bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐F‐T‐92: Clinical Benefit for Breast and Chest Wall Setup in Using a Breast Board
Authors: Li S , Miyamoto C , Serratore D , Liang Q , Dziemianowicz E 
Published: July 2016

Purpose:To validate benefit of using a breast board (BB) by analyzing the geometry and dosimetry changes of the regions of interest (ROIs) between CT scans with and without BB.Methods:Seven patients, two chest walls (CW) and five breasts, use BB at CT simulation and no BB at diagnostic CT were included. By using deformable image registration software (Velocity AI), diagnostic CT and planning CT were rigidly co‐registered according to the thoracic cage at the target. The heart and the target were then deformedly matched and the contours of the planned ROIs were transferred to the diagnostic CT. Which were brought back to the planning CT data set though the initial rigid co‐registration in order to keep the deformed ROIs redefined in the diagnostic CT. Anatomic shifts and volume changes of a ROI beyond the rigid translation were recorded and dosimetry changes to ROIs were compared with recalculated DVHs.Results:Patient setup without the BB had small but systematic heart shifts superiorly by ∼5 mm. Torso rotations in two cases moved the heart in opposite directions by ∼10 mm. The breast target volume, shape, and locations were significantly changed with arm extension over the head but not in cases with the arm extended laterally. Breast setup without BB could increase the mean dose to the heart and the maximal dose to the anterior ventricle wall by 1.1 and 6.7 Gy, respectively.Conclusion:A method for evaluation of breast setup technique is introduced and applied for patients. Results of systematic heart displacement without using the BB and the potential increase of heart doses encourage us to further investigate the current trend of not using a BB for easy setup and CT scans. Using a BB would likely increase patient sag during prolonged IMRT and real‐time patient position monitoring is clinically desired.
SU‐F‐T‐94: Plan2pdf ‐ a Software Tool for Automatic Plan Report for Philips Pinnacle TPS
Authors: Wu C 
Published: July 2016

Purpose:To implement an automatic electronic PDF plan reporting tool for Philips Pinnacle treatment planning system (TPS)Methods:An electronic treatment plan reporting software is developed by us to enable fully automatic PDF report from Pinnacle TPS to external EMR programs such as MOSAIQ. The tool is named “plan2pdf”. plan2pdf is implemented using Pinnacle scripts, Java and UNIX shell scripts, without any external program needed. plan2pdf supports full auto‐mode and manual mode reporting. In full auto‐mode, with a single mouse click, plan2pdf will generate a detailed Pinnacle plan report in PDF format, which includes customizable cover page, Pinnacle plan summary, orthogonal views through each plan POI and maximum dose point, DRR for each beam, serial transverse views captured throughout the dose grid at a user specified interval, DVH and scorecard windows. The final PDF report is also automatically bookmarked for each section above for convenient plan review. The final PDF report can either be saved on a user specified folder on Pinnacle, or it can be automatically exported to an EMR import folder via a user configured FTP service. In manual capture mode, plan2pdf allows users to capture any Pinnacle plan by full screen, individual window or rectangular ROI drawn on screen. Furthermore, to avoid possible patients’ plan mix‐up during auto‐mode reporting, a user conflict check feature is included in plan2pdf: it prompts user to wait if another patient is being exported by plan2pdf by another user.Results:plan2pdf is tested extensively and successfully at our institution consists of 5 centers, 15 dosimetrists and 10 physicists, running Pinnacle version 9.10 on Enterprise servers.Conclusion:plan2pdf provides a highly efficient, user friendly and clinical proven platform for all Philips Pinnacle users, to generate a detailed plan report in PDF format for external EMR systems.
SU‐F‐T‐96: A New Paradigm of Weekly Chart Checking for Radiation Therapy Clinics
Authors: Tan J , Pompos A , Jiang S , Yan Y 
Published: July 2016

