Christopher Ryan Peeler

Background: Radiation therapy (RT) can cause cardiac implantable electronic device (CIED) malfunction, primarily reset. Given changes in RT and CIED technologies, large observational studies examining malfunction of contemporary CIEDs during modern‐day RT are needed to guide clinical practice. Methods: Electronic medical records of all consecutive patients with CIEDs who underwent RT at a large tertiary cancer center between January 2015 and January 2022 were reviewed. All patients had CIED interrogation before and after completion of RT. Device‐related data and RT‐specific variables were collected. Statistical analyses were performed at the RT course level. Results: Over a period of 7 years, 677 patients (median age, 74 [interquartile range, 67. 8–79.2] years; 72% men) with CIEDs (498 [66%] pacemakers and 254 [34%] implantable cardioverter‐defibrillators) underwent 752 courses of RT (photon, 83.5%; proton, 10.5%; electron, 6%). Only 9 patients (1.3%) underwent CIED relocation before RT. Device malfunction was observed during 8 RT courses (1.1%), primarily soft reset with data loss. All device malfunctions were observed with neutron‐producing radiation (proton and photon >10 MV). CIED type (pacemaker or implantable cardioverter‐defibrillator), magnetic resonance imaging conditional or nonconditional system, and radiation site (thorax or nonthorax) did not show statistical differences between RT courses with reset versus no reset. No transient signal interference or oversensing due to RT was recognized by the CIED. Conclusions: CIED malfunction is a rare complication of contemporary RT, occurring primarily with neutron‐producing radiation, most of CIED malfunction cases are without serious clinical consequences. Transient signal interference and oversensing during RT does not appear to be a clinical concern in contemporary CIEDs.

Objectives: We investigated the performance of a slow computed tomography (CT) protocol to reduce alignment errors arising from motion when using CT-on-rail (CTOR) for image guidance for patients receiving thoracic stereotactic body radiation therapy (SBRT). Methods: A Quasar lung phantom with a moving tumor was programmed with three breathing rates and three motion amplitudes. MIP and average 4DCT images were used for contouring and alignment, respectively. Ten CTOR images were obtained for each of the breathing rates and amplitudes, under both CT protocols. We used in-house CAT software for image guidance, centering the tumor in the lung window within the gross tumor volume contour. Longitudinal coordinate reproducibility was compared between the two protocols. We also retrospectively analyzed CBCT SBRT image guidance alignment data from 31 patients to evaluate the systematic error in the longitudinal direction between simulation and daily treatments. Results: The mean (standard deviation) alignments (mm) for the standard and slow CT protocol ranged from 0.7 (0.68) and 1.0 (0.0), respectively, for the 28 BPM breathing rate and 5 mm amplitude combination to 5.2 (2.0) and 1.6 (0.52) for the 8 BPM breathing rate and 15 mm amplitude combination. Our retrospective analysis of patient alignment data showed a notable systematic difference in the relative bone and gross tumor volume alignment between the simulation and daily cone beam CT datasets. The mean longitudinal difference was −0.19 cm (standard deviation, 0.17 cm; range, 0.28 cm to −1.14 cm). Therefore, the position of the vertebral body cannot be used as a surrogate for mean tumor position in the longitudinal direction. Longitudinal position must be accurately determined for each patient using multiple CT images. Conclusions: A slow CT protocol improved the alignment with slower breathing rates being more challenging. A 5 mm PTV is not sufficient for tumor motion greater than 9 mm. Averaging the coordinates from multiple CTOR images is recommended.

Introduction The exact dependence of biological effect on dose and linear energy transfer (LET) in human tissue when delivering proton therapy is unknown. In this study we propose a framework for measuring this dependency using multi-modal image-based assays with deformable registrations within imaging sessions and across time. Materials and Methods 3T MRI scans were prospectively collected from 6 pediatric brain cancer patients before they underwent proton therapy treatment, and every 3 months for a year after treatment. Scans included T1-weighted with contrast enhancement (T1), T2-FLAIR (T2) and fractional anisotropy (FA) images. In addition, the planning CT, dose distributions and Monte Carlo-calculated LET distributions were collected. A multi-modal deformable image registration framework was used to create a dataset of dose, LET and imaging intensities at baseline and follow-up on a voxel-by-voxel basis. We modelled the biological effect of dose and LET from proton therapy using imaging changes over time as a surrogate for biological effect. We investigated various models to show the feasibility of the framework to model imaging changes. To account for inter- and intra-patient variations we used a nested generalized linear mixed regression model. The models were applied to predict imaging changes over time as a function of dose and LET for each modality. Results Using the nested models to predict imaging changes, we saw a decrease in the FA signal as a function of dose; however, the signal increased with increasing LET. Similarly, we saw an increase in T2 signal as a function of dose, but a decrease in signal with LET. We saw no changes in T1 voxel values as a function of either dose or LET. Conclusions The imaging changes could successfully model biological effect as a function of dose and LET using our proposed framework¹. Due to the low number of patients, the imaging changes observed for FA and T2 scans were not marked enough to draw any firm conclusions.

