HIV testing services and HIV self-testing programming within emergency care in Kenya: a qualitative study of healthcare personnel to inform enhanced service delivery approaches.

In Kenya, persons insufficiently engaged in HIV Testing Services (HTS) are often treated in emergency departments (ED). There are limited data from healthcare workers on ED-HTS. A qualitative study was completed to understand challenges and facilitators for ED-HTS and HIV self-testing (HIVST). Data were collected via six focus groups of healthcare workers. Data were inductively analyzed and mapped to the Capability-Opportunity-Motivation Behavioral Model. Focus groups were completed with 49 healthcare workers: 18 nurses, 15 HIV counselors, 10 physicians and 6 administrators. HTS challenges included staff burdens, resources access, deficiencies in systems integration and illness severity. HTS facilitators included education of healthcare workers and patients, services coordination, and specific follow-up processes. HIVST challenges included accuracy concerns, follow-up barriers and psychosocial risks. HIVST facilitators were patient autonomy and confidentiality, resource utilization and ability to reach higher-risk persons. Mapping to the Capability-Opportunity-Motivation Behavioral Model interventions within the domains of knowledge, decision processes, environmental aspects, social influences and professional identities could support enhanced ED-HTS with integrated HIVST delivery. This study provided insights into challenges and facilitators on ED-HTS and identifies pragmatic approaches to improve healthcare workers' behaviors and abilities to provide services to persons already in contact with healthcare.

Diagnostic Accuracy of the World Health Organization Pediatric Emergency Triage, Assessment and Treatment Tool Plus Among Patients Seeking Care in Nairobi, Kenya: A Cross-sectional Study.

INTRODUCTION: The World Health Organization developed Emergency Triage Assessment and Treatment Plus (ETAT+) guidelines to facilitate pediatric care in resource-limited settings. ETAT+ triages patients as nonurgent, priority, or emergency cases, but there is limited research on the performance of ETAT+ regarding patient-oriented outcomes. This study assessed the diagnostic accuracy of ETAT+ in predicting the need for hospital admission in a pediatric emergency unit at Kenyatta National Hospital in Nairobi, Kenya. METHODS: This was a secondary analysis of a cross-sectional study of pediatric emergency unit patients enrolled over a 4-week period using fixed random sampling. Diagnostic accuracy of ETAT+ was evaluated using receiver operating curves (ROCs) and respective 95% confidence intervals (CIs) with associated sensitivity and specificity (reference category: nonurgent). The ROC analysis was performed for the overall population and stratified by age group. RESULTS: A total of 323 patients were studied. The most common reasons for presentation were upper respiratory tract disease (32.8%), gastrointestinal disease (15.5%), and lower respiratory tract disease (12.4%). Two hundred twelve participants were triaged as nonurgent (65.6%), 60 as priority (18.6%), and 51 as emergency (15.8%). In the overall study population, the area under the ROC curve was 0.97 (95% CI, 0.95-0.99). The ETAT+ sensitivity was 93.8% (95% CI, 87.0%-99.0%), and the specificity was 82.0% (95% CI, 77.0%-87.0%) for admission of priority group patients. The sensitivity and specificity for the emergency patients were 66.0% (95% CI, 55.0%-77.0%) and 98.0% (95% CI, 97.0%-100.0%), respectively. CONCLUSIONS: ETAT+ demonstrated diagnostic accuracy for predicting patient need for hospital admission. This finding supports the utility of ETAT+ to inform emergency care practice. Further research on ETAT+ performance in larger populations and additional patient-oriented outcomes would enhance its generalizability and application in resource-limited settings.

Patient factors associated with incidents of aggression in a general inpatient setting.

AIMS AND OBJECTIVES: To determine patient factors associated with aggressive (code grey) events in the setting of a metropolitan hospital during a six-month period, to inform screening and prevention practices. BACKGROUND: Patient aggression continues to place nurses and patients at risk. Nurses need to be able to identify situations that are likely to escalate into aggression in order to ensure their own safety and the quality of care they can provide. Research has focussed on emergency departments and psychiatric units. Approaches that are appropriate for these settings may not fit for the general inpatient setting. DESIGN: A structured audit and epidemiological analysis of hospital population, regarding incidence of aggression. METHODS: A retrospective audit of code grey event reports and medical records of patients admitted to 16 general medical-surgical wards, during a six-month period. All available records of 121 code grey events were audited. Demographic factors for patients with code grey events were compared with factors for 6472 patients admitted. Statistical tests included chi-squared, bivariate and logistic regression. RESULTS: Diagnoses associated with increased risk of code grey were the following: delirium (11 times more likely) and dementia (seven times). Patients were more likely to have a code grey event if they were over 65 years of age (more than twice), were male (more than twice), were a recipient of Veterans' Affairs pension (four times), had never been married or had been admitted through the emergency department (almost twice). CONCLUSION: This study adds to the current knowledge of the distinctive profile of patients in medical-surgical settings who are associated with aggressive events. RELEVANCE TO CLINICAL PRACTICE: It is recommended that nurses increase their focus on assessment of identified risk factors and documentation of behaviours, to help predict aggressive events, and that this focus be supported by hospital safety and care policy.

