Dosimetric characterization of a new surface-conforming electron MLC prototype.

The purpose is to reduce normal tissue radiation toxicity for electron therapy through the creation of a surface-conforming electron multileaf collimator (SCEM). The SCEM combines the benefits of skin collimation, electron conformal radiotherapy, and modulated electron radiotherapy. An early concept for the SCEM was constructed. It consists of leaves that protrude towards the patient, allowing the leaves to conform closely to irregular patient surfaces. The leaves are made of acrylic to decrease bremsstrahlung, thereby decreasing the out-of-field dose. Water tank scans were performed with the SCEM in place for various field sizes for all available electron energies (6, 9, 12, and 15 MeV) with a 0.5 cm air gap to the water surface at 100 cm source-to-surface distance (SSD). These measurements were compared with Cerrobend cutouts with the field size-matched at 100 and 110 cm SSD. Output factor measurements were taken in solid water for each energy at dmax for both the cerrobend cutouts and SCEM at 100 cm SSD. Percent depth dose (PDD) curves for the SCEM shifted shallower for all energies and field sizes. The SCEM also produced a higher surface dose relative to Cerrobend cutouts, with the maximum being a 9.8% increase for the 3 cm × 9 cm field at 9 MeV. When compared to the Cerrobend cutouts at 110 cm SSD, the SCEM showed a significant decrease in the penumbra, particularly for lower energies (i.e., 6 and 9 MeV). The SCEM also showed reduced out-of-field dose and lower bremsstrahlung production than the Cerrobend cutouts. The SCEM provides significant improvement in the penumbra and out-of-field dose by allowing collimation close to the skin surface compared to Cerrobend cutouts. However, the added scatter from the SCEM increases shallow PDD values. Future work will focus on reducing this scatter while maintaining the penumbra and out-of-field benefits the SCEM has over conventional collimation.

Unique approaches for testicular shielding during total-body irradiation for pediatric patients.

Total-body irradiation (TBI) has been used as a part of the conditioning regimen for patients undergoing hematopoietic stem cell transplantation for certain nonmalignant conditions such as sickle cell disease. Although effective, TBI can cause lasting side effects for pediatric patients. One of these potential side effects includes oligospermia or even permanent azoospermia. Although many investigators have studied ways to shield the testicles during the TBI for nonmalignant conditions, there is no set standard. We describe the technical aspects of effective techniques to shield the testicles of male pediatric patients undergoing TBI. We verified that our techniques reduced the testicular dose by approximately 80%-85% of the TBI prescription dose in four male pediatric patients, keeping the dose well below the documented doses that can cause permanent infertility and hypogonadism.

Machine learning models to predict the delivered positions of Elekta multileaf collimator leaves for volumetric modulated arc therapy.

PURPOSE: Accurate positioning of multileaf collimator (MLC) leaves during volumetric modulated arc therapy (VMAT) is essential for accurate treatment delivery. We developed a linear regression, support vector machine, random forest, extreme gradient boosting (XGBoost), and an artificial neural network (ANN) for predicting the delivered leaf positions for VMAT plans. METHODS: For this study, 160 MLC log files from 80 VMAT plans were obtained from a single institution treated on 3 Elekta Versa HD linear accelerators. The gravity vector, X1 and X2 jaw positions, leaf gap, leaf position, leaf velocity, and leaf acceleration were extracted and used as model inputs. The models were trained using 70% of the log files and tested on the remaining 30%. Mean absolute error (MAE), root mean square error (RMSE), the coefficient of determination R2 , and fitted line plots showing the relationship between delivered and predicted leaf positions were used to evaluate model performance. RESULTS: The models achieved the following errors: linear regression (MAE = 0.158 mm, RMSE = 0.225 mm), support vector machine (MAE = 0.141 mm, RMSE = 0.199 mm), random forest (MAE = 0.161 mm, RMSE = 0.229 mm), XGBoost (MAE = 0.185 mm, RMSE = 0.273 mm), and ANN (MAE = 0.361 mm, RMSE = 0.521 mm). A significant correlation between a plan's gamma passing rate (GPR) and the prediction errors of linear regression, support vector machine, and random forest is seen (p < 0.045). CONCLUSIONS: We examined various models to predict the delivered MLC positions for VMAT plans treated with Elekta linacs. Linear regression, support vector machine, random forest, and XGBoost achieved lower errors than ANN. Models that can accurately predict the individual leaf positions during treatment can help identify leaves that are deviating from the planned position, which can improve a plan's GPR.

