bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐F‐BRD‐05: Dosimetric Comparison of Protocol‐Based SBRT Lung Treatment Modalities: Statistically Significant VMAT Advantages Over Fixed‐ Beam IMRT
Authors: Best R , Harrell A , Geesey C , Libby B , Wijesooriya K 
Published: July 2014

Purpose:The purpose of this study is to inter‐compare and find statistically significant differences between flattened field fixed‐beam (FB) IMRT with flattening‐filter free (FFF) volumetric modulated arc therapy (VMAT) for stereotactic body radiation therapy SBRT.Methods:SBRT plans using FB IMRT and FFF VMAT were generated for fifteen SBRT lung patients using 6 MV beams. For each patient, both IMRT and VMAT plans were created for comparison. Plans were generated utilizing RTOG 0915 (peripheral, 10 patients) and RTOG 0813 (medial, 5 patients) lung protocols. Target dose, critical structure dose, and treatment time were compared and tested for statistical significance. Parameters of interest included prescription isodose surface coverage, target dose heterogeneity, high dose spillage (location and volume), low dose spillage (location and volume), lung dose spillage, and critical structure maximum‐ and volumetric‐dose limits.Results:For all criteria, we found equivalent or higher conformality with VMAT plans as well as reduced critical structure doses. Several differences passed a Student's t‐test of significance: VMAT reduced the high dose spillage, evaluated with conformality index (CI), by an average of 9.4%±15.1% (p=0.030) compared to IMRT. VMAT plans reduced the lung volume receiving 20 Gy by 16.2%±15.0% (p=0.016) compared with IMRT. For the RTOG 0915 peripheral lesions, the volumes of lung receiving 12.4 Gy and 11.6 Gy were reduced by 27.0%±13.8% and 27.5%±12.6% (for both, p<0.001) in VMAT plans. Of the 26 protocol pass/fail criteria, VMAT plans were able to achieve an average of 0.2±0.7 (p=0.026) more constraints than the IMRT plans.Conclusions:FFF VMAT has dosimetric advantages over fixed beam IMRT for lung SBRT. Significant advantages included increased dose conformity, and reduced organs‐at‐risk doses. The overall improvements in terms of protocol pass/fail criteria were more modest and will require more patient data to establish difference trends of more statistical significance.
SU‐F‐BRD‐06: Dosimetric Cost of a GTV Margin for Simultaneous Integrated Intra‐ Prostatic Boost Treatments
Authors: Studenski M , Abramowitz M , Dogan N , Pollack A 
Published: July 2014

Purpose:Quantify the dosimetric cost for a margin around the MRI‐defined high risk GTV for simultaneous integrated intra‐prostatic boosts (SIIB) treated with RapidArc.Methods:For external beam radiotherapy of the prostate, a 3‐7 mm PTV margin is typically used to account for setup and intra‐fraction uncertainties after adjusting for inter‐fraction motion. On the other hand, our current paradigm is to treat the MRI‐defined high risk GTV with no margin. In this work, 11 patients treated SIIB (7 post‐prostatectomy, 4 intact prostate) with RapidArc were re‐planned with 1‐5 mm margins around the GTV to quantify dosimetric effects. Two 358 degree, 10 MV RapidArcs were used to deliver 68 Gy (76.5 Gy boost) to the post‐prostatectomy patients and 80 Gy (86 Gy boost) to the intact prostates. Paired, two tail t‐tests were used to determine if there were any significant differences (p<0.05) in the total MUs and dosimetric parameters used to evaluate rectum, bladder, and PTV dose with and without margin.Results:The average GTV volume without margin was 8.1cc (2.8% of the PTV volume) while the average GTV volume with a 5 mm margin was 20.1cc (9.0% of the PTV volume). GTV volumes ranged from 0.2% of the PTV volume up to 33.0%. Despite these changes in volume, the only statistical difference was found for the rectal V65 Gy with a 5 mm margin (18.6% vs. 19.4%; p‐value = 0.026) when all patients were considered as a single group. No difference was found when analyzed as two groups. The rectum V40Gy, bladder V40Gy and V65Gy, PTV Dmax and D95% or the total MUs did not show any significant difference for any margin.Conclusion:A 4 mm margin on the high risk GTV is possible with no statistically significant change in dosimetry or total MUs. Further work will assess the appropriate margin required for intra‐prostatic boosts.
SU‐F‐BRD‐07: Empirical Derivation of Site‐Specific Margin Formulas
Authors: Conroy L , Quirk S , Smith W 
Published: July 2014

