bluuebunny/crossref_metadata_2025 huggingface dataset:
SU‐F‐T‐69: Correction Model of NIPAM Gel and Presage for Electron and Proton PDD Measurement
Authors: Lin C , Chao T , Lin C , Nien H , Tu P , Yueh C , Wu C
Published: July 2016

Purpose:

The current standard equipment for proton PDD measurement is multilayer‐parallel‐ion‐chamber. Disadvantage of multilayer‐parallel‐ion‐chamber is expensive and complexity manipulation. NIPAM‐gel and Presage are options for PDD measurement. Due to different stopping power, the result of NIPAM‐gel and Presage need to be corrected. This study aims to create a correction model for NIPAM‐gel and Presage PDD measurement.

Methods:

Standard water based PDD profiles of electron 6MeV, 12MeV, and proton 90MeV were acquired. Electron PDD profile after 1cm thickness of NIPAM‐gel added on the top of water was measured. Electron PDD profile with extra 1cm thickness of solid water, PTW RW3, was measured. The distance shift among standard PDD, NIPAM‐gel PDD, and solid water PDD at R50% was compared and water equivalent thickness correction factor (WET) was calculated. Similar process was repeated. WETs for electron with Presage, proton with NIPAM‐gel, and proton with Presage were calculated. PDD profiles of electron and proton with NIPAM‐gel and Presage columns were corrected with each WET. The corrected profiles were compared with standard profiles.

Results:

WET for electron 12MeV with NIPAM‐gel was 1.135, and 1.034 for electron 12Mev with Presage. After correction, PDD profile matched to the standard profile at the fall‐off range well. The difference at R50% was 0.26mm shallower and 0.39mm deeper. The same WET was used to correct electron 6MeV profile. Energy independence of electron WET was observed. The difference at R50% was 0.17mm deeper for NIPAM‐gel and 0.54mm deeper for Presage. WET for proton 90MeV with NIPAM‐gel was 1.056. The difference at R50% was 0.37 deeper. Quenching effect at Bragg peak was revealed. The underestimated dose percentage at Bragg peak was 27%.

Conclusion:

This correction model can be used to modify PDD profile with depth error within 1mm. With this correction model, NIPAM‐gel and Presage can be practical at PDD profile measurement.
SU‐F‐T‐74: Experimental Validation of Monaco Electron Monte Carlo Dose Calculation for Small Fields
Authors: Varadhan  , Way S , Arentsen L , Gerbi B
Published: July 2016

Purpose:

To verify experimentally the accuracy of Monaco (Elekta) electron Monte Carlo (eMC) algorithm to calculate small field size depth doses, monitor units and isodose distributions.

Methods:

Beam modeling of eMC algorithm was performed for electron energies of 6, 9, 12 15 and 18 Mev for a Elekta Infinity Linac and all available (6, 10, 14 20 and 25 cone) applicator sizes. Electron cutouts of incrementally smaller field sizes (20, 40, 60 and 80% blocked from open cone) were fabricated. Dose calculation was performed using a grid size smaller than one‐tenth of the R80–20 electron distal falloff distance and number of particle histories was set at 500,000 per cm2. Percent depth dose scans and beam profiles at dmax, d90 and d80 depths were measured for each cutout and energy with Wellhoffer (IBA) Blue Phantom2 scanning system and compared against eMC calculated doses.

Results:

The measured dose and output factors of incrementally reduced cutout sizes (to 3cm diameter) agreed with eMC calculated doses within ± 2.5%. The profile comparisons at dmax, d90 and d80 depths and percent depth doses at reduced field sizes agreed within 2.5% or 2mm.

Conclusion:

Our results indicate that the Monaco eMC algorithm can accurately predict depth doses, isodose distributions, and monitor units in homogeneous water phantom for field sizes as small as 3.0 cm diameter for energies in the 6 to 18 MeV range at 100 cm SSD. Consequently, the old rule of thumb to approximate limiting cutout size for an electron field determined by the lateral scatter equilibrium (E (MeV)/2.5 in centimeters of water) does not apply to Monaco eMC algorithm.
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.
SU‐F‐T‐130: [18F]‐FDG Uptake Dose Response in Lung Correlates Linearly with Proton Therapy Dose
Authors: Kim D , Titt U , Mirkovic D
Published: July 2016

Purpose:

Analysis of clinical outcomes in lung cancer patients treated with protons using 18F‐FDG uptake in lung as a measure of dose response.

