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.

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.

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.

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.

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.

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.

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.

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.

Long-term disease-specific and cognitive quality of life after intensity-modulated radiation therapy: a cross-sectional survey of nasopharyngeal carcinoma survivors.

BACKGROUND: There is a lack of data on quality of life in long-term survivors of nasopharyngeal carcinoma (NPC) who have been treated with intensity-modulated radiation therapy (IMRT). We characterized long-term disease-specific and cognitive QoL in NPC survivors after IMRT. METHODS: We conducted a cross-sectional study of surviving patients diagnosed and treated for NPC at our center with curative-intent IMRT, with or without chemotherapy. Patients who were deceased, still undergoing treatment, with known recurrent disease, or treated with RT modality other than IMRT were excluded. QoL was measured by FACT-NP and FACT-Cog. RESULTS: Between May and November 2013, 44 patients completed cognitive (FACT-Cog), general (FACT-G), and NPC-specific (NPCS) QoL assessments. Patients were categorized into 4 cohorts based on duration since IMRT (≤2.5, >2.5-6, >6-10, and >10-16 years). There was no significant difference in age (p = 0.20) or stage ((I/II vs III/IV: p = 0.78) among the cohorts. The 4 cohorts differed overall for all QoL measures (ANOVA: p < 0.02 for each), due to improved scores >2.5-6 years post-IMRT compared with ≤2.5 years post-IMRT (post hoc tests: p ≤ 0.04 for each). No differences were observed between >2.5-6 and >6-10 years post-IMRT, but lower mean FACT-Cog and NPCS scores were observed for >10 years compared to >2.5-6 years post-IMRT (post hoc: p < 0.05 for each). CONCLUSIONS: All QoL measures were low during the initial recovery period (≤2.5 years) and were higher by 6 years post-IMRT. At >10 years post-IMRT, lower scores were observed in the domains of NPC-specific and cognitive QoL. Survivors of NPC, even if treated with IMRT, are at risk for detriment in domain-specific QoL measures at very long-term follow-up.

Non-local total-variation (NLTV) minimization combined with reweighted L1-norm for compressed sensing CT reconstruction.

The compressed sensing (CS) technique has been employed to reconstruct CT/CBCT images from fewer projections as it is designed to recover a sparse signal from highly under-sampled measurements. Since the CT image itself cannot be sparse, a variety of transforms were developed to make the image sufficiently sparse. The total-variation (TV) transform with local image gradient in L1-norm was adopted in most cases. This approach, however, which utilizes very local information and penalizes the weight at a constant rate regardless of different degrees of spatial gradient, may not produce qualified reconstructed images from noise-contaminated CT projection data. This work presents a new non-local operator of total-variation (NLTV) to overcome the deficits stated above by utilizing a more global search and non-uniform weight penalization in reconstruction. To further improve the reconstructed results, a reweighted L1-norm that approximates the ideal sparse signal recovery of the L0-norm is incorporated into the NLTV reconstruction with additional iterates. This study tested the proposed reconstruction method (reweighted NLTV) from under-sampled projections of 4 objects and 5 experiments (1 digital phantom with low and high noise scenarios, 1 pelvic CT, and 2 CBCT images). We assessed its performance against the conventional TV, NLTV and reweighted TV transforms in the tissue contrast, reconstruction accuracy, and imaging resolution by comparing contrast-noise-ratio (CNR), normalized root-mean square error (nRMSE), and profiles of the reconstructed images. Relative to the conventional NLTV, combining the reweighted L1-norm with NLTV further enhanced the CNRs by 2-4 times and improved reconstruction accuracy. Overall, except for the digital phantom with low noise simulation, our proposed algorithm produced the reconstructed image with the lowest nRMSEs and the highest CNRs for each experiment.

Presence of cerebral microbleeds is associated with worse executive function in pediatric brain tumor survivors.

BACKGROUND: A specific form of small-vessel vasculopathy-cerebral microbleeds (CMBs)-has been linked to various types of dementia in adults. We assessed the incidence of CMBs and their association with neurocognitive function in pediatric brain tumor survivors. METHODS: In a multi-institutional cohort of 149 pediatric brain tumor patients who received cranial radiation therapy (CRT) between 1987 and 2014 at age <21 years and 16 patients who did not receive CRT, we determined the presence of CMBs on brain MRIs. Neurocognitive function was assessed using a computerized testing program (CogState). We used survival analysis to determine cumulative incidence of CMBs and Poisson regression to examine risk factors for CMBs. Linear regression models were used to assess effect of CMBs on neurocognitive function. RESULTS: The cumulative incidence of CMBs was 48.8% (95% CI: 38.3-60.5) at 5 years. Children who had whole brain irradiation developed CMBs at a rate 4 times greater than those treated with focal irradiation (P < .001). In multivariable analysis, children with CMBs performed worse on the Groton Maze Learning test (GML) compared with those without CMBs (Z-score -1.9; 95% CI: -2.7, -1.1; P < .001), indicating worse executive function when CMBs are present. CMBs in the frontal lobe were associated with worse performance on the GML (Z-score -2.4; 95% CI: -2.9, -1.8; P < .001). Presence of CMBs in the temporal lobes affected verbal memory (Z-score -2.0; 95% CI: -3.3, -0.7; P = .005). CONCLUSION: CMBs are common and associated with neurocognitive dysfunction in pediatric brain tumor survivors treated with radiation.