Purpose:To empirically derive margin formulas from existing clinical radiotherapy plans accounting for respiratory motion and setup uncertainties.Methods:We simulated realistic treatment scenarios, including respiratory motion and setup errors. Individual probability density functions (PDF) from respiratory data were used to simulate respiratory motion. Random (σ) and systematic (Σ) setup errors were modeled as Gaussian distributions. One‐dimensional dose profiles were extracted from existing radiotherapy plans and convolved with respiratory PDFs and random error distributions to produce blurred dose profiles. Each blurred dose profile was then shifted 1000 times by randomly sampling the simulated systematic error distribution. Margins were determined from the distance between the simulated treatment and the original 95% isodose level. An equation was fit for each (σ, Σ) combination to derive margin formulas for 90% of the population receiving 95% dose. This methodology can be applied to different tumor sites. Here, dose profiles were extracted from partial breast 3DCRT plans in the anterior‐posterior (AP) and superior‐inferior (SI) directions. Respiratory motion data was from healthy volunteers, and a clinically relevant range of random and systematic setup errors (standard deviations 1 – 4 mm) was determined from the literature.Results:The PBI margin formulas in the AP and SI directions for 95% dose coverage for 90% of the population were very similar: M= 0.68σ + 1.54Σ and M= 0.72σ + 1.50Σ, respectively. Systematic setup errors had the largest influence on required margin size, whereas realistic respiratory amplitude had minimal impact. The derived formulas resulted in a smaller systematic component than commonly‐used theoretical margin recipes.Conclusion:We have demonstrated a method to derive empirical margin formulas from existing patient radiotherapy plans, incorporating realistic respiratory motion and appropriate ranges of random and systematic error. Site‐specific margin formulas may be better suited to patient populations than theoretically derived, ideal geometry general margin formulas.
SU‐F‐BRD‐08: Guaranteed Epsilon‐Optimal Treatment Plans with Minimum Number of Beams for SBRT Using RayStation
Authors: Yarmand H , Winey B , Craft D 
Published: July 2014

Purpose:To efficiently find quality‐guaranteed treatment plans with the minimum number of beams for stereotactic body radiation therapy using RayStation.Methods:For a pre‐specified pool of candidate beams we use RayStation (a treatment planning software for clinical use) to identify the deliverable plan which uses all the beams with the minimum dose to organs at risk (OARs) and dose to the tumor and other structures in specified ranges. Then use the dose matrix information for the generated apertures from RayStation to solve a linear program to find the ideal plan with the same objective and constraints allowing use of all beams. Finally we solve a mixed integer programming formulation of the beam angle optimization problem (BAO) with the objective of minimizing the number of beams while remaining in a predetermined epsilon‐optimality of the ideal plan with respect to the dose to OARs. Since the treatment plan optimization is a multicriteria optimization problem, the planner can exploit the multicriteria optimization capability of RayStation to navigate the ideal dose distribution Pareto surface and select a plan of desired target coverage versus OARs sparing, and then use the proposed technique to reduce the number of beams while guaranteeing quality. For the numerical experiments two liver cases and one lung case with 33 non‐coplanar beams are considered.Results:The ideal plan uses an impractically large number of beams. The proposed technique reduces the number of beams to the range of practical application (5 to 9 beams) while remaining in the epsilon‐optimal range of 1% to 5% optimality gap.Conclusion:The proposed method can be integrated into a general algorithm for fast navigation of the ideal dose distribution Pareto surface and finding the treatment plan with the minimum number of beams, which corresponds to the delivery time, in epsilon‐optimality range of the desired ideal plan.The project was supported by the Federal Share of program income earned by Massachusetts General Hospital on C06 CA059267, Proton Therapy Research and Treatment Center and partially by RaySearch Laboratories.
SU‐F‐BRD‐09: Is It Sufficient to Use Only Low Density Tissue‐Margin to Compensate Inter‐Fractionation Setup Uncertainties in Lung Treatment?
Authors: Nie K , Qin S , Yue N , Chen T , Millevoi R , Guo J 
Published: July 2014