Purpose: Accurate modeling of the relative biological effectiveness (RBE) of particle beams requires increased systematic in vitro studies with human cell lines with care towards minimizing uncertainties in biologic assays as well as physical parameters. In this study, we describe a novel high‐throughput experimental setup and an optimized parameterization of the Monte Carlo (MC) simulation technique that is universally applicable for accurate determination of RBE of clinical ion beams. Clonogenic cell‐survival measurements on a human lung cancer cell line (H460) are presented using proton irradiation.Methods: Experiments were performed at the Heidelberg Ion Therapy Center (HIT) with support from the Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg, Germany using a mono‐energetic horizontal proton beam. A custom‐made variable range selector was designed for the horizontal beam line using the Geant4 MC toolkit. This unique setup enabled a high‐throughput clonogenic assay investigation of multiple, well defined dose and linear energy transfer (LETs) per irradiation for human lung cancer cells (H460) cultured in a 96‐well plate. Sensitivity studies based on application of different physics lists in conjunction with different electromagnetic constructors and production threshold values to the MC simulations were undertaken for accurate assessment of the calculated dose and the dose‐averaged LET (LET_d). These studies were extended to helium and carbon ion beams.Results: Sensitivity analysis of the MC parameterization revealed substantial dependence of the dose and LET_d values on both the choice of physics list and the production threshold values. While the dose and LET_d calculations using FTFP_BERT_LIV, FTFP_BERT_EMZ, FTFP_BERT_PEN and QGSP_BIC_EMY physics lists agree well with each other for all three ions, they show large differences when compared to the FTFP_BERT physics list with the default electromagnetic constructor. For carbon ions, the dose corresponding to the largest LET_d value is observed to differ by as much as 78% between FTFP_BERT and FTFP_BERT_LIV. Furthermore, between the production threshold of 700 μm and 5 μm, proton dose varies by as much as 19% corresponding to the largest LET_d value sampled in the current investigation. Based on the sensitivity studies, the FTFP_BERT physics list with the low energy Livermore electromagnetic constructor and a production threshold of 5 μm was employed for determining accurate dose and LET_d. The optimized MC parameterization results in a different LET_d dependence of the RBE curve for 10% SF of the H460 cell line irradiated with proton beam when compared with the results from a previous study using the same cell line. When the MC parameters are kept consistent between the studies, the proton RBE results agree well with each other within the experimental uncertainties.Conclusions: A custom high‐throughput, high‐accuracy experimental design for accurate in vitro cell survival measurements was employed at a horizontal beam line. High sensitivity of the physics‐based optimization establishes the importance of accurate MC parameterization and hence the conditioning of the MC system on a case‐by‐case basis. The proton RBE results from current investigations are observed to agree with a previous measurement made under different experimental conditions. This establishes the consistency of our experimental findings across different experiments and institutions.

Background: The relative biological effectiveness (RBE) for particle therapy is a complex function of particle type, radiation dose, linear energy transfer (LET), cell type, endpoint, etc. In the clinical practice of proton therapy, the RBE is assumed to have a fixed value of 1.1. This assumption, along with the effects of physical uncertainties, may mean that the biologically effective dose distributions received by the patient may be significantly different from what is seen on treatment plans. This may contribute to unforeseen toxicities and/or failure to control the disease. Variability of Proton RBE: It has been shown experimentally that proton RBE varies significantly along the beam path, especially near the end of the particle range. While there is now an increasing acceptance that proton RBE is variable, there is an ongoing debate about whether to change the current clinical practice. Clinical Evidence: A rationale against the change is the uncertainty in the models of variable RBE. Secondly, so far there is no clear clinical evidence of the harm of assuming proton RBE to be 1.1. It is conceivable, however, that the evidence is masked partially by physical uncertainties. It is, therefore, plausible that reduction in uncertainties and their incorporation in the estimation of dose actually delivered may isolate and reveal the variability of RBE in clinical practice. Nevertheless, clinical evidence of RBE variability is slowly emerging as more patients are treated with protons and their response data are analyzed. Modelling and Incorporation of RBE in the Optimization of Proton Therapy: The improvement in the knowledge of RBE could lead to better understanding of outcomes of proton therapy and in the improvement of models to predict RBE. Prospectively, the incorporation of such models in the optimization of intensity-modulated proton therapy could lead to improvements in the therapeutic ratio of proton therapy.

We introduce an approach for global fitting of the recently published high-throughput and high accuracy clonogenic cell-survival data for therapeutic scanned proton beams. Our fitting procedure accounts for the correlation between the cell-survival, the absorbed (physical) dose and the proton linear energy transfer (LET). The fitting polynomials and constraints have been constructed upon generalization of the microdosimetric kinetic model (gMKM) adapted to account for the low energy and high lineal-energy spectrum of the beam where the current radiobiological models may underestimate the reported relative biological effectiveness (RBE). The parameters (α, β) of the linear-quadratic (LQ) model calculated by the presented method reveal a smooth transition from low to high LETs which is an advantage of the current method over methods previously employed to fit the same clonogenic data. Finally, the presented approach provides insight into underlying microscopic mechanisms which, with future study, may help to elucidate radiobiological responses along the Bragg curve and resolve discrepancies between experimental data and current RBE models.