Effects of tissue heterogeneity and comparisons of collapsed cone and Monte Carlo fast neutron patient dosimetry using the University of Washington clinical neutron therapy system (CNTS).

Fast neutron therapy is a high linear energy transfer (LET) radiation treatment modality offering advantages over low LET radiations. Multileaf collimator technology reduces normal-tissue dose (toxicity) and makes neutron therapy more comparable to MV x-ray treatments. Published clinical-trial and other experiences with fast neutron therapy are reported. Early comparative studies failed to consider differences in target-dose spatial conformality between x-ray and neutron treatments, which is especially important for organs-at-risk close to tumor targets. Treatments planning systems (TPS) for high-energy neutrons lag behind TPS tools for MV x-rays, creating challenges for comparative studies of clinical outcomes. A previously published Monte Carlo model of the University of Washington (UW) Clinical Neutron Therapy System (CNTS) is refined and integrated with the RayStation TPS as an external dose planning/verification tool. The collapsed cone (CC) dose calculations in the TPS are based on measured dose profiles and output factors in water, with the absolute dose determined using a tissue-equivalent ionization chamber. For comparison, independent (external) Monte Carlo simulation computes dose on a voxel-by-voxel basis using an atlas that maps Hounsfield Unit (HU) numbers to elemental composition and density. Although the CC algorithm in the TPS accurately computes neutron dose to water compared to Monte Carlo calculations, calculated dose to water differs from bone or tissue depending largely on hydrogen content. Therefore, the elemental composition of tissue and bone, rather than the material or electron density, affects fast neutron dose. While the CC algorithm suffices for reproducible patient dosimetry in fast neutron therapy, adopting methods that consider tissue heterogeneity would enhance patient-specific neutron dose accuracy relative to national standards for other types of ionizing radiation. Corrections for tissue composition have a significant impact on absolute dose and the relative biological effectiveness (RBE) of neutron treatments compared to other radiation types (MV x-rays, protons, and carbon ions).

Nearest Neighbor-Based Strategy to Optimize Multi-View Triplet Network for Classification of Small-Sample Medical Imaging Data.

Multi-view classification with limited sample size and data augmentation is a very common machine learning (ML) problem in medicine. With limited data, a triplet network approach for two-stage representation learning has been proposed. However, effective training and verifying the features from the representation network for their suitability in subsequent classifiers are still unsolved problems. Although typical distance-based metrics for the training capture the overall class separability of the features, the performance according to these metrics does not always lead to an optimal classification. Consequently, an exhaustive tuning with all feature-classifier combinations is required to search for the best end result. To overcome this challenge, we developed a novel nearest-neighbor (NN) validation strategy based on the triplet metric. This strategy is supported by a theoretical foundation to provide the best selection of the features with a lower bound of the highest end performance. The proposed strategy is a transparent approach to identify whether to improve the features or the classifier. This avoids the need for repeated tuning. Our evaluations on real-world medical imaging tasks (i.e., radiation therapy delivery error prediction and sarcoma survival prediction) show that our strategy is superior to other common deep representation learning baselines [i.e., autoencoder (AE) and softmax]. The strategy addresses the issue of feature's interpretability which enables more holistic feature creation such that the medical experts can focus on specifying relevant data as opposed to tedious feature engineering.

Socio-economic factors do not affect overall survival in soft tissue sarcoma when patients treated at a single high-volume center.

BACKGROUND: Treatments for soft tissue sarcoma (STS) include extensive surgical resection, radiation and chemotherapy, and can necessitate specialized care and excellent social support. Studies have demonstrated that socioeconomic factors, such as income, marital status, urban/rural residence, and educational attainment as well as treatment at high-volume institution may be associated with overall survival (OS) in STS. METHODS: In order to explore the effect of socio-economic factors on OS in patients treated at a high-volume center, we performed a retrospective analysis of STS patients treated at a single institution. RESULTS: Overall, 435 patients were included. Thirty-seven percent had grade 3 tumors and 44% had disease larger than 5 cm. Patients were most commonly privately insured (38%), married (67%) and retired or unemployed (43%). Median distance from the treatment center was 42 miles and median area deprivation index (ADI) was 5 (10 representing most deprived communities). The majority of patients (52%) were treated with neoadjuvant therapy followed by resection. As expected, higher tumor grade (HR 3.1), tumor size > 5 cm (HR 1.3), and involved lymph nodes (HR 3.2) were significantly associated with OS on multivariate analysis. Demographic and socioeconomic factors, including sex, age at diagnosis, marital status, employment status, urban vs. rural location, income, education, distance to the treatment center, and ADI were not associated with OS. CONCLUSIONS: In contrast to prior studies, we did not identify a significant association between socioeconomic factors and OS of patients with STS when patients were treated at a single high-volume center. Treatment at a high volume institution may mitigate the importance of socio-economic factors in the OS of STS.