Monte Carlo modeling of the Elekta Versa HD and patient dose calculation with EGSnrc/BEAMnrc.

INTRODUCTION: Numerous studies have proven the Monte Carlo method to be an accurate means of dose calculation. Although there are several commercial Monte Carlo treatment planning systems (TPSs), some clinics may not have access to these resources. We present a method for routine, independent patient dose calculations from treatment plans generated in a commercial TPS with our own Monte Carlo model using free, open-source software. MATERIALS AND METHODS: A model of the Elekta Versa HD linear accelerator was developed using the EGSnrc codes. A MATLAB script was created to take clinical patient plans and convert the DICOM RTP files into a format usable by EGSnrc. Ten patients' treatment plans were exported from the Monaco TPS to be recalculated using EGSnrc. Treatment simulations were done in BEAMnrc, and doses were calculated using Source 21 in DOSXYZnrc. Results were compared to patient plans calculated in the Monaco TPS and evaluated in Verisoft with a gamma criterion of 3%/2 mm. RESULTS: Our Monte Carlo model was validated within 1%/1-mm accuracy of measured percent depth doses and profiles. Gamma passing rates ranged from 82.1% to 99.8%, with 7 out of 10 plans having a gamma pass rate over 95%. Lung and prostate patients showed the best agreement with doses calculated in Monaco. All statistical uncertainties in DOSXYZnrc were less than 3.0%. CONCLUSION: A Monte Carlo model for routine patient dose calculation was successfully developed and tested. This model allows users to directly recalculate DICOM RP files containing patients' plans that have been exported from a commercial TPS.

AAPM Report 373: The content, structure, and value of the Professional Doctorate in Medical Physics (DMP).

The Professional Doctorate in Medical Physics (DMP) was originally conceived as a solution to the shortage of medical physics residency training positions. While this shortage has now been largely satisfied through conventional residency training positions, the DMP has expanded to multiple institutions and grown into an educational pathway that provides specialized clinical training and extends well beyond the creation of additional training spots. As such, it is important to reevaluate the purpose and the value of the DMP. Additionally, it is important to outline the defining characteristics of the DMP to assure that all existing and future programs provide this anticipated value. Since the formation and subsequent accreditation of the first DMP program in 2009-2010, four additional programs have been created and accredited. However, no guidelines have yet been recommended by the American Association of Physicists in Medicine. CAMPEP accreditation of these programs has thus far been based only on the respective graduate and residency program standards. This allows the development and operation of DMP programs which contain only the requisite Master of Science (MS) coursework and a 2-year clinical training program. Since the MS plus 2-year residency pathway already exists, this form of DMP does not provide added value, and one may question why this existing pathway should be considered a doctorate. Not only do we, as a profession, need to outline the defining characteristics of the DMP, we need to carefully evaluate the potential advantages and disadvantages of this pathway within our education and training infrastructure. The aims of this report from the Working Group on the Professional Doctorate Degree for Medical Physicists (WGPDMP) are to (1) describe the current state of the DMP within the profession, (2) make recommendations on the structure and content of the DMP for existing and new DMP programs, and (3) evaluate the value of the DMP to the profession of medical physics.

Prenatal Correction of X-Linked Hypohidrotic Ectodermal Dysplasia.

Genetic deficiency of ectodysplasin A (EDA) causes X-linked hypohidrotic ectodermal dysplasia (XLHED), in which the development of sweat glands is irreversibly impaired, an condition that can lead to life-threatening hyperthermia. We observed normal development of mouse fetuses with Eda mutations after they had been exposed in utero to a recombinant protein that includes the receptor-binding domain of EDA. We administered this protein intraamniotically to two affected human twins at gestational weeks 26 and 31 and to a single affected human fetus at gestational week 26; the infants, born in week 33 (twins) and week 39 (singleton), were able to sweat normally, and XLHED-related illness had not developed by 14 to 22 months of age. (Funded by Edimer Pharmaceuticals and others.).

On the evaluation of mobile target trajectory between four-dimensional computer tomography and four-dimensional cone-beam computer tomography.