Purpose:In lung radiation treatment, PTV is formed with a margin around GTV (or CTV/ITV). Although GTV is most likely of water equivalent density, the PTV margin may be formed with the surrounding low‐density tissues, which may lead to unreal dosimetric plan. This study is to evaluate whether the concern of dose calculation inside the PTV with only low density margin could be justified in lung treatment.Methods:Three SBRT cases were analyzed. The PTV from the original plan (Plan‐O) was created with a 5–10 mm margin outside the ITV to incorporate setup errors and all mobility from 10 respiratory phases. Test plans were generated with the GTV shifted to the PTV edge to simulate the extreme situations with maximum setup uncertainties. Two representative positions as the very posterior‐superior (Plan‐PS) and anterior‐inferior (Plan‐AI) edge were considered. The virtual GTV was assigned a density of 1.0 g.cm−3 and surrounding lung, including the PTV margin, was defined as 0.25 g.cm−3. Also, additional plan with a 1mm tissue‐margin instead of full lung‐margin was created to evaluate whether a composite‐margin (Plan‐Comp) has a better approximation for dose calculation. All plans were generated on the average CT using Analytical Anisotropic Algorithm with heterogeneity correction on and all planning parameters/monitor unites remained unchanged. DVH analyses were performed for comparisons.Results:Despite the non‐static dose distribution, the high‐dose region synchronized with tumor positions. This might due to scatter conditions as greater doses were absorbed in the solid‐tumor than in the surrounding low‐density lungtissue. However, it still showed missing target coverage in general. Certain level of composite‐margin might give better approximation for the dosecalculation.Conclusion:Our exploratory results suggest that with the lungmargin only, the planning dose of PTV might overestimate the coverage of the target during treatment. The significance of this overestimation might warrant further investigation.
SU‐F‐BRD‐10: Lung IMRT Planning Using Standardized Beam Bouquet Templates
Authors: Yuan L , Wu Q J. , Yin F , Ge Y 
Published: July 2014

Purpose:We investigate the feasibility of choosing from a small set of standardized templates of beam bouquets (i.e., entire beam configuration settings) for lung IMRT planning to improve planning efficiency and quality consistency, and also to facilitate automated planning.Methods:A set of beam bouquet templates is determined by learning from the beam angle settings in 60 clinical lung IMRT plans. A k‐medoids cluster analysis method is used to classify the beam angle configuration into clusters. The value of the average silhouette width is used to determine the ideal number of clusters. The beam arrangements in each medoid of the resulting clusters are taken as the standardized beam bouquet for the cluster, with the corresponding case taken as the reference case. The resulting set of beam bouquet templates was used to re‐plan 20 cases randomly selected from the database and the dosimetric quality of the plans was evaluated against the corresponding clinical plans by a paired t‐test. The template for each test case was manually selected by a planner based on the match between the test and reference cases.Results:The dosimetric parameters (mean±S.D. in percentage of prescription dose) of the plans using 6 beam bouquet templates and those of the clinical plans, respectively, and the p‐values (in parenthesis) are: lung Dmean: 18.8±7.0, 19.2±7.0 (0.28), esophagus Dmean: 32.0±16.3, 34.4±17.9 (0.01), heart Dmean: 19.2±16.5, 19.4±16.6 (0.74), spinal cord D2%: 47.7±18.8, 52.0±20.3 (0.01), PTV dose homogeneity (D2%‐D99%): 17.1±15.4, 20.7±12.2 (0.03). The esophagus Dmean, cord D02 and PTV dose homogeneity are statistically better in the plans using the standardized templates, but the improvements (<5%) may not be clinically significant. The other dosimetric parameters are not statistically different.Conclusion:It's feasible to use a small number of standardized beam bouquet templates (e.g. 6) to generate plans with quality comparable to that of clinical plans.Partially supported by NIH/NCI under grant #R21CA161389 and a master research grant by Varian Medical System.
SU‐F‐BRD‐11: Prediction of Dosimetric Endpoints From Patient Geometry Using Neural Nets
Authors: O' Connell D , Chow P , Agazaryan N , Jani S , Low D , Lamb J 
Published: July 2014