Scattering kernels for fast neutron therapy treatment planning.

The University of Washington (UW) Clinical Neutron Therapy System (CNTS) has been used to treat over 3300 patients. Treatment planning for these patients is currently performed using an MV x-ray model in Pinnacle® adapted to fit measurements of fast neutron output factors, wedge factors, depth-dose and lateral profiles. While this model has provided an adequate representation of the CNTS for 3D conformal treatment planning, later versions of Pinnacle did not allow for isocentric gantry rotation machines with a source-to-axis distance of 150 cm. This restriction limited the neutron model to version 9.0 while the photon and electron treatment planning at the UW had moved on to newer versions. Also, intensity modulated neutron therapy (IMNT) is underdevelopment at the UW, and the Pinnacle neutron model developed cannot be used for inverse treatment planning. We have used the MCNP6 Monte Carlo code system to develop Collapsed Cone (CC) and Singular Value Decomposition (SVD) neutron scattering kernels suitable for integration into the RayStation treatment planning system. Kernels were generated for monoenergetic neutrons with energies ranging from 1 keV to 51 MeV, i.e. the energy range most relevant to the CNTS. Percent depth dose (PDD) profiles computed in RayStation for the CC and SVD kernels are in excellent agreement with each other for depths beyond the beam's dose build-up region (depths greater than about 1.7 cm) for open 2.8 × 2.8 cm2, 10.3 × 10.3 cm2, and 28.8 × 32.8 cm2 fields. Lateral profiles at several depths, as well as the PDD, calculated using the CC kernels in RayStation for the full CNTS energy spectrum pass a 3%/3 mm gamma test for field sizes of 2.8 × 2.8 cm2, 10.0 × 10.3 cm2, and 28.8 × 32.8 cm2.

Reducing Cardiac Radiation Dose From Breast Cancer Radiation Therapy With Breath Hold Training and Cognitive Behavioral Therapy.

The delivery of radiation therapy shares many of the challenges encountered in imaging procedures. As in imaging, such as MRI, organ motion must be reduced to a minimum, often for lengthy time periods, to effectively target the tumor during imaging-guided therapy while reducing radiation dose to nearby normal tissues. For patients, radiation therapy is frequently a stress- and anxiety-provoking medical procedure, evoking fear from negative perceptions about irradiation, confinement from immobilization devices, claustrophobia, unease with equipment, physical discomfort, and overall cancer fear. Such stress can be a profound challenge for cancer patients' emotional coping and tolerance to treatment, and particularly interferes with advanced radiation therapy procedures where active, complex and repetitive high-level cooperation is often required from the patient.In breast cancer, the most common cancer in women worldwide, radiation therapy is an indispensable component of treatment to improve tumor control and outcome in both breast-conserving therapy for early-stage disease and in advanced-stage patients. High technological complexity and high patient cooperation is required to mitigate the known cardiac toxicity and mortality from breast cancer radiation by reducing the unintended radiation dose to the heart from left breast or left chest wall irradiation. To address this, radiation treatment in daily deep inspiration breath hold (DIBH), to create greater distance between the treatment target and the heart, is increasingly practiced. While holding the promise to decrease cardiac toxicity, DIBH procedures often augment patients' baseline stress and anxiety reaction toward radiation treatment. Patients are often overwhelmed by the physical and mental demands of daily DIBH, including the nonintuitive timed and sustained coordination of abdominal thoracic muscles for prolonged breath holding.While technologies, such as DIBH, have advanced to millimeter-precision in treatment delivery and motion tracking, the "human factor" of patients' ability to cooperate and perform has been addressed much less. Both are needed to optimally deliver advanced radiation therapy with minimized normal tissue effects, while alleviating physical and cognitive distress during this challenging phase of breast cancer therapy.This article discusses physical training and psychotherapeutic integrative health approaches, applied to radiation oncology, to leverage and augment the gains enabled by advanced technology-based high-precision radiation treatment in breast cancer. Such combinations of advanced technologies with training and cognitive integrative health interventions hold the promise to provide simple feasible and low-cost means to improve patient experience, emotional outcomes and quality of life, while optimizing patient performance for advanced imaging-guided treatment procedures - paving the way to improve cardiac outcomes in breast cancer survivors.