Purpose:To put forth an innovative clinical paradigm for weekly chart checking so that treatment status is periodically checked accurately and efficiently. This study also aims to help optimize the chart checking clinical workflow in a busy radiation therapy clinic.Methods:It is mandated by the Texas Administrative code to check patient charts of radiation therapy once a week or every five fractions, however it varies drastically among institutions in terms of when and how it is done. Some do it every day, but a lot of efforts are wasted on opening ineligible charts; some do it on a fixed day but the distribution of intervals between subsequent checks is not optimal. To establish an optimal chart checking procedure, a new paradigm was developed to achieve 1) charts are checked more accurately and more efficiently; 2) charts are checked on optimal days without any miss; 3) workload is evened out throughout a week when multiple physicists are involved. All active charts will be accessed by querying the R&V system. Priority is assigned to each chart based on the number of days before the next due date followed by sorting and workload distribution steps. New charts are also taken into account when distributing the workload so it is reasonably even throughout the week.Results:Our clinical workflow became more streamlined and smooth. In addition, charts get checked in a more timely fashion so that errors would get caught earlier should they occur.Conclusion:We developed a new weekly chart checking diagram. It helps physicists check charts in a timely manner, saves their time in busy clinics, and consequently reduces possible errors.
SU‐F‐T‐98: Knowledge Modeling for The Outcome of Brain Stereotactic Radiosurgery
Authors: Wu Q , Hauck J , Yuan L , Kirkpatrick J 
Published: July 2016

Purpose:To create a model that will better predict the survival time for patients that were treated with stereotactic radiosurgery for brain metastases using a support vector machine regression model.Methods:This study utilized data from 481 patients, which were equally divided into training and validation datasets randomly. The predictor variables for the SVM model consisted of the actual survival time of the patient, the number of brain metastases, the GPA and KPS scores, prescription dose, and the size of the largest PTV. The resulting survival time predictions were analyzed against the actual survival times by single parameter classification and two‐parameter classification. The predicted mean survival times within each classification are compared with the actual values to obtain the confidence interval associated with the model's predictions.Results:The number of metastases and KPS scores, were consistently shown to be the strongest predictors after single parameter classification, and were thus chosen for as first classifiers for the two‐parameter classification. When the survival times were analyzed with the number of metastases as the first classifier, the best correlation was obtained for patients with 3 metastases, while patients with 4 or 5 metastases had significantly worse results. When the KPS score was used as the first classifier, patients with a KPS score of 60 and 70 had similar strong correlation results.Conclusion:The number of metastases and the KPS score both showed to be good predictors of patient survival time. The model was less accurate for patients with more metastases and certain KPS score ranges due to the lack of training data.
SU‐F‐T‐99: Data Visualization From a Treatment Planning Tracking System for Radiation Oncology
Authors: Cline K , Kabat C , Li Y , Ha C , Kirby N , Stathakis S 
Published: July 2016

Purpose:A treatment planning process tracker database with input forms and a TV‐viewable display webpage was developed and implemented in our clinic to collect time data points throughout the process. Tracking plan times is important because it directly affects the patient quality of care. Simply, the longer a patient waits after their initial simulation CT for treatment to begin, the more time the cancer has to progress. The tracker helps to drive workflow through the clinic, while the data collected can be used to understand and manage the process to find and eliminate inefficiencies.Methods:The overall process steps tracked are CT‐simulation, mark patient, draw normal contours, draw target volumes, create plan, and review/approve plan. Time stamps for task completion were extracted and used to generate a set of clinic metrics, among which include average time for each step in the process split apart by type of treatment, average time to completion for plans started in a given week, and individual overall completion time per plan.Results:Trends have been tracked for fourteen weeks of clinical data (196 plans). On average, drawing normal contours and target volumes is taking 2–5 times as long as creating the plan itself. This is potentially an issue because it could mean the process is taking too long initially, and it could be forcing the planning step to be done in a short amount of time. We also saw from our graphs that there appears to be no clear trend on the average amount of time per plan week‐to‐week.Conclusion:A tracker of this type has the potential to provide insight into how time is utilized in our clinic. By equipping our dosimetrists, radiation oncologists, and physicists with individualized metric sets, the tracker can help provide visibility and drive workflow.Funded in part by CPRIT (RP140105).
SU‐F‐T‐105: The Predicted Radiation Response of Non‐Small Cell Lung Cancer for SRS, SBRT and HDR Brachytherapy
Authors: Huang Z , Feng Y , Lo S , Williams T , Grecula J , Mayr N , Yuh W 
Published: July 2016

