bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐D‐201‐07: A Survey of Radiation Oncology Residents’ Training and Preparedness to Lead Patient Safety Programs in Clinics
Authors: Spraker M , Nyflot M , Ford E , Kane G , Zeng J , Hendrickson K 
Published: July 2016

Purpose:Safety and quality has garnered increased attention in radiation oncology, and physicians and physicists are ideal leaders of clinical patient safety programs. However, it is not clear whether residency programs incorporate formal patient safety training and adequately equip residents to assume this leadership role. A national survey was conducted to evaluate medical and physics residents’ exposure to safety topics and their confidence with the skills required to lead clinical safety programs.Methods:Radiation oncology residents were identified in collaboration with ARRO and AAPM. The survey was released in February 2016 via email using REDCap. This included questions about exposure to safety topics, confidence leading safety programs, and interest in training opportunities (i.e. workshops). Residents rated their exposure, skills, and confidence on 4 or 5‐point scales. Medical and physics residents responses were compared using chi‐square tests.Results:Responses were collected from 56 of 248 (22%) physics and 139 of 690 (20%) medical residents. More than two thirds of all residents had no or only informal exposure to incident learning systems (ILS), root cause analysis (RCA), failure mode and effects analysis (FMEA), and the concept of human factors engineering (HFE). Likewise, 63% of residents had not heard of RO‐ILS. Response distributions were similar, however more physics residents had formal exposure to FMEA (p<0.0001) and felt they were adequately trained to lead FMEAs in clinic (p<0.001) than medical residents. Only 36% of residents felt their patient safety training was adequate, and 58% felt more training would benefit their education.Conclusion:These results demonstrate that, despite increasing desire for patient safety training, medical and physics residents’ exposure to relevant concepts is low. Physics residents had more exposure to FMEA than medical residents, and were more confident in leading FMEA. This suggests that increasing resident exposure to specific topics may increase their confidence.
SU‐D‐BRC‐03: Development and Validation of an Online 2D Dose Verification System for Daily Patient Plan Delivery Accuracy Check
Authors: Zhao J , Hu W , Xing Y , Wu X , Li Y 
Published: July 2016

Purpose:All plan verification systems for particle therapy are designed to do plan verification before treatment. However, the actual dose distributions during patient treatment are not known. This study develops an online 2D dose verification tool to check the daily dose delivery accuracy.Methods:A Siemens particle treatment system with a modulated scanning spot beam is used in our center. In order to do online dose verification, we made a program to reconstruct the delivered 2D dose distributions based on the daily treatment log files and depth dose distributions. In the log files we can get the focus size, position and particle number for each spot. A gamma analysis is used to compare the reconstructed dose distributions with the dose distributions from the TPS to assess the daily dose delivery accuracy. To verify the dose reconstruction algorithm, we compared the reconstructed dose distributions to dose distributions measured using PTW 729XDR ion chamber matrix for 13 real patient plans. Then we analyzed 100 treatment beams (58 carbon and 42 proton) for prostate, lung, ACC, NPC and chordoma patients.Results:For algorithm verification, the gamma passing rate was 97.95% for the 3%/3mm and 92.36% for the 2%/2mm criteria. For patient treatment analysis,the results were 97.7%±1.1% and 91.7%±2.5% for carbon and 89.9%±4.8% and 79.7%±7.7% for proton using 3%/3mm and 2%/2mm criteria, respectively. The reason for the lower passing rate for the proton beam is that the focus size deviations were larger than for the carbon beam. The average focus size deviations were −14.27% and −6.73% for proton and −5.26% and −0.93% for carbon in the x and y direction respectively.Conclusion:The verification software meets our requirements to check for daily dose delivery discrepancies. Such tools can enhance the current treatment plan and delivery verification processes and improve safety of clinical treatments.
SU‐D‐BRB‐01: A Comparison of Learning Methods for Knowledge Based Dose Prediction for Coplanar and Non‐Coplanar Liver Radiotherapy
Authors: Tran A , Ruan D , Woods K , Yu V , Nguyen D , Sheng K 
Published: July 2016