The level of detail required in a deformable phantom to accurately perform quality assurance of deformable image registration.

The primary purpose of the study was to determine how detailed deformable image registration (DIR) phantoms need to adequately simulate human anatomy and accurately assess the quality of DIR algorithms. In particular, how many distinct tissues are required in a phantom to simulate complex human anatomy? Pelvis and head-and-neck patient CT images were used for this study as virtual phantoms. Two data sets from each site were analyzed. The virtual phantoms were warped to create two pairs consisting of undeformed and deformed images. Otsu's method was employed to create additional segmented image pairs of n distinct soft tissue CT number ranges (fat, muscle, etc). A realistic noise image was added to each image. Deformations were applied in MIM Software (MIM) and Velocity deformable multi-pass (DMP) and compared with the known warping. Images with more simulated tissue levels exhibit more contrast, enabling more accurate results. Deformation error (magnitude of the vector difference between known and predicted deformation) was used as a metric to evaluate how many CT number gray levels are needed for a phantom to serve as a realistic patient proxy. Stabilization of the mean deformation error was reached by three soft tissue levels for Velocity DMP and MIM, though MIM exhibited a persisting difference in accuracy between the discrete images and the unprocessed image pair. A minimum detail of three levels allows a realistic patient proxy for use with Velocity and MIM deformation algorithms.

Evaluation of Heart Dose for Left-Sided Breast Cancer Patients Over an 11-Year Period Spanning the Transition From 2-Dimensional to 3-Dimensional Planning.

INTRODUCTION/BACKGROUND: We evaluated heart dose from left breast radiotherapy with 2-dimensional (2D) versus 3-dimensional (3D) plans. PATIENTS AND METHODS: Treatment plans from patients treated with standard fractionation for left breast cancer from 2003 to 2013 were reviewed, with patients grouped into 3 cohorts: 2003 to 2004 ("2D", with computed tomography scans for dose calculation but fields defined using simulation films; n = 29), 2005 to 2006 ("2D-post," after several influential articles on heart dose were published; n = 31), and 2007 to 2013 ("3D"; n = 256). All patients were treated with free-breathing technique. Heart volumes were retrospectively contoured for the earlier 2 cohorts. Mean heart dose (MHD) and percentage of structure receiving at least 25 Gy (V25 Gy) and percentage of structure receiving at least 5 Gy for the whole heart, left ventricle (LV), right ventricle (RV), and both ventricles were recorded and compared among cohorts. RESULTS: MHD was 345 cGy (2D), 213 cGy (2D-post) and 213 cGy (3D). LV V25 Gy was 6.3%, 1.5%, and 1.1%, respectively. Lower doses were seen over time for all indices (analysis of variance, P < .0001). Post hoc tests indicated significantly higher doses for 2D versus 2D-post or 3D cohorts (P ≤ .001) for all parameters except RV V25 Gy (P = .24). CONCLUSION: Heart doses were higher with 2D versus 3D plans. Cardiac doses and resulting toxicity with modern 3D planning might be lower than those in previous reports.

Does radiation dose matter in thyroid cancer?: Patterns of local-regional failure in recurrent and metastatic well-differentiated thyroid cancers treated with dose-painted intensity-modulated radiation therapy.

INTRODUCTION: Due to complex multimodal treatments and a lengthy natural history of disease, the impact of radiation therapy for well-differentiated thyroid cancer (WDTC) is challenging to evaluate. We analysed the effect of dose escalation, as enabled by intensity-modulated radiation therapy (IMRT), on preventing local-regional failure (LRF) of microscopic and macroscopic WDTC. METHOD: We performed a retrospective review of WDTC patients treated with IMRT from 1998-2011. Diagnostic imaging demonstrating first LRF was registered to the simulation CT containing the treated radiation isodose volumes. Areas of disease progression were contoured and the relationships of LRFs with isodose volumes were recorded. RESULTS: Thirty patients had a median follow-up of 56 months (range = 1-139). Seventeen (57%) had gross residual, five (17%) had microscopic residual and eight (27%) had clear margins at the time of IMRT. Nine patients (30%) developed LRF, at a median time of 44 months (range = 0-116). Of these, six (67%) had been radiated to gross disease and one (11%) had microscopic residual. In the seven analysable cases, only one (14%) LRF occurred within the 70 Gy isodose volume. Marginal LRFs were: four (57%) outside 70 Gy, one (14%) outside 60 Gy and one (14%) outside 50 Gy. All but one recurrence (86%) occurred in the perioesophageal region. CONCLUSIONS: Local-regional failure was seen most in patients who had gross disease at the time of IMRT, almost always occurred outside of the 70 Gy volume and was frequently in the area of oesophageal sparing. Meticulous surgical dissection, especially in the perioesophageal region, should be prioritised to prevent long-term LRF.