Evaluating effectiveness of clustering algorithms in multiple target stereotactic radiosurgery.

Objective. Single-isocenter-multiple-target technique for stereotactic radiosurgery (SRS) can reduce treatment duration but risks compromised dose coverage due to potential rotational errors. Clustering targets into two groups can reduce isocenter-target distances, mitigating the impact of rotational uncertainty. However, a comprehensive evaluation of clustering algorithms for SRS is absent. This study addresses this gap by introducing the SRS Target Clustering Framework (Framework), a comprehensive tool that utilizes commonly used clustering algorithms to generate efficient cluster configurations.Approach. The Framework incorporates four distinct optimization objectives based on two key metrics: the isocenter-target distance and the ratio of this distance to the target radius. Agglomerative and weighted agglomerative clustering are employed for minimax and weighted minimax objectives, respectively. K-means and weighted k-means are utilized for sum-of-squares and weighted sum-of-squares objectives. We applied the Framework to 126 SRS plans, comparing results to ground truth solutions obtained through a brute force algorithm.Main results. For the minimax objective, the average maximum isocenter-target distance from agglomerative clustering (4.8 cm) was slightly higher than the ground truth (4.6 cm). Similarly, the weighted agglomerative clustering achieved an average maximum ratio of 15.1 compared to the ground truth of 14.6. Notably, both k-means and weighted k-means clustering showed close agreement (within a precision of 0.1) with the ground truth for average root-mean-square target-isocenter distance and ratio (3.6 cm and 11.1, respectively).Significance. These results demonstrate the Framework's effectiveness in generating clusters for SRS targets. The proposed approach has the potential to become a valuable tool in SRS treatment planning. Furthermore, this study is the first to investigate clustering algorithms for both minimizing maximum and sum-of-squares uncertainty in SRS.

NRG-HN003: Phase I and Expansion Cohort Study of Adjuvant Pembrolizumab, Cisplatin and Radiation Therapy in Pathologically High-Risk Head and Neck Cancer.

The anti-PD1 monoclonal antibody pembrolizumab improves survival in recurrent/metastatic head and neck squamous cell carcinoma (HNSCC). Patients with locoregional, pathologically high-risk HNSCC recur frequently despite adjuvant cisplatin-radiation therapy (CRT). Targeting PD1 may reverse immunosuppression induced by HNSCC and CRT. We conducted a phase I trial with an expansion cohort (n = 20) to determine the recommended phase II schedule (RP2S) for adding fixed-dose pembrolizumab to standard adjuvant CRT. Eligible patients had resected HPV-negative, stage III-IV oral cavity, pharynx, or larynx HNSCC with extracapsular nodal extension or positive margin. RP2S was declared if three or fewer dose-limiting toxicities (DLT) occurred in a cohort of 12. DLT was defined as grade 3 or higher non-hematologic adverse event (AE) related to pembrolizumab, immune-related AE requiring over 2 weeks of systemic steroids, or unacceptable RT delay. A total of 34 patients enrolled at 23 NRG institutions. During the first cohort, only one DLT was observed (fever), thus RP2S was declared as pembrolizumab 200 mg every 3 weeks for eight doses, starting one week before CRT. During expansion, three additional DLTs were observed (wound infection, diverticulitis, nausea). Of the 34 patients, 28 (82%) received five or more doses of pembrolizumab. This regimen was safe and feasible in a cooperative group setting. Further development is warranted.

Validation of the final aperture superposition technique to calculate electron output factors and depth dose curves.