PURPOSE: For mobile lung tumors, four-dimensional computer tomography (4D CT) is often used for simulation and treatment planning. Localization accuracy remains a challenge in lung stereotactic body radiation therapy (SBRT) treatments. An attractive image guidance method to increase localization accuracy is 4D cone-beam CT (CBCT) as it allows for visualization of tumor motion with reduced motion artifacts. However, acquisition and reconstruction of 4D CBCT differ from that of 4D CT. This study evaluates the discrepancies between the reconstructed motion of 4D CBCT and 4D CT imaging over a wide range of sine target motion parameters and patient waveforms. METHODS: A thorax motion phantom was used to examine 24 sine motions with varying amplitudes and cycle times and seven patient waveforms. Each programmed motion was imaged using 4D CT and 4D CBCT. The images were processed to auto segment the target. For sine motion, the target centroid at each phase was fitted to a sinusoidal curve to evaluate equivalence in amplitude between the two imaging modalities. The patient waveform motion was evaluated based on the average 4D data sets. RESULTS: The mean difference and root-mean-square-error between the two modalities for sine motion were -0.35 ± 0.22 and 0.60 mm, respectively, with 4D CBCT slightly overestimating amplitude compared with 4D CT. The two imaging methods were determined to be significantly equivalent within ±1 mm based on two one-sided t tests (p < 0.001). For patient-specific motion, the mean difference was 1.5 ± 2.1 (0.8 ± 0.6 without outlier), 0.4 ± 0.3, and 0.8 ± 0.6 mm for superior/inferior (SI), anterior/posterior (AP), and left/right (LR), respectively. CONCLUSION: In cases where 4D CT is used to image mobile tumors, 4D CBCT is an attractive localization method due to its assessment of motion with respect to 4D CT, particularly for lung SBRT treatments where accuracy is paramount.

A sensitivity analysis of probability maps in deep-learning-based anatomical segmentation.

PURPOSE: Deep-learning-based segmentation models implicitly learn to predict the presence of a structure based on its overall prominence in the training dataset. This phenomenon is observed and accounted for in deep-learning applications such as natural language processing but is often neglected in segmentation literature. The purpose of this work is to demonstrate the significance of class imbalance in deep-learning-based segmentation and recommend tuning of the neural network optimization objective. METHODS: An architecture and training procedure were chosen to represent common models in anatomical segmentation. A family of 5-block 2D U-Nets were independently trained to segment 10 structures from the Cancer Imaging Archive's Head-Neck-Radiomics-HN1 dataset. We identify the optimal threshold for our models according to their Dice score on the validation datasets and consider perturbations about the optimum. A measure of structure prominence in segmentation datasets is defined, and its impact on the optimal threshold is analyzed. Finally, we consider the use of a 2D Dice objective in addition to binary cross entropy. RESULTS: We observe significant decreases in perceived model performance with conventional 0.5-thresholding. Perturbations of as little as ±0.05 about the optimum threshold induce a median reduction in Dice score of 11.8% for our models. There is statistical evidence to suggest a weak correlation between training dataset prominence and optimal threshold (Pearson r = 0.92 and p ≈ 10 - 4 ). We find that network optimization with respect to the 2D Dice score itself significantly reduces variability due to thresholding but does not unequivocally create the best segmentation models when assessed with distance-based segmentation metrics. CONCLUSION: Our results suggest that those practicing deep-learning-based contouring should consider their postprocessing procedures as a potential avenue for improved performance. For intensity-based postprocessing, we recommend a mixed objective function consisting of the traditional binary cross entropy along with the 2D Dice score.

Quantifying false positional corrections due to facial motion using SGRT with open-face Masks.