Characterizing a deformable registration algorithm for surface-guided breast radiotherapy.

PURPOSE: Surface-guided radiation therapy (SGRT) is a nonionizing imaging approach for patient setup guidance, intra-fraction monitoring, and automated breath-hold gating of radiation treatments. SGRT employs the premise that the external patient surface correlates to the internal anatomy, to infer the treatment isocenter position at time of treatment delivery. Deformations and posture variations are known to impact the correlation between external and internal anatomy. However, the degree, magnitude, and algorithm dependence of this impact are not intuitive and currently no methods exist to assess this relationship. The primary aim of this work was to develop a framework to investigate and understand how a commercial optical surface imaging system (C-RAD, Uppsala, Sweden), which uses a nonrigid registration algorithm, handles rotations and surface deformations. METHODS: A workflow consisting of a female torso phantom and software-introduced transformations to the corresponding digital reference surface was developed. To benchmark and validate the approach, known rigid translations and rotations were first applied. Relevant breast radiotherapy deformations related to breast size, hunching/arching back, distended/deflated abdomen, and an irregular surface to mimic a cover sheet over the lower part of the torso were investigated. The difference between rigid and deformed surfaces was evaluated as a function of isocenter location. RESULTS: For all introduced rigid body transformations, C-RAD computed isocenter shifts were determined within 1 mm and 1˚. Additional translational shifts to correct for rotations as a function of isocenter location were determined with the same accuracy. For yaw setup errors, the difference in shift corrections between a plan with an isocenter placed in the center of the breast (BrstIso) and one located 12 cm superiorly (SCFIso) was 2.3 mm/1˚ in lateral direction. Pitch setup errors resulted in a difference of 2.1 mm/1˚ in vertical direction. For some of the deformation scenarios, much larger differences up to 16 mm and 7˚ in the calculated shifts between BrstIso and SCFIso were observed that could lead to large unintended gaps or overlap between adjacent matched fields if uncorrected. CONCLUSIONS: The methodology developed lends itself well for quality assurance (QA) of SGRT systems. The deformable C-RAD algorithm determined accurate shifts for rigid transformations, and this was independent of isocenter location. For surface deformations, the position of the isocenter had considerable impact on the registration result. It is recommended to avoid off-axis isocenters during treatment planning to optimally utilize the capabilities of the deformable image registration algorithm, especially when multiple isocenters are used with fields that share a field edge.

MRI Radiomic Features Are Independently Associated With Overall Survival in Soft Tissue Sarcoma.

PURPOSE: Soft tissue sarcomas (STS) represent a heterogeneous group of diseases, and selection of individualized treatments remains a challenge. The goal of this study was to determine whether radiomic features extracted from magnetic resonance (MR) images are independently associated with overall survival (OS) in STS. METHODS AND MATERIALS: This study analyzed 2 independent cohorts of adult patients with stage II-III STS treated at center 1 (N = 165) and center 2 (N = 61). Thirty radiomic features were extracted from pretreatment T1-weighted contrast-enhanced MR images. Prognostic models for OS were derived on the center 1 cohort and validated on the center 2 cohort. Clinical-only (C), radiomics-only (R), and clinical and radiomics (C+R) penalized Cox models were constructed. Model performance was assessed using Harrell's concordance index. RESULTS: In the R model, tumor volume (hazard ratio [HR], 1.5) and 4 texture features (HR, 1.1-1.5) were selected. In the C+R model, both age (HR, 1.4) and grade (HR, 1.7) were selected along with 5 radiomic features. The adjusted c-indices of the 3 models ranged from 0.68 (C) to 0.74 (C+R) in the derivation cohort and 0.68 (R) to 0.78 (C+R) in the validation cohort. The radiomic features were independently associated with OS in the validation cohort after accounting for age and grade (HR, 2.4; P = .009). CONCLUSIONS: This study found that radiomic features extracted from MR images are independently associated with OS when accounting for age and tumor grade. The overall predictive performance of 3-year OS using a model based on clinical and radiomic features was replicated in an independent cohort. Optimal models using clinical and radiomic features could improve personalized selection of therapy in patients with STS.