Methods:

A test case lung cancer patient was selected in an unbiased way. The test patient's treatment planning and post treatment positron emission tomography (PET) were collected from picture archiving and communication system at the UT M.D. Anderson Cancer Center. Average computerized tomography scan was registered with post PET/CT through both rigid and deformable registrations for selected region of interest (ROI) via VelocityAI imaging informatics software. For the voxels in the ROI, a system that extracts the Standard Uptake Value (SUV) from PET was developed, and the corresponding relative biological effectiveness (RBE) weighted (both variable and constant) dose was computed using the Monte Carlo (MC) methods. The treatment planning system (TPS) dose was also obtained. Using histogram analysis, the voxel average normalized SUV vs. 3 different doses was obtained and linear regression fit was performed.

Results:

From the registration process, there were some regions that showed significant artifacts near the diaphragm and heart region, which yielded poor r‐squared values when the linear regression fit was performed on normalized SUV vs. dose. Excluding these values, TPS fit yielded mean r‐squared value of 0.79 (range 0.61–0.95), constant RBE fit yielded 0.79 (range 0.52–0.94), and variable RBE fit yielded 0.80 (range 0.52–0.94).

Conclusion:

A system that extracts SUV from PET to correlate between normalized SUV and various dose calculations was developed. A linear relation between normalized SUV and all three different doses was found.
SU‐F‐T‐134: Can We Use the Same Dose Constrains Learnt From Photon World to Plan Proton for Lung Cancer?
Authors: Xiao Z , Zou J , Yue N , Zhang M
Published: July 2016

Purpose:

To evaluate if the same DVH constrains used in photon plans can be safely used to plan proton therapy for lung cancer. Since protons and photons have different dose deposition patterns, the hypothesis is following DVH constrains derived from photon world is not safe for proton.

Methods:

We retrospectively evaluated plans for 11 lung cancer patients. Each patient was planned with photon and proton following the same dose constrains. Dose statistics on PTV, normal lung, heart and esophagus were extracted for comparison. gEUD for normal lung was calculated and compared between proton and photon plans. We calculated series of gEUDs for each plan by varying the parameter “a” in gEUD formula from 0.1 to 3, covering the whole confidence interval.

Results:

For all patients, proton plans yield similar PTV coverage and lower dose to heart and esophagus than photon plans. Normal lung V5 was 32.3 % on average in proton plans than 55.4 % in photon. Normal lung gEUD monotonically increased with increasing “a” for all proton and photon plans. For a given patient, the gEUD_proton(a) had a steeper slope than gEUD_photon(a). The two curves crossed for 8 out of 11 patients when “a” = [0.1, 3]. a_crossing ranged from 0.8 to 2.44 with an average of 1.15. For a<a_crossing, gEUD_proton was less than gEUD_photon and vice versa.

Conclusion:

The current clinical guideline is the lower normal lung V5 would associated with less complications. However, proton plans with a lower normal lung V5 could yield a higher gEUD than photon if the real “a” is larger than a_crossing. Since a_crossing was within the possible range of real “a”, simply following the normal lung V5 guideline for proton plan would not be a good practice. More comprehensive methods should be developed to evaluate the proton plan.
SU‐F‐T‐135: A Retrospective Analysis of the Impact of Range Uncertainty in Brain Patients
Authors: Grantham K , Santanam L , Goddu S , Sun B , Zhang T , Mutic S , Robinson C , Huang J , Perkins S , Tsien C , Bradley J , Zhao T
Published: July 2016

Purpose:

We retrospectively evaluate the dosimetric impact of a 3.5% range uncertainty on CTV coverage and normal organ toxicity for a cohort of brain patients.

Methods:

Twenty treatment plans involving 20 brain cancer patients treated with Mevions S250 were reviewed. Forty uncertain plans were made by changing the ranges in original plans by ±3.5% while keeping all devices unchanged. Fidelity to the original plans was evaluated with gamma index. Changes in generalized equivalent uniform dose (gEUD) were reported for the following structures: CTV coverage, brainstem, optic chiasm, and optic nerves. Comparisons were made by plotting the relevant endpoints from the uncertain plans as a function of the same endpoints from the original clinical plan.