The final aperture superposition technique (FAST) is a method to reproduce rapidly the electron-beam depth dose curves and output factors that would be calculated by a full Monte Carlo simulation. FAST uses precalculated Monte Carlo-based differential dose arrays and performs a superposition of open and shielded contributions to account for arbitrarily shaped insert openings. The objective of this work was to refine and validate the accuracy of the FAST method for a full range of treatment parameters. Compared to full simulations, raw FAST calculations tended to underestimate dose near the surface deposited by particles that crossed the shield-opening interface of the insert. In this study, a set of empirical correction curves was derived to reduce the errors from this "collimator effect." FAST and full simulation calculations were compared for every combination of six beam energies (6-21 MeV), four applicator sizes (10-25 cm), and two source-to-surface distances (SSDs) (100 and 110 cm). Validation tests were performed for a total of 192 fields using four sample insert openings: an open insert and 2, 3, and 5 cm diameter circular openings. Calculations were also performed for four patient inserts with irregularly shaped openings. Using the empirical correction curves, systematic errors were reduced, resulting in mean dose differences of less than 1% of the maximum full simulation dose. FAST relative output factors reproduced full simulation output factors to within 3% for all configurations except for the 2 and 3 cm diameter openings for the 6 and 9 MeV beams at 110 cm SSD. The maximum shift between the FAST and full simulation depth dose curves in the 90%-80% fall-off region was less than 3 mm for 97% of the fields. For the patient insert calculations, differences in output factors and mean differences in depth dose curves were within 1.5% with maximum shifts of 1.5 mm in the 90%-80% fall-off region. A small set measurements also demonstrated 3% accuracy in FAST output factors except for a 5% deviation for a 2 cm diameter insert for the 6 MeV beam at 110 cm SSD. These results demonstrate that FAST can be used to provide output factors and depth dose curves for most clinical cases.

Physical performance and image optimization of megavoltage cone-beam CT.

Megavoltage cone-beam CT (MVCBCT) is the most recent addition to the in-room CT systems developed for image-guided radiation therapy. The first generation MVCBCT system consists of a 6 MV treatment x-ray beam produced by a conventional linear accelerator equipped with a flat panel amorphous silicon detector. The objective of this study was to evaluate the physical performance of MVCBCT in order to optimize the system acquisition and reconstruction parameters for image quality. MVCBCT acquisitions were performed with the clinical system but images were reconstructed and analyzed with a separate research workstation. The geometrical stability and the positioning accuracy of the system were evaluated by comparing geometrical calibrations routinely performed over a period of 12 months. The beam output and detector intensity stability during MVCBCT acquisition were also evaluated by analyzing in-air acquisitions acquired at different exposure levels. Several system parameters were varied to quantify their impact on image quality including the exposure (2.7, 4.5, 9.0, 18.0, and 54.0 MU), the craniocaudal imaging length (2, 5, 15, and 27.4 cm), the voxel size (0.5, 1, and 2 mm), the slice thickness (1, 3, and 5 mm), and the phantom size. For the reconstruction algorithm, the study investigated the effect of binning, averaging and diffusion filtering of raw projections as well as three different projection filters. A head-sized water cylinder was used to measure and improve the uniformity of MVCBCT images. Inserts of different electron densities were placed in a water cylinder to measure the contrast-to-noise ratio (CNR). The spatial resolution was obtained by measuring the point-spread function of the system using an iterative edge blurring technique. Our results showed that the geometric stability and accuracy of MVCBCT were better than 1 mm over a period of 12 months. Beam intensity variations per projection of up to 35.4% were observed for a 2.7 MU MVCBCT acquisition. These variations did not cause noticeable reduction in the image quality. The results on uniformity suggest that the cupping artifact occurring with MVCBCT is mostly due to off-axis response of the detector and not scattered radiation. Simple uniformity correction methods were developed to nearly eliminate this cupping artifact. The spatial resolution of the baseline MVCBCT reconstruction protocol was approximately 2 mm. An optimized reconstruction protocol was developed and showed an improvement of 75% in CNR with a penalty of only 8% in spatial resolution. Using this new reconstruction protocol, large adipose and muscular structures were differentiated at an exposure of 9 MU. A reduction of 36% in CNR was observed on a larger (pelvic-sized) phantom. This study demonstrates that soft-tissue visualization with MVCBCT can be substantially improved with proper system settings. Further improvement is expected from the next generation MVCBCT system with an optimized megavoltage imaging beamline.

Evaluating the impact of extended field-of-view CT reconstructions on CT values and dosimetric accuracy for radiation therapy.