PURPOSE: Studies have evaluated the viability of using open-face masks as an immobilization technique to treat intracranial and head and neck cancers. This method offers less stress to the patient with comparable accuracy to closed-face masks. Open-face masks permit implementation of surface guided radiation therapy (SGRT) to assist in positioning and motion management. Research suggests that changes in patient facial expressions may influence the SGRT system to generate false positional corrections. This study aims to quantify these errors produced by the SGRT system due to face motion. METHODS: Ten human subjects were immobilized using open-face masks. Four discrete SGRT regions of interest (ROIs) were analyzed based on anatomical features to simulate different mask openings. The largest ROI was lateral to the cheeks, superior to the eyebrows, and inferior to the mouth. The smallest ROI included only the eyes and bridge of the nose. Subjects were asked to open and close their eyes and simulate fear and annoyance and peak isocenter shifts were recorded. This was performed in both standard and SRS specific resolutions with the C-RAD Catalyst HD system. RESULTS: All four ROIs analyzed in SRS and Standard resolutions demonstrated an average deviation of 0.3 ± 0.3 mm for eyes closed and 0.4 ± 0.4 mm shift for eyes open, and 0.3 ± 0.3 mm for eyes closed and 0.8 ± 0.9 mm shift for eyes open. The average deviation observed due to changing facial expressions was 1.4 ± 0.9 mm for SRS specific and 1.6 ± 1.6 mm for standard resolution. CONCLUSION: The SGRT system can generate false positional corrections for face motion and this is amplified at lower resolutions and smaller ROIs. These errors should be considered in the overall tolerances and treatment plan when using open-face masks with SGRT and may warrant additional radiographic imaging.

A customizable aluminum compensator system for total body irradiation.

PURPOSE: To develop a simplified aluminum compensator system for total body irradiation (TBI) that is easy to assemble and modify in a short period of time for customized patient treatments. METHODS: The compensator is composed of a combination of 0.3 cm thick aluminum bars, two aluminum T-tracks, spacers, and metal bolts. The system is mounted onto a plexiglass block tray. The design consists of 11 fixed sectors spanning from the patient's head to feet. The outermost sectors utilize 7.6 cm wide aluminum bars, while the remaining sectors use 2.5 cm wide aluminum bars. There is a magnification factor of 5 from the compensator to the patient treatment plane. Each bar of aluminum is interconnected at each adjacent sector with a tongue and groove arrangement and fastened to the T-track using a metal washer, bolt, and nut. Inter-bar leakage of the compensator was tested using a water tank and diode. End-to-end measurements were performed with an ion chamber in a solid water phantom and also with a RANDO phantom using internal and external optically stimulated luminescent detectors (OSLDs). In-vivo patient measurements from the first 20 patients treated with this aluminum compensator were compared to those from 20 patients treated with our previously used lead compensator system. RESULTS: The compensator assembly time was reduced to 20-30 min compared to the 2-4 h it would take with the previous lead design. All end-to-end measurements were within 10% of that expected. The median absolute in-vivo error for the aluminum compensator was 3.7%, with 93.8% of measurements being within 10% of that expected. The median error for the lead compensator system was 5.3%, with 85.1% being within 10% of that expected. CONCLUSION: This design has become the standard compensator at our clinic. It allows for quick assembly and customization along with meeting the Task Group 29 recommendations for dose uniformity.

Safety and immunogenicity of Fc-EDA, a recombinant ectodysplasin A1 replacement protein, in human subjects.

In X-linked hypohidrotic ectodermal dysplasia, the most frequent ectodermal dysplasia, an inherited deficiency of the signalling protein ectodysplasin A1 (EDA1) impairs the development of the skin and its appendages, various eccrine glands, and dentition. The severe hypohidrosis common to X-linked hypohidrotic ectodermal dysplasia patients may lead to life-threatening hyperthermia, especially during hot weather or febrile illness. Fc-EDA, an EDA1 replacement protein known to prevent the disease in newborn animals, was tested in 2 clinical trials (human adults and neonates) and additionally administered under compassionate use to 3 infants in utero. The data support the safety of Fc-EDA and efficacy if applied prenatally. Anti-drug antibodies were detected after intravenous administration in adult males and nonpregnant females, but not in pregnant women when Fc-EDA was delivered intra-amniotically. Most importantly, there was no detectable immune response to the investigational drug in neonates treated by intravenous infusions and in infants who had received Fc-EDA in utero. In conclusion, the safety profile of this drug encourages further development of prenatal EDA1 replacement therapy.

Deep learning-based survival analysis for brain metastasis patients with the national cancer database.