Purpose:The predictive power of knowledge based planning (KBP) has considerable potential in the development of automated treatment planning. Here, we examine the predictive capabilities and accuracy of previously reported KBP methods, as well as an artificial neural networks (ANN) method. Furthermore, we compare the predictive accuracy of these methods on coplanar volumetric‐modulated arc therapy (VMAT) and non‐coplanar 4π radiotherapy.Methods:30 liver SBRT patients previously treated using coplanar VMAT were selected for this study. The patients were re‐planned using 4π radiotherapy, which involves 20 optimally selected non‐coplanar IMRT fields. ANNs were used to incorporate enhanced geometric information including liver and PTV size, prescription dose, patient girth, and proximity to beams. The performance of ANN was compared to three methods from statistical voxel dose learning (SVDL), wherein the doses of voxels sharing the same distance to the PTV are approximated by either taking the median of the distribution, non‐parametric fitting, or skew‐normal fitting. These three methods were shown to be capable of predicting DVH, but only median approximation can predict 3D dose. Prediction methods were tested using leave‐one‐out cross‐validation tests and evaluated using residual sum of squares (RSS) for DVH and 3D dose predictions.Results:DVH prediction using non‐parametric fitting had the lowest average RSS with 0.1176(4π) and 0.1633(VMAT), compared to 0.4879(4π) and 1.8744(VMAT) RSS for ANN. 3D dose prediction with median approximation had lower RSS with 12.02(4π) and 29.22(VMAT), compared to 27.95(4π) and 130.9(VMAT) for ANN.Conclusion:Paradoxically, although the ANNs included geometric features in addition to the distances to the PTV, it did not perform better in predicting DVH or 3D dose compared to simpler, faster methods based on the distances alone. The study further confirms that the prediction of 4π non‐coplanar plans were more accurate than VMAT.NIH R43CA183390 and R01CA188300
SU‐D‐BRB‐02: Combining a Commercial Autoplanning Engine with Database Dose Predictions to Further Improve Plan Quality
Authors: Robertson SP , Moore JA , Hui X , DeWeese TL , Tran P , Quon H , Cheng Z , Bzdusek K , Kumar P , McNutt TR 
Published: July 2016

Purpose:Database dose predictions and a commercial autoplanning engine both improve treatment plan quality in different but complimentary ways. The combination of these planning techniques is hypothesized to further improve plan quality.Methods:Four treatment plans were generated for each of 10 head and neck (HN) and 10 prostate cancer patients, including Plan_A: traditional IMRT optimization using clinically relevant default objectives; Plan_B: traditional IMRT optimization using database dose predictions; Plan_C: autoplanning using default objectives; and Plan_D: autoplanning using database dose predictions. One optimization was used for each planning method. Dose distributions were normalized to 95% of the planning target volume (prostate: 8000 cGy; HN: 7000 cGy). Objectives used in plan optimization and analysis were the larynx (25%, 50%, 90%), left and right parotid glands (50%, 85%), spinal cord (0%, 50%), rectum and bladder (0%, 20%, 50%, 80%), and left and right femoral heads (0%, 70%).Results:All objectives except larynx 25% and 50% resulted in statistically significant differences between plans (Friedman's χ2 ≥ 11.2; p ≤ 0.011). Maximum dose to the rectum (Plans A‐D: 8328, 8395, 8489, 8537 cGy) and bladder (Plans A‐D: 8403, 8448, 8527, 8569 cGy) were significantly increased. All other significant differences reflected a decrease in dose. Plans B‐D were significantly different from Plan_A for 3, 17, and 19 objectives, respectively. Plans C‐D were also significantly different from Plan_B for 8 and 13 objectives, respectively. In one case (cord 50%), Plan_D provided significantly lower dose than plan C (p = 0.003).Conclusion:Combining database dose predictions with a commercial autoplanning engine resulted in significant plan quality differences for the greatest number of objectives. This translated to plan quality improvements in most cases, although special care may be needed for maximum dose constraints. Further evaluation is warranted in a larger cohort across HN, prostate, and other treatment sites.This work is supported by Philips Radiation Oncology Systems.
SU‐D‐BRB‐07: Use of Knowledge‐Based Planning to Evaluate the Need for Motion Management in Lung SBRT
Authors: Lalonde R , Huq M , Heron D 
Published: July 2016

