Organ absorbed doses in the IORT treatment of breast cancer with the INTRABEAM device: a Monte-Carlo study.

Purpose: The current prescription and the assessment of the delivered absorbed dose in intraoperative radiation therapy (IORT) with the INTRABEAM system rely mainly on depth-dose measurements in water. The accuracy of this approach is limited because tissue heterogeneity is ignored. It is also difficult to accurately determine the dose delivered to the patient experimentally as the steep dose gradient is highly sensitive to geometric errors. Our goal is to determine the dose to the target volume and the organs at risk of a clinical breast cancer patient from treatment with the system.Methods: A homogeneous water-equivalent CT dataset was derived from the preoperative CT scan of a patient by setting all materials in the patient volume as water-equivalent. This homogeneous CT data represents the current assumption of a homogenous patient, while the original CT data is considered the ground truth. An in-house Monte Carlo algorithm was used to simulate the delivered dose in both setups for a prescribed treatment dose of 20 Gy to the surface of the 3.5 cm diameter spherical applicator.Results: The doses received by 2% (D2%) of the target volume for the homogeneous and heterogeneous geometries are 16.26 Gy and 9.33 Gy, respectively. The D2% for the heart are 0.035 Gy and 0.119 Gy for the homogeneous and heterogeneous geometries, respectively. This trend is also observed for the other organs at risk.Conclusions: The assumption of a homogeneous patient overestimates the dose to the target volume and underestimates the doses to the organs at risk.

A knowledge-based quantitative approach to characterize treatment plan quality: Application to prostate VMAT planning.

PURPOSE: To characterize treatment plan (TP) quality, a quantitative quality control (QC) tool is proposed. The tool is validated using volumetric modulated arc therapy (VMAT) plans for treatment of prostate cancer by estimating the achievable organ at risk (OAR) sparing, based on the knowledge learned from prior plans. METHODS: Prostate TP quality was investigated by evaluating the achieved OAR sparing in the rectum and bladder, based on their proximity to target surface. The knowledge base used in this work comprises 450 plans, consisting of 181 homogenous prostate plans and 269 simultaneous integrated boost (SIB) prostate plans. A knowledge-based algorithm was used to relate the absorbed doses of the OARs (rectum and bladder) and their proximity to the planning target volume (PTV). A metric (Mq,r value) was calculated to characterize the OAR sparing based on the weighted differences of the mean doses at binned distances to the PTV surface. The 90% probability ellipse of the normally distributed OARs Mq,r values was considered to define a threshold above which the treatment plan was re-optimized. RESULTS: Following re-optimization, 8/11 of the homogenous plans and 6/13 of the SIB plans outside the 90% probability ellipse could be re-optimized to gain better OAR sparing while achieving the same or better target coverage. However, 3/4 of the homogenous TPs and 1/9 of the SIB TPs between 80% and 90% were improved. Mq,r values of bladder and rectum after re-optimizing the plans in both groups of homogenous and SIB showed lower values compared to the corresponding values before re-optimization, which implies that better OARs sparing was achieved. CONCLUSIONS: This work demonstrates an effective anatomy-specific QC tool for identifying suboptimal plans and determining the achievable OAR sparing for each individual patient anatomy.

A single-source photon source model of a linear accelerator for Monte Carlo dose calculation.

PURPOSE: To introduce a new method of deriving a virtual source model (VSM) of a linear accelerator photon beam from a phase space file (PSF) for Monte Carlo (MC) dose calculation. MATERIALS AND METHODS: A PSF of a 6 MV photon beam was generated by simulating the interactions of primary electrons with the relevant geometries of a Synergy linear accelerator (Elekta AB, Stockholm, Sweden) and recording the particles that reach a plane 16 cm downstream the electron source. Probability distribution functions (PDFs) for particle positions and energies were derived from the analysis of the PSF. These PDFs were implemented in the VSM using inverse transform sampling. To model particle directions, the phase space plane was divided into a regular square grid. Each element of the grid corresponds to an area of 1 mm2 in the phase space plane. The average direction cosines, Pearson correlation coefficient (PCC) between photon energies and their direction cosines, as well as the PCC between the direction cosines were calculated for each grid element. Weighted polynomial surfaces were then fitted to these 2D data. The weights are used to correct for heteroscedasticity across the phase space bins. The directions of the particles created by the VSM were calculated from these fitted functions. The VSM was validated against the PSF by comparing the doses calculated by the two methods for different square field sizes. The comparisons were performed with profile and gamma analyses. RESULTS: The doses calculated with the PSF and VSM agree to within 3% /1 mm (>95% pixel pass rate) for the evaluated fields. CONCLUSION: A new method of deriving a virtual photon source model of a linear accelerator from a PSF file for MC dose calculation was developed. Validation results show that the doses calculated with the VSM and the PSF agree to within 3% /1 mm.

Knowledge-based radiation therapy (KBRT) treatment planning versus planning by experts: validation of a KBRT algorithm for prostate cancer treatment planning.

BACKGROUND: A 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. METHODS: Thirty 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. RESULTS: The 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). CONCLUSIONS: The 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.

A global quality assurance system for personalized radiation therapy treatment planning for the prostate (or other sites).

INTRODUCTION: The quality of radiotherapy treatment plans varies across institutions and depends on the experience of the planner. For the purpose of intra- and inter-institutional homogenization of treatment plan quality, we present an algorithm that learns the organs-at-risk (OARs) sparing patterns from a database of high quality plans. Thereafter, the algorithm predicts the dose that similar organs will receive in future radiotherapy plans prior to treatment planning on the basis of the anatomies of the organs. The predicted dose provides the basis for the individualized specification of planning objectives, and for the objective assessment of the quality of radiotherapy plans. MATERIALS AND METHOD: One hundred and twenty eight (128) Volumetric Modulated Arc Therapy (VMAT) plans were selected from a database of prostate cancer plans. The plans were divided into two groups, namely a training set that is made up of 95 plans and a validation set that consists of 33 plans. A multivariate analysis technique was used to determine the relationships between the positions of voxels and their dose. This information was used to predict the likely sparing of the OARs of the plans of the validation set. The predicted doses were visually and quantitatively compared to the reference data using dose volume histograms, the 3D dose distribution, and a novel evaluation metric that is based on the dose different test. RESULTS: A voxel of the bladder on the average receives a higher dose than a voxel of the rectum in optimized radiotherapy plans for the treatment of prostate cancer in our institution if both voxels are at the same distance to the PTV. Based on our evaluation metric, the predicted and reference dose to the bladder agree to within 5% of the prescribed dose to the PTV in 18 out of 33 cases, while the predicted and reference doses to the rectum agree to within 5% in 28 out of the 33 plans of the validation set. CONCLUSION: We have described a method to predict the likely dose that OARs will receive before treatment planning. This prospective knowledge could be used to implement a global quality assurance system for personalized radiation therapy treatment planning.