PURPOSE: Prognostic indices such as the Brain Metastasis Graded Prognostic Assessment have been used in clinical settings to aid physicians and patients in determining an appropriate treatment regimen. These indices are derivative of traditional survival analysis techniques such as Cox proportional hazards (CPH) and recursive partitioning analysis (RPA). Previous studies have shown that by evaluating CPH risk with a nonlinear deep neural network, DeepSurv, patient survival can be modeled more accurately. In this work, we apply DeepSurv to a test case: breast cancer patients with brain metastases who have received stereotactic radiosurgery. METHODS: Survival times, censorship status, and 27 covariates including age, staging information, and hormone receptor status were provided for 1673 patients by the NCDB. Monte Carlo cross-validation with 50 samples of 1400 patients was used to train and validate the DeepSurv, CPH, and RPA models independently. DeepSurv was implemented with L2 regularization, batch normalization, dropout, Nesterov momentum, and learning rate decay. RPA was implemented as a random survival forest (RSF). Concordance indices of test sets of 140 patients were used for each sample to assess the generalizable predictive capacity of each model. RESULTS: Following hyperparameter tuning, DeepSurv was trained at 32 min per sample on a 1.33 GHz quad-core CPU. Test set concordance indices of 0.7488 ± 0.0049, 0.6251 ± 0.0047, and 0.7368 ± 0.0047, were found for DeepSurv, CPH, and RSF, respectively. A Tukey HSD test demonstrates a statistically significant difference between the mean concordance indices of the three models. CONCLUSION: Our results suggest that deep learning-based survival prediction can outperform traditional models, specifically in a case where an accurate prognosis is highly clinically relevant. We recommend that where appropriate data are available, deep learning-based prognostic indicators should be used to supplement classical statistics.

An open-source tool to visualize potential cone collisions while planning SRS cases.

PURPOSE: To create an open-source visualization program that allows one to find potential cone collisions while planning intracranial stereotactic radiosurgery cases. METHODS: Measurements of physical components in the treatment room (gantry, cone, table, localization stereotactic radiation surgery frame, etc.) were incorporated into a script in MATLAB (MathWorks, Natick, MA) that produces three-dimensional visualizations of the components. A localization frame, used during simulation, fully contains the patient. This frame was used to represent a safety zone for collisions. Simple geometric objects are used to approximate the simulated components. The couch is represented as boxes, the gantry head and cone are represented by cylinders, and the patient safety zone can be represented by either a box or ellipsoid. These objects are translated and rotated based upon the beam geometry and the treatment isocenter to mimic treatment. A simple graphical user interface (GUI) was made in MATLAB (compatible with GNU Octave) to allow users to pass the treatment isocenter location, the initial and terminal gantry angles, the couch angle, and the number of angular points to visualize between the initial and terminal gantry angle. RESULTS: The GUI provides a fast and simple way to discover collisions in the treatment room before the treatment plan is completed. Twenty patient arcs were used as an end-to-end validation of the system. Seventeen of these appeared the same in the software as in the room. Three of the arcs appeared closer in the software than in the room. This is due to the treatment couch having rounded corners, whereas the software visualizes sharp corners. CONCLUSIONS: This simple GUI can be used to find the best orientation of beams for each patient. By finding collisions before a plan is being simulated in the treatment room, a user can save time due to replanning of cases.

Patient-specific dose quality assurance of single-isocenter multiple brain metastasis stereotactic radiosurgery using PTW Octavius 4D.

PURPOSE: Single-isocenter multiple brain metastasis stereotactic radiosurgery is an efficient treatment modality increasing in clinical practice. The need to provide accurate, patient-specific quality assurance (QA) for these plans is met by several options. This study reviews some of these options and explores the use of the Octavius 4D as a solution for patient-specific plan quality assurance. METHODS: The Octavius 4D Modular Phantom (O4D) with the 1000 SRS array was evaluated in this study. The array consists of 977 liquid-filled ion chambers. The center 5.5 cm × 5.5 cm area has a detector spacing of 2.5 mm. The ability of the O4D to reconstruct three-dimensional (3D) dose was validated against a 3D gel dosimeter, ion chamber, and film measurements. After validation, 15 patients with 2-11 targets had their plans delivered to the phantom. The criteria used for the gamma calculation was 3%/1 mm. The portion of targets which were measurable by the phantom was countable. The accompanying software compiled the measured doses allowing each target to be counted from the measured dose distribution. RESULTS: Spatial resolution was sufficient to verify the high dose distributions characteristic of SRS. Amongst the 15 patients there were 74 targets. Of the 74 targets, 61 (82%) of them were visible on the measured dose distribution. The average gamma passing rate was 99.3% (with sample standard deviation of 0.68%). CONCLUSIONS: The high resolution provided by the O4D with 1000 SRS board insert allows for very high-resolution measurement. This high resolution in turn can allow for high gamma passing rates. The O4D with the 1000 SRS array is an acceptable method of performing quality assurance for single-isocenter multiple brain metastasis SRS.