Results:

Gamma‐index analysis resulted in a 50% pass rate of the uncertain plans using a 90% passing rate and 3%/3mm criterion. A 9.5% decrease in the slope of gEUD plot for the CTV was observed for the 3.5% downward range shift. However, the change in slope did not result in a gEUD change greater than 1.1% for the CTV. The slopes of the gEUD plots for normal structures increased by 3.1% 3.9% 2.4% and 0.2% for the chiasm, brainstem, left optic nerve and right optic nerve respectively. The maximum deviation from the gEUD of the clinical plan for normal structures was: 64% in the chiasm, 31% for the brainstem, and 19% for both optic nerves.

Conclusion:

A retrospective review shows moderate radiobiological impact of range uncertainty in passively scattered proton therapy with sporadic catastrophe. The linear regression analysis on the statistical data indicates a systematic deviation of gEUD from treatment planning in the light of range uncertainty.
SU‐F‐T‐136: Breath Hold Lung Phantom Study in Using CT Density Versus Relative Stopping Power Ratio for Proton Pencil Beam Scanning System
Authors: Syh J , Wu H , Rosen L
Published: July 2016

Purpose:

To evaluate mass density effects of CT conversion table and its variation in current treatment planning system of spot scanning proton beam using an IROC proton lung phantom for this study.

Methods:

A proton lung phantom study was acquired to Imaging and Radiation Oncology Core Houston (IROC) Quality Assurance Center. Inside the lung phantom, GAF Chromic films and couples of thermal luminescent dosimeter (TLD) capsules embedded in specified PTV and adjacent structures to monitor delivered dosage and 3D dose distribution profiles. Various material such as cork (Lung), blue water (heart), Techron HPV (ribs) and organic material of balsa wood and cork as dosimetry inserts within phantom of solid water (soft tissue). Relative stopping power (RLSP) values were provided. Our treatment planning system (TPS) doesn't require SP instead relative density was converted relative to water. However lung phantom was irradiated by planning with density override and the results were compared with IROC measurements. The second attempt was conducted without density override and compared with IROC's.

Results:

The higher passing rate of imaging and measurement results of the lung phantom irradiation met the criteria by IROC without density override. The film at coronal plane was found to be shift due to inclined cylinder insertion. The converted CT density worked as expected to correlate relative stopping power.

Conclusion:

The proton lung phantom provided by IROC is a useful tool to qualify our commissioned proton pencil beam delivery with TPS within reliable confidence. The relative mass stopping power ratios of materials were converted from the relative physical density relative to water and the results were satisfied.
SU‐F‐T‐138: Commissioning and Evaluating Dose Computation Models for a Dedicated Proton Line Scanning Beam Nozzle in Eclipse Treatment Planning System
Authors: Tsai P , Huang H , Cai S , Chen H , Wu S , Wu T , Lee S , Yeh C , Wu T , Lee C
Published: July 2016

Purpose:

In this study, we present an effective method to derive low dose envelope of the proton in‐air spot fluence at beam positions other than the isocenter to reduce amount of measurements required for planning commission. Also, we demonstrate commissioning and validation results of this method to the Eclipse treatment planning system (version 13.0.29) for a Sumitomo dedicated proton line scanning beam nozzle.

Methods:

The in‐air spot profiles at five beam‐axis positions (±200, ±100 and 0 mm) were obtained in trigger mode using a MP3 Water tank (PTW‐Freiburg) and a pinpoint ionization chamber (model 31014, PTW‐Freiburg). Low dose envelope (below 1% of the center dose) of the spot profile at isocenter was obtained by repeated point measurements to minimize dosimetry uncertainty. The double Gaussian (DG) model was used to fit and obtain optimal σ1, σ2 and their corresponding weightings through our in‐house MATLAB (Mathworks) program. σ1, σ2 were assumed to expand linearly along the beam axis from a virtual source position calculated by back projecting fitted sigmas from the single Gaussian (SG) model. Absolute doses in water were validated using an Advanced Markus chamber at the depth of 2cm with Pristine Peak (BP) R90d ranging from 5–32 cm for 10×10 cm2 scanned fields. The field size factors were verified with square fields from 2 to 20 cm at 2cm and before BP depth.

Results:

The absolute dose outputs were found to be within ±3%. For field size factor, the agreement between calculated and measurement were within ±2% at 2cm and ±3% before BP, except for the field size below 2×2 cm2.