PURPOSE: Wide bore CT scanners use extended field-of-view (eFOV) reconstruction algorithms to attempt to recreate tissue truncated due to large patient habitus. Radiation therapy planning systems rely on accurate CT numbers in order to correctly plan and calculate radiation dose. This study looks at the impact of eFOV reconstructions on CT numbers and radiation dose calculations in real patient geometries. METHODS: A large modular phantom based on real patient geometries was created to surround a CIRS Model 062M phantom. The modular sections included a smooth patient surface, a skin fold in the patient surface, and the addition of arms for simulation of the patient in arms up or arms down position. This phantom was used to evaluate the accuracy of CT numbers for three extended FOV algorithms implemented on Siemens CT scanners: eFOV, HDFOV, and HDProFOV. Six different configurations of the phantoms were scanned and images were reconstructed for the three different extended FOV algorithms. The CIRS phantom inserts and overall phantom geometry were contoured in each image, and the Hounsfield units (HU) numbers were compared to an image of the phantom within the standard scan FOV (sFOV) without the modular sections. To evaluate the effect on dose calculations, six radiotherapy patients previously treated at our institution (three head and neck and three chest/pelvis) whose body circumferences extended past the 50 cm sFOV in the treatment planning CT were used. Images acquired on a Siemens Sensation Open scanner were reconstructed using sFOV, eFOV and HDFOV algorithms. A physician and dosimetrist identified the radiation target, critical organs, and external patient contour. A benchmark CT was created for each patient, consisting of an average of the 3 CT reconstructions with a density override applied to regions containing truncation artifacts. The benchmark CT was used to create an optimal radiation treatment plan. The plan was copied onto each CT reconstruction without density override and dose was recalculated. RESULTS: Tissue extending past the sFOV impacts the HU numbers for tissues inside and outside the sFOV when using extended FOV reconstructions. On average, the HU for all CIRS density inserts in the arms up (arms down) position varied by 43 HU (67 HU), 39 HU (73 HU), and 18 HU (51 HU) for the eFOV, HDFOV, and HDProFOV scans, respectively. In the patient dose calculations, patients with a smooth patient contour had the least deviation from the benchmark in the HDFOV (0.1-0.5%) compared to eFOV (0.4-1.8%) reconstructions. In cases with large amounts of tissue and irregular skin folds, the eFOV deviated the least from the benchmark (range 0.2-0.6% dose difference) compared to HDFOV (range 1.3-1.8% dose difference). CONCLUSIONS: All reconstruction algorithms demonstrated good CT number accuracy in the center of the image. Larger artifacts are seen near and extending outside the scan FOV, however, dose calculations performed using typical beam arrangements using the extended FOV reconstructions were still mostly within 2.5% of best estimated reference values.

A convolutional neural network algorithm for automatic segmentation of head and neck organs at risk using deep lifelong learning.

PURPOSE: This study suggests a lifelong learning-based convolutional neural network (LL-CNN) algorithm as a superior alternative to single-task learning approaches for automatic segmentation of head and neck (OARs) organs at risk. METHODS AND MATERIALS: Lifelong learning-based convolutional neural network was trained on twelve head and neck OARs simultaneously using a multitask learning framework. Once the weights of the shared network were established, the final multitask convolutional layer was replaced by a single-task convolutional layer. The single-task transfer learning network was trained on each OAR separately with early stoppage. The accuracy of LL-CNN was assessed based on Dice score and root-mean-square error (RMSE) compared to manually delineated contours set as the gold standard. LL-CNN was compared with 2D-UNet, 3D-UNet, a single-task CNN (ST-CNN), and a pure multitask CNN (MT-CNN). Training, validation, and testing followed Kaggle competition rules, where 160 patients were used for training, 20 were used for internal validation, and 20 in a separate test set were used to report final prediction accuracies. RESULTS: On average contours generated with LL-CNN had higher Dice coefficients and lower RMSE than 2D-UNet, 3D-Unet, ST- CNN, and MT-CNN. LL-CNN required ~72 hrs to train using a distributed learning framework on 2 Nvidia 1080Ti graphics processing units. LL-CNN required 20 s to predict all 12 OARs, which was approximately as fast as the fastest alternative methods with the exception of MT-CNN. CONCLUSIONS: This study demonstrated that for head and neck organs at risk, LL-CNN achieves a prediction accuracy superior to all alternative algorithms.

Optimizing beam models for dosimetric accuracy over a wide range of treatments.