Purpose:The previously‐published overlap volume histogram (OVH) technique lends itself naturally to prediction of the dose received by a given volume of tissue (e.g. D90) in intensity‐modulated radiotherapy (IMRT) treatment plans. Here we extend the OVH technique using artificial neural networks in order to predict the volume of tissue receiving a given dose (e.g. V90) in both prostate IMRT and conventional breast radiotherapy.Methods:Twenty‐nine prostate treatment plans and forty‐three breast treatment plans were analyzed. The spatial relationships between the prostate and rectum and between the breast and ipsilateral lung were characterized using OVHs. The OVH is a cumulative histogram representing the fractional volume of the risk organ overlapped by a series of isotropic expansions of the planning target volume (PTV). Seven cases were identified as outliers and replanned. OVH points were used as inputs to a one hidden layer feed forward artificial neural network with quality parameters of the corresponding plan, such as the rectum V50, as targets. A 3‐fold cross‐validation was used to estimate the prediction error.Results:The root mean square (RMS) error between the predicted rectum V50s and the planned values was 2.3, which was 35% of the standard deviation of V50 for the twenty‐nine plans. The RMS error of prediction of V20 of the ipsilateral lung in breast cases was 3.9, which was 90% of the standard deviation of the V20 values in the breast plan database.Conclusion:This study demonstrates that artificial neural nets can be used to extend the OVH technique to predict dosimetric endpoints taking the form of a volume receiving a given dose, rather than the minimum dose received by a given volume. Prediction of ipsilateral lung dose in breast radiotherapy using the OVH technique remains a work in progress.
SU‐F‐BRD‐12: Prostate Cancer Dose Escalation Including Lymph Nodes: A Planning Study
Authors: Wu Q , Song H , Salama J 
Published: July 2014

Purpose:Dose escalation improves local control for prostate cancer but not overall survival (OS), only systemic therapy such as ADT can improve OS. One explanation is that occult diseases in the lymph nodes (LNs) are either not treated, or treated at inadequate level. This study explores dose escalation using IMRT with reduced margins.Methods:14 previously treated patients were selected. The CTV1 includes prostate and seminal vesicles, and CTV2 includes pelvic LNs. OARs include rectum, bladder, small bowel, femoral heads, and penile bulb. Reference plan (RP) was designed to follow RTOG‐0521, with 10 mm margin for PTVs. In phase I, both PTVs are prescribed to 1.8 Gy/fx for 26 fractions. In phase II, PTV1 is boosted for another 16 fractions. In dose escalation plans (DEPs), margins from 0 to 10 mm are added to form PTVs, which are prescribed to 1.8 Gy/fx for 42 fractions. 95% of PTVs are covered by prescription.Results:The CTV1/CTV2 has volume of 63±20 and 415±132 cc. To keep bladder Dmean same as in RP, margin must be ≤6 mm in DEPs. Similarly, margin needs to be ≤7 mm for rectum Dmean. For small bowel, even at margin of 0, Dmean and EUDs on DEPs are higher than RP, because RP was also IMRT. Instead, QUANTEC criteria of “45Gy volume<195cc” was adopted. 6/14 patients fail this criteria in RP. For the remaining 8 patients, margins of ≤7(3) mm are necessary for 6(2) patients, respectively.Conclusion:To escalate LNs to full dose, planning margins need to be ≤6 mm to achieve same tolerance for rectum and bladder. A large variation in patient anatomy exists for small bowel and dose escalation was possible for half of patients with ≤6 mm margin. Investigations of whether and how ≤6 mm margin can be achieved through image guidance are needed.
SU‐F‐BRD‐13: Quantum Annealing Applied to IMRT Beamlet Intensity Optimization
Authors: Nazareth D , Spaans J 
Published: July 2014

Purpose:We report on the first application of quantum annealing (QA) to the process of beamlet intensity optimization for IMRT. QA is a new technology, which employs novel hardware and software techniques to address various discrete optimization problems in many fields.Methods:We apply the D‐Wave Inc. proprietary hardware, which natively exploits quantum mechanical effects for improved optimization. The new QA algorithm, running on this hardware, is most similar to simulated annealing, but relies on natural processes to directly minimize the free energy of a system. A simple quantum system is slowly evolved into a classical system, representing the objective function. To apply QA to IMRT‐type optimization, two prostate cases were considered. A reduced number of beamlets were employed, due to the current QA hardware limitation of ∼500 binary variables. The beamlet dose matrices were computed using CERR, and an objective function was defined based on typical clinical constraints, including dose‐volume objectives. The objective function was discretized, and the QA method was compared to two standard optimization Methods: simulated annealing and Tabu search, run on a conventional computing cluster.Results:Based on several runs, the average final objective function value achieved by the QA was 16.9 for the first patient, compared with 10.0 for Tabu and 6.7 for the SA. For the second patient, the values were 70.7 for the QA, 120.0 for Tabu, and 22.9 for the SA. The QA algorithm required 27–38% of the time required by the other two methods.Conclusion:In terms of objective function value, the QA performance was similar to Tabu but less effective than the SA. However, its speed was 3–4 times faster than the other two methods. This initial experiment suggests that QA‐based heuristics may offer significant speedup over conventional clinical optimization methods, as quantum annealing hardware scales to larger sizes.
SU‐F‐BRD‐14: The Effect of Radiation‐Induced Esophageal Swelling On Dose‐Volume Histograms
Authors: Niedzielski J , Yang J , Martel M , Tucker S , Briere T , Gomez D 
Published: July 2014