Conclusion:

The double Gaussian model was found to be sufficient for characterizing the Sumitomo dedicated proton line scanning nozzle. With our effective double Gaussian fitting method, we are able to save significant proton beam time with acceptable output accuracy.
SU‐F‐T‐139: Meeting the Challenges of Quality Control in the TOPAS Monte Carlo Simulation Toolkit for Proton Therapy
Authors: Hall D , Perl J , Schuemann J , Faddegon B , Paganetti H
Published: July 2016

Purpose:

Monte Carlo particle transport simulation (MC) codes have become important tools in proton therapy and biology, both for research and practice. TOPAS is an MC toolkit serving users worldwide (213 licensed users at 95 institutions in 21 countries). It provides unprecedented ease in 4D placement of geometry components, beam sources and scoring through its user‐friendly and reproducible parameter file interface. Quality control (QC) of stochastic simulation software is inherently difficult, and the versatility of TOPAS introduces additional challenges. But QC is vital as the TOPAS development team implements new features, addresses user feedback and reacts to upgrades of underlying software (i.e. Geant4).

Methods:

Whenever code is committed to our repository, over 50 separate module tests are automatically triggered via a continuous integration service. They check that the various module options execute successfully and that their results are statistically consistent with previous reference values. Prior to each software release, longer end‐to‐end tests automatically validate TOPAS against experimental data and a TOPAS benchmark. These include simulating multiple properties of spread‐out Bragg peaks, validating nuclear models, and searching for differences in patient simulations.

Results:

Continuous integration has proven effective in catching regressions at the time they are introduced, particularly when implementing new features that involve refactoring code (e.g. multithreading and ntuple output). Code coverage statistics highlight untested portions of code and guide development of new tests. The various end‐to‐end tests demonstrate that TOPAS accurately describes the physics of proton therapy within clinical tolerances.

Conclusion:

The TOPAS QC strategy of frequent short tests and pre‐release long tests has led to a more reliable tool. However, the versatility of TOPAS makes it difficult to predict how users might combine different modules, and so QC ultimately remains a partnership between the developer and the user.

This work was funded by National Cancer Institute grant R01 CA 140735.
SU‐F‐T‐140: Assessment of the Proton Boron Fusion Reaction for Practical Radiation Therapy Applications Using MCNP6
Authors: Adam D , Bednarz B
Published: July 2016

Purpose:

The proton boron fusion reaction is a reaction that describes the creation of three alpha particles as the result of the interaction of a proton incident upon a 11B target. Theoretically, the proton boron fusion reaction is a desirable reaction for radiation therapy applications in that, with the appropriate boron delivery agent, it could potentially combine the localized dose delivery protons exhibit (Bragg peak) and the local deposition of high LET alpha particles in cancerous sites. Previous efforts have shown significant dose enhancement using the proton boron fusion reaction; the overarching purpose of this work is an attempt to validate previous Monte Carlo results of the proton boron fusion reaction.

Methods:

The proton boron fusion reaction, 11B(p, 3α), is investigated using MCNP6 to assess the viability for potential use in radiation therapy. Simple simulations of a proton pencil beam incident upon both a water phantom and a water phantom with an axial region containing 100ppm boron were modeled using MCNP6 in order to determine the extent of the impact boron had upon the calculated energy deposition.

Results:

The maximum dose increase calculated was 0.026% for the incident 250 MeV proton beam scenario. The MCNP simulations performed demonstrated that the proton boron fusion reaction rate at clinically relevant boron concentrations was too small in order to have any measurable impact on the absorbed dose.

Conclusion:

For all MCNP6 simulations conducted, the increase of absorbed dose of a simple water phantom due to the 11B(p, 3α) reaction was found to be inconsequential. In addition, it was determined that there are no good evaluations of the 11B(p, 3α) reaction for use in MCNPX/6 and further work should be conducted in cross section evaluations in order to definitively evaluate the feasibility of the proton boron fusion reaction for use in radiation therapy applications.
SU‐F‐T‐144: Analytical Closed Form Approximation for Carbon Ion Bragg Curves in Water
Authors: Tuomanen S , Moskvin V , Farr J
Published: July 2016

Purpose:

Semi‐empirical modeling is a powerful computational method in radiation dosimetry. A set of approximations exist for proton ion depth dose distribution (DDD) in water. However, the modeling is more complicated for carbon ions due to fragmentation. This study addresses this by providing and evaluating a new methodology for DDD modeling of carbon ions in water.