This work presents a systematic approach for testing a dose calculation algorithm over a variety of conditions designed to span the possible range of clinical treatment plans. Using this method, a TrueBeam STx machine with high definition multi-leaf collimators (MLCs) was commissioned in the RayStation treatment planning system (TPS). The initial model parameters values were determined by comparing TPS calculations with standard measured depth dose and profile curves. The MLC leaf offset calibration was determined by comparing measured and calculated field edges utilizing a wide range of MLC retracted and over-travel positions. The radial fluence was adjusted using profiles through both the center and corners of the largest field size, and through measurements of small fields that were located at highly off-axis positions. The flattening filter source was adjusted to improve the TPS agreement for the output of MLC-defined fields with much larger jaw openings. The MLC leaf transmission and leaf end parameters were adjusted to optimize the TPS agreement for highly modulated intensity-modulated radiotherapy (IMRT) plans. The final model was validated for simple open fields, multiple field configurations, the TG 119 C-shape target test, and a battery of clinical IMRT and volumetric-modulated arc therapy (VMAT) plans. The commissioning process detected potential dosimetric errors of over 10% and resulted in a final model that provided in general 3% dosimetric accuracy. This study demonstrates the importance of using a variety of conditions to adjust a beam model and provides an effective framework for achieving high dosimetric accuracy.

Comparison of dosimetric and biologic effective dose parameters for prostate and urethra using 131 Cs and 125 I for prostate permanent implant brachytherapy.

PURPOSE: To compare the urethral and prostate absolute and biologic effective doses (BEDs) for 131 Cs and 125 I prostate permanent implant brachytherapy (PPI). METHODS AND MATERIALS: Eight previously implanted manually planned 125 I PPI patients were replanned manually with 131 Cs, and re-planned using Inverse Planning Simulated Annealing. 131 Cs activity and the prescribed dose (115 Gy) were determined from that recommended by IsoRay. The BED was calculated for the prostate and urethra using an alpha/beta ratio of 2 and was also calculated for the prostate using an alpha/beta ratio of 6 and a urethral alpha/beta ratio of 2. The primary endpoints of this study were the prostate D90 BED (pD90BED) and urethral D30 BED normalized to the maximal potential prostate D90 BED (nuD30BED). RESULTS: The manual plan comparison (alpha/beta = 2) yielded no significant difference in the prostate D90 BED (median, 192 Gy2 for both isotopes). No significant difference was observed for the nuD30BED (median, 199 Gy2 and 202 Gy2 for 125 I and 131 Cs, respectively). For the inverse planning simulated annealing plan comparisons (alpha/beta = 2), the prostate D90 BED was significantly lower with 131 Cs than with 125 I (median, 177 Gy2 vs. 187 Gy2, respectively; p = 0.01). However, the nuD30BED was significantly greater with 131 Cs than with 125 I (median, 192 Gy2 vs. 189 Gy2, respectively; p = 0.01). Both the manual and the inverse planning simulated annealing plans resulted in a significantly lower prostate D90 BED (p = 0.01) and significantly greater nuD30BED for 131 Cs (p = 0.01), compared with 125 I, when the prostate alpha/beta ratio was 6 and the urethral alpha/beta ratio was 2. CONCLUSION: This report highlights the controversy in comparing the dose to both the prostate and the organs at risk with different radionuclides.

Scalable Electronic Phenotyping For Studying Patient Comorbidities.

Over 75 million Americans have multiple concurrent chronic conditions and medical decision making for these patients is mostly based on retrospective cohort studies. Current methods to generate cohorts of patients with comorbidities are neither scalable nor generalizable. We propose a supervised machine learning algorithm for learning comorbidity phenotypes without requiring manually created training sets. First, we generated myocardial infarction (MI) and type-2 diabetes (T2DM) patient cohorts using ICD9-based imperfectly labeled samples upon which LASSO logistic regression models were trained. Second, we assessed the effects of training sample size, inclusion of physician input, and inclusion of clinical text features on model performance. Using ICD9 codes as our labeling heuristic, we achieved comparable performance to models created using keywords as labeling heuristic. We found that expert input and higher training sample sizes could compensate for the lack of clinical text derived features. However, our best performing model included clinical text as features with a large training sample size.

Large Vessel Arteriopathy After Cranial Radiation Therapy in Pediatric Brain Tumor Survivors.

Among childhood cancer survivors, increased stroke risk after cranial radiation therapy may be caused by radiation-induced arteriopathy, but limited data exist to support this hypothesis. Herein, we assess the timing and presence of cerebral arteriopathy identified by magnetic resonance angiography (MRA) after cranial radiation therapy in childhood brain tumor survivors. In a cohort of 115 pediatric brain tumor survivors, we performed chart abstraction and prospective annual follow-up to assess the presence of large vessel cerebral arteriopathy by MRA. We identified 10 patients with cerebral arteriopathy. The cumulative incidence of arteriopathy 5 years post-cranial radiation therapy was 5.4% (CI 0.6%-10%) and 10 years was 16% (CI 4.6%-26%). One patient had an arterial ischemic stroke 2.4 years post-cranial radiation therapy in the distribution of a radiation-induced stenotic artery. We conclude that large vessel arteriopathies can occur within a few years of cranial radiation therapy and can become apparent on MRA in under a year.