Purpose:The planning margins necessary to safely treat targets in the lung can be reduced depending upon the use of gating or abdominal compression to manage respiratory motion, but these may be uncomfortable for the patient, involve expensive equipment, or increase the length of each treatment fraction. The goal of this project is to use knowledge‐based‐planning (KBP) to evaluate which patients may benefit most from motion management.Methods:The Varian RapidPlan” KBP module was used to estimate doses for VMAT SBRT lung treatment plans with different planning margins corresponding to 1) standard lung setup (1.0 cm radial margin, 2.0 cm S‐I margin), 2) daily IGRT (0.7 cm radial 1.5 cm S‐I), 3) daily IGRT with motion management (0.5 cm radial, 1.0 cm S‐I). The estimated doses were then compared with treatment plan doses (AAA dose calculation) generated using the RapidPlan™ optimization constraints. Treatment plan doses were then compared against RTOG protocol dose limits for organs at risk.Results:The OAR doses were within or below the estimated range generated by the KBP system in 100% of plans. With standard margins, only 10% of the plans were able to meet all RTOG dose goals, compared with 75% of plans with IGRT margins. All plans were able to meet RTOG dose guidelines with motion management. The DVH estimation was able to correctly identify 100% of the plans in which one or more OAR doses exceeded RTOG guidelines. Also, the dose estimates correctly identified 88% of individual OAR doses exceeding RTOG guidelines.Conclusion:The KBP system was able to accurately predict the achievable doses in treatment plans, and predict patients in which RTOG dose guidelines would be exceeded. The system may be a useful tool in identifying patients where motion management may be necessary for SBRT treatment.This work was supported by Varian Medical Systems.
SU‐D‐BRA‐02: Motion Assessment During Open Face Mask SRS Using CBCT and Surface Monitoring
Authors: Williams BB , Fox CJ , Hartford AC , Gladstone DJ 
Published: July 2016

Purpose:To assess the robustness of immobilization using open‐face mask technology for linac‐based stereotactic radiosurgery (SRS) with multiple non‐coplanar arcs via repeated CBCT acquisition, with comparison to contemporaneous optical surface tracking data.Methods:25 patients were treated in open faced masks with cranial SRS using 3–4 non‐coplanar arcs. Repeated CBCT imaging was performed to verify the maintenance of proper patient positioning during treatment. Initial patient positioning was performed based on prescribed shifts and optical surface tracking. Positioning refinements employed rigid 3D‐matching of the planning CT and CBCT images and were implemented via automated 6DOF couch control. CBCT imaging was repeated following the treatment of all non‐transverse beams with associated couch kicks. Detected patient translations and rotations were recorded and automatically corrected. Optical surface tracking was applied throughout the treatments to monitor motion, and this contemporaneous patient positioning data was recorded to compare against CBCT data and 6DOF couch adjustments.Results:Initial patient positions were refined on average by translations of 3±1mm and rotations of ±0.9‐degrees. Optical surface tracking corroborated couch corrections to within 1±1mm and ±0.4‐degrees. Following treatment of the transverse and subsequent superior‐oblique beam, average translations of 0.6±0.4mm and rotations of ±0.4‐degrees were reported via CBCT, with optical surface tracking in agreement to within 1.1±0.6mm and ±0.6‐degrees. Following treatment of the third beam, CBCT indicated additional translations of 0.4±0.2mm and rotations of ±0.3‐degrees. Cumulative couch corrections resulted in 0.7 ± 0.4mm average magnitude translations and rotations of ±0.4‐degrees.Conclusion:Based on CBCT measurements of patients during SRS, the open face mask maintained patient positioning to within 1.5mm and 1‐degree with >95% confidence. Patient positioning determined by optical surface tracking agreed with CBCT assessment to within 1±1mm and ±0.6‐degree rotations. These data support the use of 1–2mm PTV margins and repeated CBCT to maintain stereotactic positioning tolerances.
SU‐D‐BRA‐05: Time Series Analysis of EPID Images to Identify Patients in Need of Treatment Adaptation
Authors: Archambault L , Piron O , Varfalvy N 
Published: July 2016

Purpose:to evaluate if time series analysis of portal dose images can be used to identify patients undergoing important anatomical changes.Methods:daily EPID images of every treatment fields were acquired for 48 patients treated for lung cancer. In addition, CBCT were acquired on a regular basis (weekly or biweekly). Gamma analysis was performed relative to the first fraction given that no significant anatomical change was observed on the CBCT of the first fraction compared to the planning CT. Several parameters were extracted from the gamma analysis (e.g. average gamma value, standard deviation, percent above 1). The gamma parameters formed a patient‐specific time series that was analyzed. The first 24 patients were retrospectively evaluated to establish an action threshold that was then applied on the remaining 24 patients. Dosimetric evaluation of patients above that threshold was performed to as assess the level of degradation compared to the initial treatment plan.Results:after performing a clinical retrospective analysis of the first 24 cases, an action threshold on the average gamma value was established at 0.6 and a warning level was set at 0.4. These thresholds were then applied to the remaining 24 cases. Of these, 6 patients (25%) were above the warning level and 4 (17%) were above the action threshold. Dosimetric evaluation was performed on the CBCT for all these 6 patients. Three of these patients had changes above 3% in their PTV coverage, one had changes of about 2% and the remaining two had negligible changes. Patients with the strongest changes all had clear trending in their gamma parameters that could be classified with techniques such as hidden Markov models.Conclusion:by using time series analysis of relative EPID image it was possible to identify a subset of patient most likely to benefit from treatment adaptation.This work was funded in part by Varian Medical Systems
SU‐D‐BRA‐06: Duodenal Interfraction Motion with Abdominal Compression
Authors: Witztum A , Holyoake D , Warren S , Partridge M , Hawkins M 
Published: July 2016