Incorporating biological modeling into patient-specific plan verification.

PURPOSE: Dose-volume histogram (DVH) measurements have been integrated into commercially available quality assurance systems to provide a metric for evaluating accuracy of delivery in addition to gamma analysis. We hypothesize that tumor control probability and normal tissue complication probability calculations can provide additional insight beyond conventional dose delivery verification methods. METHODS: A commercial quality assurance system was used to generate DVHs of treatment plan using the planning CT images and patient-specific QA measurements on a phantom. Biological modeling was performed on the DVHs produced by both the treatment planning system and the quality assurance system. RESULTS: The complication-free tumor control probability, P+ , has been calculated for previously treated intensity modulated radiotherapy (IMRT) patients with diseases in the following sites: brain (-3.9% ± 5.8%), head-neck (+4.8% ± 8.5%), lung (+7.8% ± 1.3%), pelvis (+7.1% ± 12.1%), and prostate (+0.5% ± 3.6%). CONCLUSION: Dose measurements on a phantom can be used for pretreatment estimation of tumor control and normal tissue complication probabilities. Results in this study show how biological modeling can be used to provide additional insight about accuracy of delivery during pretreatment verification.

Prenatal Treatment of X-Linked Hypohidrotic Ectodermal Dysplasia using Recombinant Ectodysplasin in a Canine Model.

X-linked hypohidrotic ectodermal dysplasia (XLHED) is caused by defects in the EDA gene that inactivate the function of ectodysplasin A1 (EDA1). This leads to abnormal development of eccrine glands, hair follicles, and teeth, and to frequent respiratory infections. Previous studies in the naturally occurring dog model demonstrated partial prevention of the XLHED phenotype by postnatal administration of recombinant EDA1. The results suggested that a single or two temporally spaced injections of EDI200 prenatally might improve the clinical outcome in the dog model. Fetuses received ultrasound-guided EDI200 intra-amniotically at gestational days 32 and 45, or 45 or 55 alone (of a 65-day pregnancy). Growth rates, lacrimation, hair growth, meibomian glands, sweating, dentition, and mucociliary clearance were compared in treated and untreated XLHED-affected dogs, and in heterozygous and wild-type control dogs. Improved phenotypic outcomes were noted in the earlier and more frequently treated animals. All animals treated prenatally showed positive responses compared with untreated dogs with XLHED, most notably in the transfer of moisture through paw pads, suggesting improved onset of sweating ability and restored meibomian gland development. These results exemplify the feasibility of ultrasound-guided intra-amniotic injections for the treatment of developmental disorders, with improved formation of specific EDA1-dependent structures in dogs with XLHED.

An evaluation of the stability of image quality parameters of Elekta X-ray volume imager and iViewGT imaging systems.

INTRODUCTION: A robust image quality assurance and analysis methodology for image-guided localization systems is crucial to ensure the accurate localization and visualization of target tumors. In this study, the long-term stability of selected image parameters was assessed and evaluated for the cone-beam computed tomography (CBCT) mode, planar radiographic kV mode, and the radiographic MV mode of an Elekta VersaHD. MATERIALS AND METHODS: The CATPHAN, QckV-1, and QC-3 phantoms were used to evaluate the image quality parameters. The planar radiographic images were analyzed in PIPSpro™ with spatial resolution (f30, f40, f50), contrast to noise ratio (CNR) and noise being recorded. For XVI CBCT, Head and Neck Small20 (S20) and Pelvis Medium20 (M20) standard acquisition modes were evaluated for uniformity, noise, spatial resolution, and HU constancy. Dose and kVp for the XVI were recorded using the Unfors RaySafe Xi system with the R/F low detector for the kV planar radiographic mode. For each metric, values were normalized to the mean and the standard deviations were recorded. RESULTS: A total of 30 measurements were performed on a single Elekta VersaHD linear accelerator over an 18-month period without significant adjustment or recalibration to the XVI or iViewGT systems during the evaluated time frame. For the planar radiographic spatial resolution, the normalized standard deviation values of the f30, f40, and f50 were 0.004, 0.003, and 0.003 and 0.015, 0.009, and 0.017 for kV and MV, respectively. The average recorded dose for kV was 67.96 µGy. The standard deviations of the evaluated metrics for the S20 acquisition were 0.083(f30), 0.058(f40), 0.056(f50), 0.021(Water/poly-HU constancy), 0.029(uniformity) and 0.028(noise). The standard deviations for the M20 acquisition were 0.093(f30), 0.043(f40), 0.037(f50), 0.016(Water/poly-HU constancy), 0.010(uniformity) and 0.011(Noise). CONCLUSION: A study was performed to assess the stability of the basic image quality parameters recommended by TG-142 for the Elekta XVI and iViewGT imaging systems. The two systems show consistent imaging and dosimetric properties over the evaluated time frame.