Dose calculation using megavoltage cone-beam CT.

PURPOSE: To demonstrate the feasibility of performing dose calculation on megavoltage cone-beam CT (MVCBCT) of head-and-neck patients in order to track the dosimetric errors produced by anatomic changes. METHODS AND MATERIALS: A simple geometric model was developed using a head-size water cylinder to correct an observed cupping artifact occurring with MVCBCT. The uniformity-corrected MVCBCT was calibrated for physical density. Beam arrangements and weights from the initial treatment plans defined using the conventional CT were applied to the MVCBCT image, and the dose distribution was recalculated. The dosimetric inaccuracies caused by the cupping artifact were evaluated on the water phantom images. An ideal test patient with no observable anatomic changes and a patient imaged with both CT and MVCBCT before and after considerable weight loss were used to clinically validate MVCBCT for dose calculation and to determine the dosimetric impact of large anatomic changes. RESULTS: The nonuniformity of a head-size water phantom ( approximately 30%) causes a dosimetric error of less than 5%. The uniformity correction method developed greatly reduces the cupping artifact, resulting in dosimetric inaccuracies of less than 1%. For the clinical cases, the agreement between the dose distributions calculated using MVCBCT and CT was better than 3% and 3 mm where all tissue was encompassed within the MVCBCT. Dose-volume histograms from the dose calculations on CT and MVCBCT were in excellent agreement. CONCLUSION: MVCBCT can be used to estimate the dosimetric impact of changing anatomy on several structures in the head-and-neck region.

Patient dose considerations for routine megavoltage cone-beam CT imaging.

Megavoltage cone-beam CT (MVCBCT), the recent addition to the family of in-room CT imaging systems for image-guided radiation therapy (IGRT), uses a conventional treatment unit equipped with a flat panel detector to obtain a three-dimensional representation of the patient in treatment position. MVCBCT has been used for more than two years in our clinic for anatomy verification and to improve patient alignment prior to dose delivery. The objective of this research is to evaluate the image acquisition dose delivered to patients for MVCBCT and to develop a simple method to reduce the additional dose resulting from routine MVCBCT imaging. Conventional CT scans of phantoms and patients were imported into a commercial treatment planning system (TPS: Phillips, Pinnacle) and an arc treatment mimicking the MVCBCT acquisition process was generated to compute the delivered acquisition dose. To validate the dose obtained from the TPS, a simple water-equivalent cylindrical phantom with spaces for MOSFETs and an ion chamber was used to measure the MVCBCT image acquisition dose. Absolute dose distributions were obtained by simulating MVCBCTs of 9 and 5 monitor units (MU) on pelvis and head and neck patients, respectively. A compensation factor was introduced to generate composite plans of treatment and MVCBCT imaging dose. The article provides a simple equation to compute the compensation factor. The developed imaging compensation method was tested on routinely used clinical plans for prostate and head and neck patients. The quantitative comparison between the calculated dose by the TPS and measurement points on the cylindrical phantom were all within 3%. The dose percentage difference for the ion chamber placed in the center of the phantom was only 0.2%. For a typical MVCBCT, the dose delivered to patients forms a small anterior-posterior gradient ranging from 0.6 to 1.2 cGy per MVCBCT MU. MVCBCT acquisitions in the pelvis and head and neck areas deliver slightly more dose than current portal imaging but render soft tissue information for positioning. Overall, the additional dose from daily 9 MU MVCBCTs of prostate patients is small compared to the treatment dose (<4%). Dose-volume histograms of compensated plans for pelvis and head and neck patients imaged daily with MVCBCT showed no additional dose to the target and small increases at low doses. The results indicate that the dose delivered for MVCBCT imaging can be precisely calculated in the TPS and therefore included in the treatment plan. This allows simple plan compensations, such as slightly reducing the treatment dose, to minimize the total dose received by critical structures from daily positioning with MVCBCT. The proposed compensation factor reduces the number of MU per treatment beam per fraction. Both the number of fractions and the beam arrangement are kept unchanged. Reducing the imaging volume in the cranio-caudal direction can further reduce the dose delivered for MVCBCT. This is a useful feature to eliminate the imaging dose to the eyes or to focus on a specific region of interest for alignment.