An automated deformable image registration evaluation of confidence tool.

Deformable image registration (DIR) is a powerful tool for radiation oncology, but it can produce errors. Beyond this, DIR accuracy is not a fixed quantity and varies on a case-by-case basis. The purpose of this study is to explore the possibility of an automated program to create a patient- and voxel-specific evaluation of DIR accuracy. AUTODIRECT is a software tool that was developed to perform this evaluation for the application of a clinical DIR algorithm to a set of patient images. In brief, AUTODIRECT uses algorithms to generate deformations and applies them to these images (along with processing) to generate sets of test images, with known deformations that are similar to the actual ones and with realistic noise properties. The clinical DIR algorithm is applied to these test image sets (currently 4). From these tests, AUTODIRECT generates spatial and dose uncertainty estimates for each image voxel based on a Student's t distribution. In this study, four commercially available DIR algorithms were used to deform a dose distribution associated with a virtual pelvic phantom image set, and AUTODIRECT was used to generate dose uncertainty estimates for each deformation. The virtual phantom image set has a known ground-truth deformation, so the true dose-warping errors of the DIR algorithms were also known. AUTODIRECT predicted error patterns that closely matched the actual error spatial distribution. On average AUTODIRECT overestimated the magnitude of the dose errors, but tuning the AUTODIRECT algorithms should improve agreement. This proof-of-principle test demonstrates the potential for the AUTODIRECT algorithm as an empirical method to predict DIR errors.

Split-field vs extended-field intensity-modulated radiation therapy plans for oropharyngeal cancer: Which spares the larynx? Which spares the thyroid?

Radiation of the low neck can be accomplished using split-field intensity-modulated radiation therapy (sf-IMRT) or extended-field intensity-modulated radiation therapy (ef-IMRT). We evaluated the effect of these treatment choices on target coverage and thyroid and larynx doses. Using data from 14 patients with cancers of the oropharynx, we compared the following 3 strategies for radiating the low neck: (1) extended-field IMRT, (2) traditional split-field IMRT with an initial cord-junction block to 40Gy, followed by a full-cord block to 50Gy, and (3) split-field IMRT with a full-cord block to 50Gy. Patients were planned using each of these 3 techniques. To facilitate comparison, extended-field plans were normalized to deliver 50Gy to 95% of the neck volume. Target coverage was assessed using the dose to 95% of the neck volume (D95). Mean thyroid and larynx doses were computed. Extended-field IMRT was used as the reference arm; the mean larynx dose was 25.7 ± 7.4Gy, and the mean thyroid dose was 28.6 ± 2.4Gy. Split-field IMRT with 2-step blocking reduced laryngeal dose (mean larynx dose 15.2 ± 5.1Gy) at the cost of a moderate reduction in target coverage (D95 41.4 ± 14Gy) and much higher thyroid dose (mean thyroid dose 44.7 ± 3.7Gy). Split-field IMRT with initial full-cord block resulted in greater laryngeal sparing (mean larynx dose 14.2 ± 5.1Gy) and only a moderately higher thyroid dose (mean thyroid dose 31 ± 8Gy) but resulted in a significant reduction in target coverage (D95 34.4 ± 15Gy). Extended-field IMRT comprehensively covers the low neck and achieves acceptable thyroid and laryngeal sparing. Split-field IMRT with a full-cord block reduces laryngeal doses to less than 20Gy and spares the thyroid, at the cost of substantially reduced coverage of the low neck. Traditional 2-step split-field IMRT similarly reduces the laryngeal dose but also reduces low-neck coverage and delivers very high doses to the thyroid.

Monte Carlo modeling of ultrasound probes for image guided radiotherapy.

