Pepin, Mark D

PurposeDevelop an efficient, interactive, and instructive checklist document for the management of implanted electronic medical devices in a multimodality radiotherapy clinic.MethodsThe built‐in scripting and interactivity of a popular commercial word processor was used to develop an interactive document that changes the information presented to the user based on drop‐down selections. The interactivity and scripting were compatible with the radiation oncology information system (ROIS) which allows the document to be accessible by all team members and serve as a permanent record in a patient's electronic chart.ResultsThe final interactive document, which was clinically deployed after beta testing with a group consisting of nurses and medical physicists, presents information and action plans to the user based on multiple departmental medical device decision trees that are specific to the combination of device, treatment modality, rhythm‐pacing dependence for cardiac devices, and distance from the device to the treatment volume.ConclusionA script‐enabled interactive document was developed for a busy multimodality clinic, condensing multiple comprehensive departmental guidelines spanning multiple device types and treatment modalities into a single interactive checklist accessible within the ROIS. Given the wide accessibility of the commercial word processor, this approach could be adopted by other clinics to streamline their own respective workflows.

Background: Particle therapy is a noninvasive, catheter-free modality for cardiac ablation. We previously demonstrated the efficacy for creating ablation lesions in the porcine heart. Despite several earlier studies, the exact mechanism of early biophysical effects of proton and photon beam delivery on the myocardium remain incompletely resolved. Methods: Ten normal and 9 infarcted in situ porcine hearts received proton beam irradiation (40 Gy) delivered to the left ventricular myocardium with follow-up for 8 weeks. High-resolution electroanatomical mapping of the left ventricular was performed at baseline and follow-up. Bipolar voltage amplitude, conduction velocity, and connexin-43 were determined within the irradiated and nonirradiated areas. Results: The irradiated area in normal hearts showed a significant reduction of bipolar voltage amplitude (10.1±4.9 mV versus 5.7±3.2, P <0.0001) and conduction velocity (85±26 versus 55±13 cm/s, P =0.03) beginning at 4 weeks after irradiation. In infarcted myocardium after irradiation, bipolar voltage amplitude of the infarct scar (2.0±2.9 versus 0.8±0.7 mV, P =0.008) was significantly reduced as well as the conduction velocity in the infarcted heart (43.7±15.7 versus 26.3±11.4 cm/s, P =0.02). There were no significant changes in bipolar voltage amplitude and conduction velocity in nonirradiated myocardium. Myocytolysis, capillary hyperplasia, and dilation were seen in the irradiated myocardium 8 weeks after irradiation. Active caspase-3 and reduction of connexin-43 expression began in irradiated myocardium 1 week after irradiation and decreased over 8 weeks. Conclusions: Irradiation of the myocardium with proton beams reduce connexin-43 expression, conduction velocity, and bipolar conducted electrogram amplitude in a large porcine model. The changes in biomarkers preceded electrophysiological changes after proton beam therapy.

AbstractPurposeTo investigate the dependency of dose‐volume histogram (DVH) behavior and precision on underlying discretization using shapes and dose distributions with known analytical DVHs for five commercial DVH calculators.MethodsDVHs and summary metrics were extracted from all five systems using synthetic cone and cylinder objects for which the true volume and DVH curves were known. Trends in the curves and metrics were explored by varying the underlying voxelization of the CT image, structure set, and dose grid as well by varying the geometry of the structure and direction of a linear dose gradient. Using synthetic structures allowed for comparison with ground truth DVH curves to assess their accuracy while an algorithm was additionally developed to assess the precision of each system. The precision was calculated with a novel algorithm that treats any “stair step” behavior in a DVH curve as an uncertainty band and calculates the width, characterized as a percent difference, of the band for various DVH metrics. The underlying voxelization was additionally changed and DVHs were extracted for two clinical examples. The details of how each system calculated DVHs were also investigated and tendencies in the calculated curves, metrics, and precision were related to choices made in the calculation methodology.ResultsCalculation methodology differences that had a noticeable impact on the DVH curves and summary metrics include supersampling beyond the input grids and interpretation of the superior and inferior ends of the structures. Among the systems studied, the median precision ranged from 0.902% to 3.22%, and interquartile ranges varied from 1.09% to 3.91%.ConclusionsCommercial dose‐evaluation solutions can calculate different DVH curves, structure volume measures, and dose statistics for the same input data due to differences in their calculation methodologies. This study highlights the importance of understanding and investigating the DVH calculation when considering a new clinical system and when using more than one system for data transfer.