Purpose:The data on the α/β ratio of non‐small cell lung cancer (NSCLC) is scarce in the literature. We have previously proposed a generalized LQ (gLQ) model to address the high dose dilemma of the LQ model. In this study, we applied the gLQ model to both the patients and in vitro cell irradiation data treated with a large range of doses, and investigated the α/β ratio in NSCLC.Methods:150 patients with T1T2 and non‐T1T2 stages were treated with stereotactic body radiotherapy (SBRT). In vitro datasets of 14 NSCLC cell lines from the National Cancer Institute published in Eur J Cancer Clin Oncol. 25(3):527–534 (1989) and 7 NSCLC cell lines published in Cancer Res 57:4285–300 (1997) were included. The gLQ model was used to fit datasets. The least χ2 method was adopted to determine the goodness of fit. Errors of the model parameters were determined by propagating minimal χ2. The α/β ratios from both the patients and these in vitro NSCLC cell lines were obtained.Results:The average of α/β ratios for T1T2 and non‐T1T2 NSCLC was 1.45 Gy. The same type of cell lines irradiated with different modalities but almost the same dose rate yielded approximately the same α/β ratio. The average of α/β ratios for NSCLC cell lines in this study was 5.45 Gy.Conclusion:The difference in the α/β ratios between the patients and in vitro cell data is expected and the lower α/β ratio for patients suggests the higher radiosensitivity, which could be associated with higher tumor perfusion or other tumor microenvironmental effects. The α/β ratios derived from the gLQ model can be used in high dose regions or high fraction sizes and are useful to extend our clinical experience accumulated from conversional low‐dose fractionation to high dose irradiation schedules.
SU‐F‐T‐109: A Shortcoming of the Fisher‐Kolmogorov Reaction‐Diffusion Equation for Modeling Tumor Growth
Authors: Dahlman E , Watanabe Y 
Published: July 2016

Purpose:To demonstrate the shortcoming of the Fisher‐Kolmogorov reaction‐diffusion (FK) equation for modeling the exponential tumor growth through a semi‐analytical method and full numerical solutions.Methods:The FK equation is increasingly often chosen as the mathematical equation, by which one can model the time‐dependent variation of the tumor volume in multi‐dimensions after a therapeutic intervention of cancer therapy. The hall‐mark of the cancer growth is its exponential growth, in particular, at its early stage of the development. In this study, we solved the FK equation in a slab geometry analytically, which lead to a simple formula of the solution with one integral term. Also, a full numerical solution of the FK equation was accomplished in the spherical coordinate system. The former gave us an insight on the characteristics of the solution. The latter method provided the data for the tumor growth as the function of time with biologically sound model parameters.Results:The semi‐analytical solution showed that the tumor volume only grows slower than the exponential function of the time. The numerical solutions clearly demonstrated that the tumor volume growth can be very well approximated by a second‐order polynomial function instead of an exponential function.Conclusion:The FK equation is not adequate for modeling the tumor volume variation, in particular, in the early stage of the development, since the solution only can provide the growth in the second order polynomial function of the time.
SU‐F‐T‐117: A Pilot Study of Organ Dose Reconstruction for Wilms Tumor Patients Treated with Radiation Therapy
Authors: Makkia R , Gopalakrishnan M , Lee C , Mille M , Pelletier C , Kalapurakal J , Lee C , Jung J 
Published: July 2016