A method to predict patient-specific table coordinates for quality assurance in external beam radiation therapy.

PURPOSE: While external beam radiotherapy treatment planning determines nearly every mechanical and dosimetric parameter of the linear accelerator (LINAC), the table coordinates in all three dimensions are generally unknown until initial patient setup at the LINAC. Knowing these parameters in advance could help verify the direction of patient shifts and prevent wrong-site errors. This study aims to determine the feasibility and accuracy of table coordinate prediction for indexed immobilization devices. METHODS: A total of 303 table coordinates were predicted for patients on Varian and Elekta linear accelerators with immobilization devices including Orfit mask with baseplate, wingboard, breastboard and BodyFix. Predictions were made for all three spatial dimensions except for Body Fix setups due to the lack of a radiographically apparent indexing-related landmark. Coordinates were predicted by measuring baseline table coordinates in all dimensions at specified landmark positions. RESULTS: Predictions were accurate within 2 cm for 86% of coordinates (71% within 1 cm). Table coordinates were predicted most accurately for head and neck patients with a base plate and the most difficult prediction was in the lateral direction for breastboard patients. CONCLUSIONS: With proper indexing, table coordinates can be predicted with reasonable accuracy. The data suggest an action of level of 2 cm with certain exceptions for specific immobilization devices and directions.

Exploring undergraduate students' attitudes towards interprofessional learning, motivation-to-learn, and perceived impact of learning conflict resolution skills.

Conflict resolution skills are important for all healthcare professionals as conflict and mis-communication can have detrimental effects on decision-making, potentially impacting significantly on patient care, morbidity, and mortality. Interprofessional learning (IPL) has been found to increase collaboration and improve collegial relationships and hence may be an appropriate way to increase conflict resolution skills among healthcare graduates. This study examined transference of conflict resolution skills, motivation-to-learn, and attitudes to IPL of medical (n = 52) and nursing (n = 74) undergraduate students who undertook an IPL conflict resolution program. Results indicated that motivation-to-learn, attitudes to IPL, and transfer of conflict resolution skills were significantly related to each other, even when controlling for other variables, such as age and gender. When comparing the two groups, undergraduate nursing students were found to have statistically higher motivation-to-learn and transference of conflict resolution skills, and reported a more positive attitude to IPL than medical students. Some of these differences may be attributed to lack of clinical placements for medical students in the first half of their degree at their university, giving them less opportunity to apply the conflict resolution skills taught, as well as less contextual relevance. This may potentially affect their motivation-to-learn and attitude to IPL thus impacting on how they perceive the relevance of learning conflict resolution skills. Without the contextual relevancy of placements at the time of learning for medical students, the newly acquired conflict resolution skills are less likely to transfer to practice in an optimal fashion.

Automatic recognition of the XLHED phenotype from facial images.

X-linked hypohidrotic ectodermal dysplasia (XLHED) is a genetic disorder that affects ectodermal structures and presents with a characteristic facial appearance. The ability of automated facial recognition technology to detect the phenotype from images was assessed . In Phase 1 of this study we examined if the age of male patients affected the technology's recognition. In Phase 2 we investigated how well the technology discriminated affected males cases from female carriers and from individuals with other ectodermal dysplasia syndromes. The system detected XLHED to be the most likely diagnosis in all genetically confirmed affected male patients of all ages, and in 55% of heterozygous females. Interestingly, patients with other ED syndromes were also detected by the XLHED-targeted analysis, consistent with shared developmental features. Thus the automated facial recognition system represents a promising non-invasive technology to screen patients at all ages for a possible diagnosis of ectodermal dysplasia, with greatest sensitivity and specificity for males affected with XLHED.