Abstract The primary objective is to evaluate the potential dosimetric gains of performing functional avoidance‐based proton treatment planning using 4DCT derived ventilation imaging. 4DCT data of 31 patients from a prospective functional avoidance clinical trial were evaluated with intensity modulated proton therapy (IMPT) plans and compared with clinical volumetric modulated arc therapy (VMAT) plans. Dosimetric parameters were compared between standard and functional plans with IMPT and VMAT with one‐way analysis of variance and post hoc paired student t‐test. Normal Tissue Complication Probability (NTCP) models were employed to estimate the risk of two toxicity endpoints for healthy lung tissues. Dose degradation due to proton motion interplay effect was evaluated. Functional IMPT plans led to significant dose reduction to functional lung structures when compared with functional VMAT without significant dose increase to Organ at Risk (OAR) structures. When interplay effect is considered, no significant dose degradation was observed for the OARs or the clinical target volume (CTV) volumes for functional IMPT. Using fV20 as the dose metric and Grade 2+ pneumonitis as toxicity endpoint, there is a mean 5.7% reduction in Grade 2+ RP with the functional IMPT and as high as 26% in reduction for individual patient when compared to the standard IMPT planning. Functional IMPT was able to spare healthy lung tissue to avoid excess dose to normal structures while maintaining satisfying target coverage. NTCP calculation also shows that the risk of pulmonary complications can be further reduced with functional based IMPT.

PurposeThe presence of respiratory motion during radiation treatment leads to degradation of the expected dose distribution, both for target coverage and healthy tissue sparing, particularly for techniques like pencil beam scanning proton therapy which have dynamic delivery systems. While tools exist to estimate this degraded four‐dimensional (4D) dose, they typically have one or more deficiencies such as not including the particular effects from a dynamic delivery, using analytical dose calculations, and/or using nonphysical dose‐accumulation methods. This work presents a clinically useful 4D‐dose calculator that addresses each of these shortcomings.MethodsTo quickly compute the 4D dose, the three main tasks of the calculator were run on graphics processing units (GPUs). These tasks were (a) simulating the delivery of the plan using measured delivery parameters to distribute the plan amongst 4DCT phases characterizing the patient breathing, (b) using an in‐house Monte Carlo simulation (MC) dose calculator to determine the dose delivered to each breathing phase, and (c) accumulating the doses from the various breathing phases onto a single phase for evaluation. The accumulation was performed by individually transferring the energy and mass of dose‐grid subvoxels, a technique that models the transfer of dose in a more physically realistic manner. The calculator was run on three test cases, with lung, esophagus, and liver targets, respectively, to assess the various uncertainties in the beam delivery simulation as well as to characterize the dose‐accumulation technique.ResultsFour‐dimensional doses were successfully computed for the three test cases with computation times ranging from 4–6 min on a server with eight NVIDIA Titan X graphics cards; the most time‐consuming component was the MC dose engine. The subvoxel‐based dose‐accumulation technique produced stable 4D‐dose distributions at subvoxel scales of 0.5–1.0 mm without impairing the total computation time. The uncertainties in the beam delivery simulation led to moderate variations of the dose–volume histograms for these cases; the variations were reduced by implementing repainting or phase‐gating motion mitigation techniques in the calculator.ConclusionsA MC‐based and GPU‐accelerated 4D‐dose calculator was developed to estimate the effects of respiratory motion on pencil beam scanning proton therapy treatments. After future validation, the calculator could be used to assess treatment plans and its quick runtime would make it easily usable in a future 4D‐robust optimization system.