BackgroundA knowledge-based radiation therapy (KBRT) treatment planning algorithm was recently developed. The purpose of this work is to investigate how plans that are generated with the objective KBRT approach compare to those that rely on the judgment of the experienced planner.MethodsThirty volumetric modulated arc therapy plans were randomly selected from a database of prostate plans that were generated by experienced planners (expert plans). The anatomical data (CT scan and delineation of organs) of these patients and the KBRT algorithm were given to a novice with no prior treatment planning experience. The inexperienced planner used the knowledge-based algorithm to predict the dose that the OARs receive based on their proximity to the treated volume. The population-based OAR constraints were changed to the predicted doses. A KBRT plan was subsequently generated. The KBRT and expert plans were compared for the achieved target coverage and OAR sparing. The target coverages were compared using the Uniformity Index (UI), while 5 dose-volume points (D10, D30, D50, D70 and D90) were used to compare the OARs (bladder and rectum) doses. Wilcoxon matched-pairs signed rank test was used to check for significant differences (p < 0.05) between both datasets.ResultsThe KBRT and expert plans achieved mean UI values of 1.10 ± 0.03 and 1.10 ± 0.04, respectively. The Wilcoxon test showed no statistically significant difference between both results. The D90, D70, D50, D30 and D10 values of the two planning strategies, and the Wilcoxon test results suggests that the KBRT plans achieved a statistically significant lower bladder dose (at D30), while the expert plans achieved a statistically significant lower rectal dose (at D10 and D30).ConclusionsThe results of this study show that the KBRT treatment planning approach is a promising method to objectively incorporate patient anatomical variations in radiotherapy treatment planning.

The purpose of this work is to develop a virtual source model (VSM) for the 50 kV INTRABEAM® device for Monte Carlo (MC) dose calculation. The geometry of the device was modelled in Geant4. A phase space file (PSF) was computed by simulating the interactions of monoenergetic primary electrons with the target. The PSF was approximated by computing the energy spectrum of the photons in the PSF. The variation of photon intensity, mean direction cosine and standard deviation along the axis of the source was thereafter computed. The isotropy of the source was used to approximate the properties of the source on the transverse plane. These functional approximations thereafter defined the VSM of the device. A sub-source was used to account for two kinds of photons, which were suppressed by the PSF approximation method. The intensity (relative to the main source) and emission directions of the sub-source required optimization. Optimization was achieved by the iterative adjustment of either or both parameters following MC simulation with the VSM and comparison of the calculated results to experimental data. The optimized source model was validated by comparing the calculated dose to water under several experimental setups, with reference data from the manufacturer, independent dosimetric check, and to literature results. The calculated photon energy spectra at other operating potentials (30 and 40 kV) of the device were also compared to literature data. The calculated energy spectra at all operating voltages are consistent with literature reports. The optimized sub-source has a relative intensity of 5% and an emission direction that is favoured along the axis of the source. The calculated depth dose curve for the bare probe agreed with the reference data, and the isodose lines are similar to published experimental results. Validation of the source model under a more complex experimental setup by film dosimetry agreed to within 2%/1 mm (98% pixel pass rate) of the values calculated with the VSM. We derived a VSM of the 50 kVp INTRABEAM source from a PSF. The dose predicted by the model agreed with reports in literature, reference data from the device manufacturer, and with an independent validation check. The algorithm could be used for treatment planning.