PURPOSE: To build Monte Carlo (MC) models of two ultrasound (US) probes and to quantify the effect of beam attenuation due to the US probes for radiation therapy delivered under real-time US image guidance. METHODS: MC models of two Philips US probes, an X6-1 matrix-array transducer and a C5-2 curved-array transducer, were built based on their megavoltage (MV) CT images acquired in a Tomotherapy machine with a 3.5 MV beam in the EGSnrc, BEAMnrc, and DOSXYZnrc codes. Mass densities in the probes were assigned based on an electron density calibration phantom consisting of cylinders with mass densities between 0.2 and 8.0 g/cm(3). Beam attenuation due to the US probes in horizontal (for both probes) and vertical (for the X6-1 probe) orientation was measured in a solid water phantom for 6 and 15 MV (15 × 15) cm(2) beams with a 2D ionization chamber array and radiographic films at 5 cm depth. The MC models of the US probes were validated by comparison of the measured dose distributions and dose distributions predicted by MC. Attenuation of depth dose in the (15 × 15) cm(2) beams and small circular beams due to the presence of the probes was assessed by means of MC simulations. RESULTS: The 3.5 MV CT number to mass density calibration curve was found to be linear with R(2) > 0.99. The maximum mass densities in the X6-1 and C5-2 probes were found to be 4.8 and 5.2 g/cm(3), respectively. Dose profile differences between MC simulations and measurements of less than 3% for US probes in horizontal orientation were found, with the exception of the penumbra region. The largest 6% dose difference was observed in dose profiles of the X6-1 probe placed in vertical orientation, which was attributed to inadequate modeling of the probe cable. Gamma analysis of the simulated and measured doses showed that over 96% of measurement points passed the 3%/3 mm criteria for both probes placed in horizontal orientation and for the X6-1 probe in vertical orientation. The X6-1 probe in vertical orientation caused the highest attenuation of the 6 and 15 MV beams, which at 10 cm depth accounted for 33% and 43% decrease compared to the respective (15 × 15) cm(2) open fields. The C5-2 probe in horizontal orientation, on the other hand, caused a dose increase of 10% and 53% for the 6 and 15 MV beams, respectively, in the buildup region at 0.5 cm depth. For the X6-1 probe in vertical orientation, the dose at 5 cm depth for the 3-cm diameter 6 MV and 5-cm diameter 15 MV beams was attenuated compared to the corresponding open fields to a greater degree by 65% and 43%, respectively. CONCLUSIONS: MC models of two US probes used for real-time image guidance during radiotherapy have been built. Due to the high beam attenuation of the US probes, the authors generally recommend avoiding delivery of treatment beams that intersect the probe. However, the presented MC models can be effectively integrated into US-guided radiotherapy treatment planning in cases for which beam avoidance is not practical due to anatomy geometry.

Comparison between target margins derived from 4DCT scans and real-time tumor motion tracking: insights from lung tumor patients treated with robotic radiosurgery.

PURPOSE: A unique capability of the CyberKnife system is dynamic target tracking. However, not all patients are eligible for this approach. Rather, their tumors are tracked statically using the vertebral column for alignment. When using static tracking, the internal target volume (ITV) is delineated on the four-dimensional (4D) CT scan and an additional margin is added to account for setup uncertainty [planning target volume (PTV)]. Treatment margins are difficult to estimate due to unpredictable variations in tumor motion and respiratory pattern during the course of treatment. The inability to track the target and detect changes in respiratory characteristics might result in geographic misses and local tumor recurrences. The purpose of this study is to develop a method to evaluate the adequacy of ITV-to-PTV margins for patients treated in this manner. METHODS: Data from 24 patients with lesions in the upper lobe (n = 12), middle lobe (n = 3), and lower lobe (n = 9) were included in this study. Each patient was treated with dynamic tracking and underwent 4DCT scanning at the time of simulation. Data including the 3D coordinates of the target over the course of treatment were extracted from the treatment log files and used to determine actual target motion in the superior-inferior (S-I), anterior-posterior (A-P), and left-right (L-R) directions. Different approaches were used to calculate anisotropic and isotropic margins, assuming that the tumor moves as a rigid body. Anisotropic margins were calculated by separating target motion in the three anatomical directions, and a uniform margin was calculated by shifting the gross tumor volume contours in the 3D space and by computing the percentage of overlap with the PTV. The analysis was validated by means of a theoretical formulation. RESULTS: The three methods provided consistent results. A uniform margin of 4.5 mm around the ITV was necessary to assure 95% target coverage for 95% of the fractions included in the analysis. In the case of anisotropic margins, the expansion required in the S-I direction was larger (8.1 mm) than those in the L-R (4.9 mm) and A-P (4.5 mm) directions. This margin accounts for variations of target position within the same treatment fraction. CONCLUSIONS: The use of bony alignment for CyberKnife lung stereotactic body radiation therapy requires careful considerations, in terms of the potential for increased toxicity or local miss. Our method could be used by other centers to determine the adequacy of ITV-to-PTV margins for their patients.