Purpose:To quantify the effect of abdominal compression on duodenal motion during pancreatic radiotherapy.Methods:Seven patients treated for pancreatic cancer were selected for analysis. Four patients were treated with abdominal compression and three without. The duodenum was contoured by the same physician on each CBCT (five CBCTs for patients with compression, four for non‐compression patients). CBCTs were rigidly registered using a soft tissue match and contours were copied to the delivered plans which were all radical (BED > 50 Gy). The distance between the duodenum on the planning CT and each CBCT was quantified by calculating the root mean square (RMS) distance. The DVHs of each abdominal compression patient was converted to an EQD2 DVH (alpha/beta = 10) using an in‐house tool and volumes receiving at least 25, 35, 45, and 50 Gy were recorded.Results:The maximum variation in duodenal volumes on the CBCTs for the four abdominal compression patients were 19.1 cm3 (32.8%), 19.1 cm3 (20.6%), 19.9 cm3 (14.3%), and 12.9 cm3 (27.3%) compared to 15.2 cm3 (17.6%), 34.7 cm3 (83.4%), and 56 cm3 (60.2%) for non‐compression patients. The average RMS distance between the duodenum on the planning CT and each CBCT for all abdominal compression patients was 0.3 cm compared to 0.7 cm for non‐compressed patients. The largest (and average) difference between the planning CT and CBCTs in volume of duodenum receiving more than 25, 35, 45 and 50 Gy for abdominal compression patients was 11% (5%), 9% (3%), 9% (2%), and 6% (1%).Conclusion:Abdominal compression reduces variation in volume and absolute position of the duodenum throughout treatment. This is seen as an improvement but does not eliminate the need to consider dosimetric effects of motion. Abdominal compression is particularly useful in SBRT when only a few fractions are delivered.Alon Witztum is supported by an MRC/Gray Institute DPhil Studentship. Daniel Holyoake is supported by a CRUK/Nuffield Clinical Research Fellowship. Sam Warren and Mike Partridge are supported by CRUK grant C5255/A15935. Maria Hawkins received an MRC Fellowship MC_PC_12001/2.
SU‐D‐202‐02: Quantitative Imaging: Correlation Between Image Feature Analysis and the Accuracy of Manually Drawn Contours On PET Images
Authors: Lamichhane N , Johnson P , Chinea F , Patel V , Yang F 
Published: July 2016

Purpose:To evaluate the correlation between image features and the accuracy of manually drawn target contours on synthetic PET imagesMethods:A digital PET phantom was used in combination with Monte Carlo simulation to create a set of 26 simulated PET images featuring a variety of tumor shapes and activity heterogeneity. These tumor volumes were used as a gold standard in comparisons with manual contours delineated by 10 radiation oncologist on the simulated PET images. Metrics used to evaluate segmentation accuracy included the dice coefficient, false positive dice, false negative dice, symmetric mean absolute surface distance, and absolute volumetric difference. Image features extracted from the simulated tumors consisted of volume, shape complexity, mean curvature, and intensity contrast along with five texture features derived from the gray‐level neighborhood difference matrices including contrast, coarseness, busyness, strength, and complexity. Correlation between these features and contouring accuracy were examined.Results:Contour accuracy was reasonably well correlated with a variety of image features. Dice coefficient ranged from 0.7 to 0.90 and was correlated closely with contrast (r=0.43, p=0.02) and complexity (r=0.5, p<0.001). False negative dice ranged from 0.10 to 0.50 and was correlated closely with contrast (r=0.68, p<0.001) and complexity (r=0.66, p<0.001). Absolute volumetric difference ranged from 0.0002 to 0.67 and was correlated closely with coarseness (r=0.46, p=0.02) and complexity (r=0.49, p=0.008). Symmetric mean absolute difference ranged from 0.02 to 1 and was correlated closely with mean curvature (r=0.57, p=0.02) and contrast (r=0.6, p=0.001).Conclusion:The long term goal of this study is to assess whether contouring variability can be reduced by providing feedback to the practitioner based on image feature analysis. The results are encouraging and will be used to develop a statistical model which will enable a prediction of contour accuracy based purely on image feature analysis.
SU‐D‐202‐03: Statistical Segmentation On Quantitative CT for Assessing Spatial Tumor Response During Radiation Therapy Delivery
Authors: Schott D , Schmidt T , Chen X , Klawikowski S , Noid G , Dalah E , Li X 
Published: July 2016