Purpose:To reconstruct major organ doses for the Wilms tumor pediatric patients treated with radiation therapy using pediatric computational phantoms, treatment planning system (TPS), and Monte Carlo (MC) dose calculation methods.Methods:A total of ten female and male pediatric patients (15–88 months old) were selected from the National Wilms Tumor Study cohort and ten pediatric computational phantoms corresponding to the patient's height and weight were selected for the organ dose reconstruction. Treatment plans were reconstructed on the computational phantoms in a Pinnacle TPS (v9.10) referring to treatment records and exported into DICOM‐RT files, which were then used to generate the input files for XVMC MC code. The mean doses to major organs and the dose received by 50% of the heart were calculated and compared between TPS and MC calculations. The same calculations were conducted by replacing the computational human phantoms with a series of diagnostic patient CT images selected by matching the height and weight of the patients to validate the anatomical accuracy of the computational phantoms.Results:Dose to organs located within the treatment fields from the computational phantoms and the diagnostic patient CT images agreed within 2% for all cases for both TPS and MC calculations. The maximum difference of organ doses was 55.9 % (thyroid), but the absolute dose difference in this case was 0.33 Gy which was 0.96% of the prescription dose. The doses to ovaries and testes from MC in out‐of‐field provided more discrepancy (the maximum difference of 13.2% and 50.8%, respectively). The maximum difference of the 50% heart volume dose between the phantoms and the patient CT images was 40.0%.Conclusion:This study showed the pediatric computational phantoms are applicable to organ doses reconstruction for the radiotherapy patients whose three‐dimensional radiological images are not available.
SU‐F‐T‐118: Characterization of Change in Fractional Anisotropy After Radiation Therapy: Does Nearby Disruption Predict for White Matter Damage?
Authors: Pettersson N , Karunamuni R , Connor M , Moiseenko V , Dale A , Bartsch H , Brewer J , Krishnan A , Kuperman J , Hagler D , McDonald C , Farid N , White N , Hattangadi‐Gluth J , Cervino L 
Published: July 2016

Purpose:We investigated predictors of fractional anisotropy (FA) change in the corticospinal white matter tract (CST) following radiation therapy (RT).Methods:Diffusion tensor imaging (DTI) is a non‐invasive modality which models water diffusion properties. FA quantifies the extent of directional bias—a decrease indicates disrupted white matter integrity. Fifteen patients with high‐grade glioma underwent DTI scans before, and ten months after RT to 59.4–60 Gy. The CST was segmented using an automated atlas‐based algorithm on all DTI images. Treatment planning CT and DTI images were aligned using non‐linear registration allowing for baseline FA, follow‐up FA, and absorbed dose to be determined in each voxel. Relative FA change was dichotomized into a binary outcome using 25% decrease as cutoff. Three metrics were assessed as predictors: voxel dose, distance from the voxel to the center of the CST (Rc), and the number of neighboring voxels (Nadj from 0 to 26) with ≥25% decrease in FA. Logistic regression and the area under the receiver‐operating characteristics curve (AUC) analysis were performed for each patient.Results:Median age of the cohort was 59 years (range: 40–85). The average number of voxels in the CST amongst all patients was 1181 (±172, SD). In logistic regression, the probability of FA change was highly associated with Nadj in all 15 patients with corresponding AUCs between 0.81 and 0.97. With all three metrics included in the logistic regression models, Nadj was highly significant (p<0.001) in all patients, voxel dose significant (p<0.05) in 3/15 patients, and Rc significant in 12/15 patients (p<0.05).Conclusion:The number of neighboring voxels with change in FA was the dominant predictor of FA change at any given voxel. This suggests that the microenvironment of surrounding white matter disruption after radiation therapy may drive local effects along a white matter tract.Pettersson and Cervino are funded by a Varian Medical Systems grant.
SU‐F‐T‐122: 4Dand 5D Proton Dose Evaluation with Monte Carlo
Authors: Titt U , Mirkovic D , Yepes P , Liu A , Peeler C , Randenyia S , Mohan R 
Published: July 2016