Validating Dose Uncertainty Estimates Produced by AUTODIRECT: An Automated Program to Evaluate Deformable Image Registration Accuracy.

Deformable image registration is a powerful tool for mapping information, such as radiation therapy dose calculations, from one computed tomography image to another. However, deformable image registration is susceptible to mapping errors. Recently, an automated deformable image registration evaluation of confidence tool was proposed to predict voxel-specific deformable image registration dose mapping errors on a patient-by-patient basis. The purpose of this work is to conduct an extensive analysis of automated deformable image registration evaluation of confidence tool to show its effectiveness in estimating dose mapping errors. The proposed format of automated deformable image registration evaluation of confidence tool utilizes 4 simulated patient deformations (3 B-spline-based deformations and 1 rigid transformation) to predict the uncertainty in a deformable image registration algorithm's performance. This workflow is validated for 2 DIR algorithms (B-spline multipass from Velocity and Plastimatch) with 1 physical and 11 virtual phantoms, which have known ground-truth deformations, and with 3 pairs of real patient lung images, which have several hundred identified landmarks. The true dose mapping error distributions closely followed the Student t distributions predicted by automated deformable image registration evaluation of confidence tool for the validation tests: on average, the automated deformable image registration evaluation of confidence tool-produced confidence levels of 50%, 68%, and 95% contained 48.8%, 66.3%, and 93.8% and 50.1%, 67.6%, and 93.8% of the actual errors from Velocity and Plastimatch, respectively. Despite the sparsity of landmark points, the observed error distribution from the 3 lung patient data sets also followed the expected error distribution. The dose error distributions from automated deformable image registration evaluation of confidence tool also demonstrate good resemblance to the true dose error distributions. Automated deformable image registration evaluation of confidence tool was also found to produce accurate confidence intervals for the dose-volume histograms of the deformed dose.

Interfraction Anatomical Variability Can Lead to Significantly Increased Rectal Dose for Patients Undergoing Stereotactic Body Radiotherapy for Prostate Cancer.

Stereotactic body radiotherapy for prostate cancer is rapidly growing in popularity. Stereotactic body radiotherapy plans mimic those of high-dose rate brachytherapy, with tight margins and inhomogeneous dose distributions. The impact of interfraction anatomical changes on the dose received by organs at risk under these conditions has not been well documented. To estimate anatomical variation during stereotactic body radiotherapy, 10 patients were identified who received a prostate boost using robotic stereotactic body radiotherapy after completing 25 fractions of pelvic radiotherapy with daily megavoltage computed tomography. Rectal and bladder volumes were delineated on each megavoltage computed tomography, and the stereotactic body radiotherapy boost plan was registered to each megavoltage computed tomography image using a point-based rigid registration with 3 fiducial markers placed in the prostate. The volume of rectum and bladder receiving 75% of the prescription dose (V75%) was measured for each megavoltage computed tomography. The rectal V75% from the daily megavoltage computed tomographies was significantly greater than the planned V75% (median increase of 0.93 cm3, P < .001), whereas the bladder V75% on megavoltage computed tomography was not significantly changed (median decrease of -0.12 cm3, P = .57). Although daily prostate rotation was significantly correlated with bladder V75% (Spearman ρ = .21, P = .023), there was no association between rotation and rectal V75% or between prostate deformation and either rectal or bladder V75%. Planning organ-at-risk volume-based replanning techniques using either a 6-mm isotropic expansion of the plan rectal contour or a 1-cm expansion from the planning target volume in the superior and posterior directions demonstrated significantly improved rectal V75% on daily megavoltage computed tomographies compared to the original stereotactic body radiotherapy plan, without compromising plan quality. Thus, despite tight margins and full translational and rotational corrections provided by robotic stereotactic body radiotherapy, we find that interfraction anatomical variations can lead to a substantial increase in delivered rectal doses during prostate stereotactic body radiotherapy. A planning organ-at-risk volume-based approach to treatment planning may help mitigate the impact of daily organ motion and reduce the risk of rectal toxicity.

Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making.