Methods:

The FLUKA, Monte Carlo (MC) general‐purpose transport code was used for simulation of carbon DDDs for energies of 100–400 MeV in water as reference data model benchmarking. Based on Thomas Bortfeld's closed form equation approximating proton Bragg Curves as a basis, we derived the critical constants for a beam of Carbon ions by applying models of radiation transport by Lee et. al. and Geiger to our simulated Carbon curves. We hypothesized that including a new exponential (κ) residual distance parameter to Bortfeld's fluence reduction relation would improve DDD modeling for carbon ions. We are introducing an additional term to be added to Bortfeld's equation to describe fragmentation tail. This term accounts for the pre‐peak dose from nuclear fragments (NF). In the post peak region, the NF transport will be treated as new beams utilizing the Glauber model for interaction cross sections and the Abrasion‐ Ablation fragmentation model.

Results:

The carbon beam specific constants in the developed model were determined to be : p= 1.75, β=0.008 cm‐1, γ=0.6, α=0.0007 cm MeV, σmono=0.08, and the new exponential parameter κ=0.55. This produced a close match for the plateau part of the curve (max deviation 6.37%).

Conclusion:

The derived semi‐empirical model provides an accurate approximation of the MC simulated clinical carbon DDDs. This is the first direct semi‐empirical simulation for the dosimetry of therapeutic carbon ions. The accurate modeling of the NF tail in the carbon DDD will provide key insight into distal edge dose deposition formation.
SU‐F‐T‐145: Modeling of a Proton Pencil Beam Partially Blocked by a Collimator
Authors: Slopsema R , Matysiak W
Published: July 2016

Purpose:

To develop a mathematical model of the fluence distribution of a proton pencil beam that is partially blocked by a collimator.

Methods:

In one dimension, an exact analytical solution of the proton distribution behind a collimator edge was derived, taking the beam's phase space, the edge position, and the scattering downstream of the collimator as input. Extension to two dimensions was achieved using a Clarkson‐like

Method:

determine the radial distances from each calculation point to the collimator outline, calculate the beam's phase space in the direction of each radial line, apply the one‐dimensional model along each radial line, and sum the contributions. A Matlab implementation of the full model produced high‐resolution distributions with relatively short calculation times (of about 2 ms per calculation point). The model was validated by comparison to Monte Carlo simulations for select pencil beam phase space distributions and collimator shapes.

Results:

Agreement between model and simulation confirm the exact mathematical solution of the one‐dimensional model. In two dimensions the agreement is reasonable, but not exact, due to the approximations introduced in the Clarkson‐like method.

Conclusion:

A model has been developed and validated that gives the lateral fluence distribution of a pencil beam beyond a collimator. Notably, this model does not include free parameters and the final fluence is completely determined by the phase space parameters of the beam, already existing in the treatment planning system, as well as both axial and lateral position of the aperture edge. As such, it can be incorporated in a full pencil‐beam dose calculation algorithm allowing for the modeling of field‐specific collimators in proton pencil beam scanning and, potentially, the optimization of collimator shape and/or pencil phase space in addition to the customary spot weight optimization.
SU‐F‐T‐148: Are the Approximations in Analytic Semi‐Empirical Dose Calculation Algorithms for Intensity Modulated Proton Therapy for Complex Heterogeneities of Head and Neck Clinically Significant?
Authors: Yepes P , Titt U , Mirkovic D , Liu A , Frank S , Mohan R
Published: July 2016

Purpose:

Evaluate the differences in dose distributions between the proton analytic semi‐empirical dose calculation algorithm used in the clinic and Monte Carlo calculations for a sample of 50 head‐and‐neck (H&N) patients and estimate the potential clinical significance of the differences.

Methods:

A cohort of 50 H&N patients, treated at the University of Texas Cancer Center with Intensity Modulated Proton Therapy (IMPT), were selected for evaluation of clinical significance of approximations in computed dose distributions. H&N site was selected because of the highly inhomogeneous nature of the anatomy. The Fast Dose Calculator (FDC), a fast track‐repeating accelerated Monte Carlo algorithm for proton therapy, was utilized for the calculation of dose distributions delivered during treatment plans. Because of its short processing time, FDC allows for the processing of large cohorts of patients. FDC has been validated versus GEANT4, a full Monte Carlo system and measurements in water and for inhomogeneous phantoms. A gamma‐index analysis, DVHs, EUDs, and TCP and NTCPs computed using published models were utilized to evaluate the differences between the Treatment Plan System (TPS) and FDC.

Results:

The Monte Carlo results systematically predict lower dose delivered in the target. The observed differences can be as large as 8 Gy, and should have a clinical impact. Gamma analysis also showed significant differences between both approaches, especially for the target volumes.

Conclusion:

Monte Carlo calculations with fast algorithms is practical and should be considered for the clinic, at least as a treatment plan verification tool.