Dosimetric effects of bolus and lens shielding in treating ocular lymphomas with low-energy electrons.

Radiation therapy is an effective treatment for primary orbital lymphomas. Lens shielding with electrons can reduce the risk of high-grade cataracts in patients undergoing treatment for superficial tumors. This work evaluates the dosimetric effects of a suspended eye shield, placement of bolus, and varying electron energies. Film (GafChromic EBT3) dosimetry and relative output factors were measured for 6, 8, and 10 MeV electron energies. A customized 5-cm diameter circle electron orbital cutout was constructed for a 6 × 6-cm applicator with a suspended lens shield (8-mm diameter Cerrobend cylinder, 2.2-cm length). Point doses were measured using a scanning electron diode in a solid water phantom at depths representative of the anterior and posterior lens. Depth dose profiles were compared for 0-mm, 3-mm, and 5-mm bolus thicknesses. At 5 mm (the approximate distance of the anterior lens from the surface of the cornea), the percent depth dose under the suspended lens shield was reduced to 15%, 15%, and 14% for electron energies 6, 8, and 10 MeV, respectively. Applying bolus reduced the benefit of lens shielding by increasing the estimated doses under the block to 27% for 3-mm and 44% for 5-mm bolus for a 6 MeV incident electron beam. This effect is minimized with 8 MeV electron beams where the corresponding values were 15.5% and 18% for 3-mm and 5-mm bolus. Introduction of a 7-mm hole in 5-mm bolus to stabilize eye motion during treatment altered lens doses by about 1%. Careful selection of electron energy and consideration of bolus effects are needed to account for electron scatter under a lens shield.

Deep learning for patient-specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks.

PURPOSE: Patient-specific quality assurance (QA) for intensity-modulated radiation therapy (IMRT) is a ubiquitous clinical procedure, but conventional methods have often been criticized as being insensitive to errors or less effective than other common physics checks. Recently, there has been interest in the application of radiomics, quantitative extraction of image features, to radiotherapy QA. In this work, we investigate a deep learning approach to classify the presence or absence of introduced radiotherapy treatment delivery errors from patient-specific QA. METHODS: Planar dose maps from 186 IMRT beams from 23 IMRT plans were evaluated. Each plan was transferred to a cylindrical phantom CT geometry. Three sets of planar doses were exported from each plan corresponding to (a) the error-free case, (b) a random multileaf collimator (MLC) error case, and (c) a systematic MLC error case. Each plan was delivered to the electronic portal imaging device (EPID), and planned and measured doses were used to calculate gamma images in an EPID dosimetry software package (for a total of 558 gamma images). Two radiomic approaches were used. In the first, a convolutional neural network with triplet learning was used to extract image features from the gamma images. In the second, a handcrafted approach using texture features was used. The resulting metrics from both approaches were input into four machine learning classifiers (support vector machines, multilayer perceptrons, decision trees, and k-nearest-neighbors) in order to determine whether images contained the introduced errors. Two experiments were considered: the two-class experiment classified images as error-free or containing any MLC error, and the three-class experiment classified images as error-free, containing a random MLC error, or containing a systematic MLC error. Additionally, threshold-based passing criteria were calculated for comparison. RESULTS: In total, 303 gamma images were used for model training and 255 images were used for model testing. The highest classification accuracy was achieved with the deep learning approach, with a maximum accuracy of 77.3% in the two-class experiment and 64.3% in the three-class experiment. The performance of the handcrafted approach with texture features was lower, with a maximum accuracy of 66.3% in the two-class experiment and 53.7% in the three-class experiment. Variability between the results of the four machine learning classifiers was lower for the deep learning approach vs the texture feature approach. Both radiomic approaches were superior to threshold-based passing criteria. CONCLUSIONS: Deep learning with convolutional neural networks can be used to classify the presence or absence of introduced radiotherapy treatment delivery errors from patient-specific gamma images. The performance of the deep learning network was superior to a handcrafted approach with texture features, and both radiomic approaches were better than threshold-based passing criteria. The results suggest that radiomic QA is a promising direction for clinical radiotherapy.

Error Detection in Intensity-Modulated Radiation Therapy Quality Assurance Using Radiomic Analysis of Gamma Distributions.