BACKGROUND AND PURPOSE: Clinical decision support systems are a growing class of tools with the potential to impact healthcare. This study investigates the construction of a decision support system through which clinicians can efficiently identify which previously approved historical treatment plans are achievable for a new patient to aid in selection of therapy. MATERIAL AND METHODS: Treatment data were collected for early-stage lung and postoperative oropharyngeal cancers treated using photon (lung and head and neck) and proton (head and neck) radiotherapy. Machine-learning classifiers were constructed using patient-specific feature-sets and a library of historical plans. Model accuracy was analyzed using learning curves, and historical treatment plan matching was investigated. RESULTS: Learning curves demonstrate that for these datasets, approximately 45, 60, and 30 patients are needed for a sufficiently accurate classification model for radiotherapy for early-stage lung, postoperative oropharyngeal photon, and postoperative oropharyngeal proton, respectively. The resulting classification model provides a database of previously approved treatment plans that are achievable for a new patient. An exemplary case, highlighting tradeoffs between the heart and chest wall dose while holding target dose constant in two historical plans is provided. CONCLUSIONS: We report on the first artificial-intelligence based clinical decision support system that connects patients to past discrete treatment plans in radiation oncology and demonstrate for the first time how this tool can enable clinicians to use past decisions to help inform current assessments. Clinicians can be informed of dose tradeoffs between critical structures early in the treatment process, enabling more time spent on finding the optimal course of treatment for individual patients.

Image-guided radiotherapy using megavoltage cone-beam computed tomography for treatment of paraspinous tumors in the presence of orthopedic hardware.

PURPOSE: This report describes a new image-guided radiotherapy (IGRT) technique using megavoltage cone-beam computed tomography (MV-CBCT) to treat paraspinous tumors in the presence of orthopedic hardware. METHODS AND MATERIALS: A patient with a resected paraspinous high-grade sarcoma was treated to 59.4 Gy with an IMRT plan. Daily MV-CBCT imaging was used to ensure accurate positioning. The displacement between MV-CBCT and planning CT images were determined daily and applied remotely to the treatment couch. The dose-volume histograms of the original and a hypothetical IMRT plan (shifted by the average daily setup errors) were compared to estimate the impact on dosimetry. RESULTS: The mean setup corrections in the lateral, longitudinal, and vertical directions were 3.6 mm (95% CI, 2.6-4.6 mm), 4.1 mm (95% CI, 3.2-5.0 mm), and 1.0 mm (95% CI, 0.6-1.3 mm), respectively. Without corrected positioning, the dose to 0.1 cc of the spinal cord increased by 9.4 Gy, and the doses to 95% of clinical target volumes 1 and 2 were reduced by 4 Gy and 4.8 Gy, respectively. CONCLUSIONS: Megavoltage-CBCT provides a new alternative image-guided radiotherapy approach for treatment of paraspinous tumors in the presence of orthopedic hardware by providing 3D anatomic information in the treatment position, with clear imaging of metallic objects and without compromising soft-tissue information.

Calibration of an amorphous-silicon flat panel portal imager for exit-beam dosimetry.

Amorphous-silicon flat panel detectors are currently used to acquire digital portal images with excellent image quality for patient alignment before external beam radiation therapy. As a first step towards interpreting portal images acquired during treatment in terms of the actual dose delivered to the patient, a calibration method is developed to convert flat panel portal images to the equivalent water dose deposited in the detector plane and at a depth of 1.5 cm. The method is based on empirical convolution models of dose deposition in the flat panel detector and in water. A series of calibration experiments comparing the response of the flat panel imager and ion chamber measurements of dose in water determines the model parameters. Kernels derived from field size measurements account for the differences in the production and detection of scattered radiation in the two systems. The dissimilar response as a function of beam energy spectrum is characterized from measurements performed at various off-axis positions and for increasing attenuator thickness in the beam. The flat panel pixel inhomogeneity is corrected by comparing a large open field image with profiles measured in water. To verify the accuracy of the calibration method, calibrated flat panel profiles were compared with measured dose profiles for fields delivered through solid water slabs, a solid water phantom containing an air cavity, and an anthropomorphic head phantom. Open rectangular fields of various sizes and locations as well as a multileaf collimator-shaped field were delivered. For all but the smallest field centered about the central axis, the calibrated flat panel profiles matched the measured dose profiles with little or no systematic deviation and approximately 3% (two standard deviations) accuracy for the in-field region. The calibrated flat panel profiles for fields located off the central axis showed a small -1.7% systematic deviation from the measured profiles for the in-field region. Out of the field, the differences between the calibrated flat panel and measured profiles continued to be small, approximately 0%-2% of the mean in-field dose. Further refinement of the calibration model should increase the accuracy of the procedure. This calibration method for flat panel portal imagers may be used as part of a validation scheme to verify the dose delivered to the patient during treatment.