Purpose:Acute esophagitis results in esophageal swelling. Here we evaluate the effect of this response on DVH metrics calculated throughout the course of radiation therapy.Methods:Twenty‐nine NSCLC patients were identified who received weekly CT imaging, and varying esophagitis grades (11 grade0 patients, 12 grade2 patients, 6 grade3 patients), using CTCAE scoring criteria. Deformable image registration was used to map the planning esophagus contour to the weekly CT images. Treatment plans were recalculated on each weekly 4DCT and DVH metrics for the esophagus were compared to the delivered treatment plan. DVH metrics were also extracted for esophagus planning‐at‐risk volumes (PRV) with 1–3mm uniform expansions.Results:The esophagus V50 increased as the treatment progressed by 2.3±0.7cc and 9.0±1.1cc, for the grade 0 and grade 2/3 patients, respectively. The mean esophageal dose (MED) increased by 3.5±0.9Gy, and 8.0±1.1Gy for the grade 0 and grade 2/3 patients, respectively. In some cases where the planned V50 was similar, it remained the same at the end of treatment for grade 0 cases, but increased for higher grade cases. These apparent changes in delivered dose, as expressed by the DVH, are mostly attributed to volume changes in the regions of esophagitis. In addition, portions of the esophagus of some patients moved into highdose regions. The 2mm PRV was able to account for these differences in all but 1 of the 18 G2/3 patients. The 1mm PRV produced the closest DVH metrics calculated from the average weekly plans compared to the true treatment plan.Conclusion:Esophagus radiation response affects DVH parameters throughout treatment, especially patients with high toxicity. This effect must be considered when comparing DVHs calculated using daily IGRT CT images with those from the original planning CT (e.g. for adaptive planning). Adding a margin to the esophagus can account for variation in DVH metrics.
SU‐F‐BRD‐15: The Impact of Dose Calculation Algorithm and Hounsfield Units Conversion Tables On Plan Dosimetry for Lung SBRT
Authors: Kuo L , Yorke E , Lim S , Mechalakos J , Rimner A 
Published: July 2014

Purpose:To assess dosimetric differences in IMRT lung stereotactic body radiotherapy (SBRT) plans calculated with Varian AAA and Acuros (AXB) and with vendor‐supplied (V) versus in‐house (IH) measured Hounsfield units (HU) to mass and HU to electron density conversion tables.Methods:In‐house conversion tables were measured using Gammex 472 density‐plug phantom. IMRT plans (6 MV, Varian TrueBeam, 6–9 coplanar fields) meeting departmental coverage and normal tissue constraints were retrospectively generated for 10 lung SBRT cases using Eclipse Vn 10.0.28 AAA with in‐house tables (AAA/IH). Using these monitor units and MLC sequences, plans were recalculated with AAA and vendor tables (AAA/V) and with AXB with both tables (AXB/IH and AXB/V). Ratios to corresponding AAA/IH values were calculated for PTV D95, D01, D99, mean‐dose, total and ipsilateral lung V20 and chestwall V30. Statistical significance of differences was judged by Wilcoxon Signed Rank Test (p<0.05).Results:For HU<−400 the vendor HU‐mass density table was notably below the IH table. PTV D95 ratios to AAA/IH, averaged over all patients, are 0.963±0.073 (p=0.508), 0.914±0.126 (p=0.011), and 0.998±0.001 (p=0.005) for AXB/IH, AXB/V and AAA/V respectively. Total lung V20 ratios are 1.006±0.046 (p=0.386), 0.975±0.080 (p=0.514) and 0.998±0.002 (p=0.007); ipsilateral lung V20 ratios are 1.008±0.041(p=0.284), 0.977±0.076 (p=0.443), and 0.998±0.018 (p=0.005) for AXB/IH, AXB/V and AAA/V respectively. In 7 cases, ratios to AAA/IH were within ± 5% for all indices studied. For 3 cases characterized by very low lung density and small PTV (19.99±8.09 c.c.), PTV D95 ratio for AXB/V ranged from 67.4% to 85.9%, AXB/IH D95 ratio ranged from 81.6% to 93.4%; there were large differences in other studied indices.Conclusion:For AXB users, careful attention to HU conversion tables is important, as they can significantly impact AXB (but not AAA) lung SBRT plans. Algorithm selection is also important for small targets in low density lung.