PURPOSE: To improve the detection of errors in intensity-modulated radiation therapy (IMRT) with a novel method that uses quantitative image features from radiomics to analyze gamma distributions generated during patient specific quality assurance (QA). METHODS AND MATERIALS: One hundred eighty-six IMRT beams from 23 patient treatments were delivered to a phantom and measured with electronic portal imaging device dosimetry. The treatments spanned a range of anatomic sites; half were head and neck treatments, and the other half were drawn from treatments for lung and rectal cancers, sarcoma, and glioblastoma. Planar gamma distributions, or gamma images, were calculated for each beam using the measured dose and calculated doses from the 3-dimensional treatment planning system under various scenarios: a plan without errors and plans with either simulated random or systematic multileaf collimator mispositioning errors. The gamma images were randomly divided into 2 sets: a training set for model development and testing set for validation. Radiomic features were calculated for each gamma image. Error detection models were developed by training logistic regression models on these radiomic features. The models were applied to the testing set to quantify their predictive utility, determined by calculating the area under the curve (AUC) of the receiver operator characteristic curve, and were compared with traditional threshold-based gamma analysis. RESULTS: The AUC of the random multileaf collimator mispositioning model on the testing set was 0.761 compared with 0.512 for threshold-based gamma analysis. The AUC for the systematic mispositioning model was 0.717 versus 0.660 for threshold-based gamma analysis. Furthermore, the models could discriminate between the 2 types of errors simulated here, exhibiting AUCs of approximately 0.5 (equivalent to random guessing) when applied to the error they were not designed to detect. CONCLUSIONS: The feasibility of error detection in patient-specific IMRT QA using radiomic analysis of QA images has been demonstrated. This methodology represents a substantial step forward for IMRT QA with improved sensitivity and specificity over current QA methods and the potential to distinguish between different types of errors.

Dosimetric characteristics of the University of Washington Clinical Neutron Therapy System.

The University of Washington (UW) Clinical Neutron Therapy System (CNTS), which generates high linear energy transfer fast neutrons through interactions of 50.5 MeV protons incident on a Be target, has depth-dose characteristics similar to 6 MV x-rays. In contrast to the fixed beam angles and primitive blocking used in early clinical trials of neutron therapy, the CNTS has a gantry with a full 360° of rotation, internal wedges, and a multi-leaf collimator (MLC). Since October of 1984, over 3178 patients have received conformal neutron therapy treatments using the UW CNTS. In this work, the physical and dosimetric characteristics of the CNTS are documented through comparisons of measurements and Monte Carlo simulations. A high resolution computed tomography scan of the model 17 ionization chamber (IC-17) has also been used to improve the accuracy of simulations of the absolute calibration geometry. The response of the IC-17 approximates well the kinetic energy released per unit mass (KERMA) in water for neutrons and photons for energies from a few tens of keV up to about 20 MeV. Above 20 MeV, the simulated model 17 ion chamber response is 20%-30% higher than the neutron KERMA in water. For CNTS neutrons, simulated on- and off-axis output factors in water match measured values within ~2% ± 2% for rectangular and irregularly shaped field with equivalent square areas ranging in a side dimension from 2.8 cm to 30.7 cm. Wedge factors vary by less than 1.9% of the measured dose in water for clinically relevant field sizes. Simulated tissue maximum ratios in water match measured values within 3.3% at depths up to 20 cm. Although the absorbed dose for water and adipose tissue are within 2% at a depth of 1.7 cm, the absorbed dose in muscle and bone can be as much as 12 to 40% lower than the absorbed dose in water. The reported studies are significant from a historical perspective and as additional validation of a new tool for patient quality assurance and as an aid in ongoing efforts to clinically implement advanced treatment techniques, such as intensity modulated neutron therapy, at the UW.

Commissioning, clinical implementation, and performance of the Mobetron 2000 for intraoperative radiation therapy.

The Mobetron is a mobile electron accelerator designed to deliver therapeutic radiation dose intraoperatively while diseased tissue is exposed. Experience with the Mobetron 1000 has been reported extensively. However, since the time of those publications a new model, the Mobetron 2000, has become commercially available. Experience commissioning this new model and 3 years of data from historical use are reported here. Descriptions of differences between the models are emphasized, both in physical form and in dosimetric characteristics. Results from commissioning measurements including output factors, air gap factors, percent depth doses (PDDs), and 2D dose profiles are reported. Output factors are found to have changed considerably in the new model, with factors as high as 1.7 being measured. An example lookup table of appropriate accessory/energy combinations for a given target dimension is presented, and the method used to generate it described. Results from 3 years of daily QA measurements are outlined. Finally, practical considerations garnered from 3 years of use are presented.