Purpose:Develop a method to segment regions of interest (ROIs) in tumor with statistically similar Hounsfield unit (HU) values and/or HU changes during chemoradiation therapy (CRT) delivery, to assess spatial tumor treatment response based on daily CTs during CRT delivery.Methods:Generate a three region map of ROIs with differential HUs, by sampling neighboring voxels around a selected voxel and comparing to the mean of the entire ROI using a t‐test. The cumulative distribution function, P, is calculated from the t‐test. The P value is assigned to be the value at the selected voxel, and this is repeated over all voxels in the initial ROI. Three regions are defined as: (1‐P) < 0.00001 (mid region), and 0.00001 < (1‐P) (mean greater than baseline and mean lower than baseline). The test is then expanded to compare daily CT sets acquired during routine CT‐guided RT delivery using a CT‐on‐rails. The first fraction CT is used as the baseline for comparison. We tested 15 pancreatic head tumor cases undergoing CRT, to identify the ROIs and changes corresponding to normal, fibrotic, and tumor tissue. The obtained ROIs were compared with MRI‐ADC maps acquired pre‐ and post‐CRT.Results:The ROIs in 13 out of 15 patients’ first fraction CTs and pre‐CRT MRIs matched the general region and slices covered, as well as in 6 out of the 9 patients with post‐CRT MRIs. The high HU region designated by the t‐test was seen to correlate with the tumor region in MR, and these ROIs are positioned within the same region over the course of treatment. In patients with poorly delineated tumors in MR, the t‐test was inconclusive.Conclusion:The proposed statistical segmentation technique shows the potential to identify regions in tumor with differential HUs and HU changes during CRT delivery for patients with pancreas head cancer.
SU‐D‐202‐04: Validation of Deformable Image Registration Algorithms for Head and Neck Adaptive Radiotherapy in Routine Clinical Setting
Authors: Zhang L , Wang Z , Shi C , Pi Y , Long T , Luo W , Wang F , Chen Z , Xu X 
Published: July 2016

Purpose:To evaluate the ROI contours and accumulated dose difference using different deformable image registration (DIR) algorithms for head and neck (H&N) adaptive radiotherapy.Methods:Eight H&N cancer patients were randomly selected from the affiliated hospital. During the treatment, patients were rescanned every week with ROIs well delineated by radiation oncologist on each weekly CT. New weekly treatment plans were also re‐designed with consistent dose prescription on the rescanned CT and executed for one week on Siemens CT‐on‐rails accelerator. At the end, we got six weekly CT scans from CT1 to CT6 including six weekly treatment plans for each patient. The primary CT1 was set as the reference CT for DIR proceeding with the left five weekly CTs using ANACONDA and MORFEUS algorithms separately in RayStation and the external skin ROI was set to be the controlling ROI both. The entire calculated weekly dose were deformed and accumulated on corresponding reference CT1 according to the deformation vector field (DVFs) generated by the two different DIR algorithms respectively. Thus we got both the ANACONDA‐based and MORFEUS‐based accumulated total dose on CT1 for each patient. At the same time, we mapped the ROIs on CT1 to generate the corresponding ROIs on CT6 using ANACONDA and MORFEUS DIR algorithms. DICE coefficients between the DIR deformed and radiation oncologist delineated ROIs on CT6 were calculated.Results:For DIR accumulated dose, PTV D95 and Left‐Eyeball Dmax show significant differences with 67.13 cGy and 109.29 cGy respectively (Table1). For DIR mapped ROIs, PTV, Spinal cord and Left‐Optic nerve show difference with −0.025, −0.127 and −0.124 (Table2).Conclusion:Even two excellent DIR algorithms can give divergent results for ROI deformation and dose accumulation. As more and more TPS get DIR module integrated, there is an urgent need to realize the potential risk using DIR in clinical.