degree) to a maximum of 4.6% (50 degrees) for a 6 MV beam and from 1.9% (0 degree) to a maximum of 4.0% (50 degrees) for a 10 MV beam. The pencil beam and convolution algorithms failed to accurately calculate couch attenuation. The collapsed cone and superposition algorithms calculated attenuation within an absolute error of +/- 1.2% for 6 MV and +/- 0.8% for 10 MV for gantry angles from 0 degree to 40 degrees. Some differences in attenuation were observed dependent on how the couch was contoured. CONCLUSIONS: These results demonstrate that the presence of the iBEAM evo carbon fiber couch increases the surface dose and dose in the build up region. The inclusion of the couch in the planning scan is limited by the field of view employed and the couch height at the time of CT scanning.

Method for Automatic Selection of Parameters in Normal Tissue Complication Probability Modeling.

PURPOSE: To present a fully automatic method to generate multiparameter normal tissue complication probability (NTCP) models and compare its results with those of a published model, using the same patient cohort. METHODS AND MATERIALS: Data were analyzed from 345 rectal cancer patients treated with external radiation therapy to predict the risk of patients developing grade 1 or ≥2 cystitis. In total, 23 clinical factors were included in the analysis as candidate predictors of cystitis. Principal component analysis was used to decompose the bladder dose-volume histogram into 8 principal components, explaining more than 95% of the variance. The data set of clinical factors and principal components was divided into training (70%) and test (30%) data sets, with the training data set used by the algorithm to compute an NTCP model. The first step of the algorithm was to obtain a bootstrap sample, followed by multicollinearity reduction using the variance inflation factor and genetic algorithm optimization to determine an ordinal logistic regression model that minimizes the Bayesian information criterion. The process was repeated 100 times, and the model with the minimum Bayesian information criterion was recorded on each iteration. The most frequent model was selected as the final "automatically generated model" (AGM). The published model and AGM were fitted on the training data sets, and the risk of cystitis was calculated. RESULTS: The 2 models had no significant differences in predictive performance, both for the training and test data sets (P value > .05) and found similar clinical and dosimetric factors as predictors. Both models exhibited good explanatory performance on the training data set (P values > .44), which was reduced on the test data sets (P values < .05). CONCLUSIONS: The predictive value of the AGM is equivalent to that of the expert-derived published model. It demonstrates potential in saving time, tackling problems with a large number of parameters, and standardizing variable selection in NTCP modeling.

Automatic detection of MLC relative position errors for VMAT using the EPID-based picket fence test.

Multi-leaf collimators (MLCs) ensure the accurate delivery of treatments requiring complex beam fluences like intensity modulated radiotherapy and volumetric modulated arc therapy. The purpose of this work is to automate the detection of MLC relative position errors ⩾0.5 mm using electronic portal imaging device-based picket fence tests and compare the results to the qualitative assessment currently in use. Picket fence tests with and without intentional MLC errors were measured weekly on three Varian linacs. The picket fence images analysed covered a time period ranging between 14-20 months depending on the linac. An algorithm was developed that calculated the MLC error for each leaf-pair present in the picket fence images. The baseline error distributions of each linac were characterised for an initial period of 6 months and compared with the intentional MLC errors using statistical metrics. The distributions of median and one-sample Kolmogorov-Smirnov test p-value exhibited no overlap between baseline and intentional errors and were used retrospectively to automatically detect MLC errors in routine clinical practice. Agreement was found between the MLC errors detected by the automatic method and the fault reports during clinical use, as well as interventions for MLC repair and calibration. In conclusion the method presented provides for full automation of MLC quality assurance, based on individual linac performance characteristics. The use of the automatic method has been shown to provide early warning for MLC errors that resulted in clinical downtime.