Purpose:We evaluated uncertainties in therapeutic proton doses of a lung treatment, taking into account intra‐fractional geometry changes, such as breathing, and inter‐fractional changes, such as tumor shrinkage and weight loss.Methods:A Monte Carlo study was performed using four dimensional CT image sets (4DCTs) and weekly repeat imaging (5DCTs) to compute fixed RBE (1.1) and variable RBE weighted dose in an actual lung treatment geometry. The MC2 Monte Carlo system was employed to simulate proton energy deposition and LET distributions according to a thoracic cancer treatment plan developed with a 3D‐CT in a commercial treatment planning system, as well as in each of the phases of 4DCT sets which were recorded weekly throughout the course of the treatment. A cumulative dose distribution in relevant structures was computed and compared to the predictions of the treatment planning system.Results:Using the Monte Carlo method, dose deposition estimates with the lowest possible uncertainties were produced. Comparison with treatment planning predictions indicates that significant uncertainties may be associated with therapeutic lung dose prediction from treatment planning systems, depending on the magnitude of inter‐ and intra‐fractional geometry changes.Conclusion:As this is just a case study, a more systematic investigation accounting for a cohort of patients is warranted; however, this is less practical because Monte Carlo simulations of such cases require enormous computational resources. Hence our study and any future case studies may serve as validation/benchmarking data for faster dose prediction engines, such as the track repeating algorithm, FDC.
SU‐F‐T‐125: Radial Dose Distributions From Carbon Ions of Therapeutic Energies Calculated with Geant4‐DNA
Authors: Vassiliev O 
Published: July 2016

Purpose:Radial dose distribution D(r) is the dose as a function of lateral distance from the path of a heavy charged particle. Its main application is in modelling of biological effects of heavy ions, including applications to hadron therapy. It is the main physical parameter of a broad group of radiobiological models known as the amorphous track models. Our purpose was to calculate D(r) with Monte Carlo for carbon ions of therapeutic energies, find a simple formula for D(r) and fit it to the Monte Carlo data.Methods:All calculations were performed with Geant4‐DNA code, for carbon ion energies from 10 to 400 MeV/u (ranges in water: ∼ 0.4 mm to 27 cm). The spatial resolution of dose distribution in the lateral direction was 1 nm. Electron tracking cut off energy was 11 eV (ionization threshold). The maximum lateral distance considered was 10 µm. Over this distance, D(r) decreases with distance by eight orders of magnitude.Results:All calculated radial dose distributions had a similar shape dominated by the well‐known inverse square dependence on the distance. Deviations from the inverse square law were observed close to the beam path (r<10 nm) and at large distances (r >1 µm). At small and large distances D(r) decreased, respectively, slower and faster than the inverse square of distance. A formula for D(r) consistent with this behavior was found and fitted to the Monte Carlo data. The accuracy of the fit was better than 10% for all distances considered.Conclusion:We have generated a set of radial dose distributions for carbon ions that covers the entire range of therapeutic energies, for distances from the ion path of up to 10 µm. The latter distance is sufficient for most applications because dose beyond 10 µm is extremely low.
SU‐F‐T‐127: Charged Particle Transport in Condensed Media
Authors: Dingfelder M , McLawhorn R , Maertz E , Shinpaugh J 
Published: July 2016

Purpose:Provide quality interaction cross sections for charged particle Monte Carlo (MC) track structure codes and evaluate low energy electron transport in condensed media.Methods:MC methods and codes are often used to model or simulate charged particle radiation transport in matter. Detailed (or event‐by‐event) track structure simulations are of special interest for the modeling of the physical and chemical stages of radiation action with matter and the initial radiation damage to biological systems. They require reliable interaction cross sections of all radiation qualities considered (e.g., electrons, protons, alpha particles, light and heavy ions) with the target material under consideration, mainly in the condensed phase, including liquids and solids. Interaction cross sections are calculated using the dielectric formalism, a mixture of first principles, theoretical modeling and experimental information for bulk and surface transport and implemented into MC track structure codes. Secondary electron emission yields from amorphous solid water (ASW) and thin metal foils (copper and gold) after fast proton impact are simulated and measured experimentally.Results:After considering different transport models for bulk and surface transport and a careful modeling of the experimental geometry, simulated secondary electron emission yields follow the trends of experimental data well. Furthermore, yields for electron emissions below 50 eV are sensitive to differential elastic scattering cross sections.Conclusion:Low‐energy electron transport in condensed media is still a challenge for detailed track structure simulation codes. Interaction cross‐sections, transport models, and target geometry need to be considered adequately.The research was funded in part by NSF Major Research Instrumentation Program 2009, Award Number: 0923270 and by NIH/NCI R01 CA93351.