Preliminary evaluation of the dosimetric accuracy of the in vivo plastic scintillation detector OARtrac system for prostate cancer treatments.

A promising, new, in vivo prostate dosimetry system has been developed for clinical radiation therapy. This work outlines the preliminary end-to-end testing of the accuracy and precision of the new OARtrac scintillation dosimetry system. We tested 94 calibrated plastic scintillation detector (PSD) probes before their final integration into endorectal balloon assemblies. These probes had been calibrated at The University of Texas MD Anderson Cancer Center Dosimetry Laboratory. We used a complete clinical OARtrac system including the PSD probes, charge coupled device camera monitoring system, and the manufacturer's integrated software package. The PSD probes were irradiated at 6 MV in a Solid Water® phantom. Irradiations were performed with a 6 MV linear accelerator using anterior-posterior/posterior-anterior matched fields to a maximum dose of 200 cGy in a 100 cm source-axis distance geometry. As a whole, the OARtrac system has good accuracy with a mean error of 0.01% and an error spread of ±5.4% at the 95% confidence interval. These results reflect the PSD probes' accuracy before their final insertion into endorectal balloons. Future work will test the dosimetric effects of mounting the PSD probes within the endorectal balloon assemblies.

Effectiveness of a novel gas-release endorectal balloon in the removal of rectal gas for prostate proton radiation therapy.

Endorectal balloons (ERBs) are routinely used in prostate proton radiation therapy to immobilize the prostate and spare the rectal wall. Rectal gas can distend the rectum and displace the prostate even in the presence of ERBs. The purpose of this work was to quantify the effects an ERB with a passive gas release conduit had on the incidence of rectal gas. Fifteen patients who were treated with a standard ERB and 15 with a gas-release ERB were selected for this retrospective study. Location and cross-sectional area of gas pockets and the fraction of time they occurred on 1133 lateral kilovoltage (kV) images were analyzed. Gas locations were classified as trapped between the ERB and anterior rectal wall, between the ERB and posterior rectal wall, or superior to the ERB. For patients using the standard ERB, gas was found in at least one region in 45.8% of fractions. Gas was trapped in the anterior region in 37.1% of fractions, in the posterior region in 5.0% of fractions, and in the sigmoid region in 9.6% of fractions. For patients using the ERB with the gas-release conduit, gas was found in at least one region in 19.7% of fractions. Gas was trapped in the anterior region in 5.6% of fractions, in the posterior region in 8.3% of fractions, and in the sigmoid region in 7.4% of fractions. Both the number of fractions with gas in the anterior region and the number of fractions with gas in at least one region were significantly higher in the former group than in the latter. The cross-sectional area of trapped gas did not differ between the two groups. Thus gas-release balloon can effectively release gas, and may be able to improve clinical workflow by reducing the need for catheterization.

Proton dose perturbations caused by high-voltage leads from implanted cardioverter defibrillators.

An increasing number of patients undergoing proton radiotherapy have cardiac implantable electrical devices (CIEDs). We recently encountered a situation in which a high-voltage coil on a lead from an implanted cardiac defibrillator was located within the clinical treatment volume for a patient receiving proton radiotherapy for esophageal cancer. To study the effects of the lead on the dose delivery, we placed a high-Z CIED lead at both the center and the distal edge of a clinical spread-out Bragg peak (SOBP) in a water phantom, in both a stationary position and with the lead moving in a periodic pattern to simulate cardiorespiratory movement. We then calculated planned doses using a commercial proton treatment planning system (TPS), and compared them with the doses delivered in the phantom, measured using radiographic film. Dose profiles from TPS-calculated and measured dose distributions showed large pertubrations in the delivered proton dose in the vicinity of the CIED lead when it was not moving. The TPS predicted perturbations up to 20% and measurements revealed perturbations up to 35%. However, the perturbations were less than 3% when the lead was moving. Greater dose perturbations were seen when the lead was placed at the distal edge of the SOBP than when it was placed in the center of the SOBP. We conclude that although cardiorespiratory motion of the lead mitigates some of the perturbations, the effects of the leads should be considered and steps taken to reduce these effects during the treatment planning process.