bluuebunny/crossref_metadata_2025_split huggingface dataset:
WE‐AB‐209‐08: Novel Beam‐Specific Adaptive Margins for Reducing Organ‐At‐Risk Doses
Authors: Tsang H , Kamerling CP , Ziegenhein P , Nill S , Oelfke U 
Published: July 2016

Purpose:Current practice of using 3D margins in radiotherapy with high‐energy photon beams provides larger‐than‐required target coverage. According to the photon depth‐dose curve, target displacements in beam direction result in minute changes in dose delivered. We exploit this behavior by generating margins on a per‐beam basis which simultaneously account for the relative distance of the target and adjacent organs‐at‐risk (OARs).Methods:For each beam, we consider only geometrical uncertainties of the target location perpendicular to beam direction. By weighting voxels based on its proximity to an OAR, we generate adaptive margins that yield similar overall target coverage probability and reduced OAR dose‐burden, at the expense of increased target volume. Three IMRT plans, using 3D margins and 2D per‐beam margins with and without adaptation, were generated for five prostate patients with a prescription dose Dpres of 78Gy in 2Gy fractions using identical optimisation constraints. Systematic uncertainties of 1.1, 1.1, 1.5mm in the LR, SI, and AP directions, respectively, and 0.9, 1.1, 1.0mm for the random uncertainties, were assumed. A verification tool was employed to simulate the effects of systematic and random errors using a population size of 50,000. The fraction of the population that satisfies or violates a given DVH constraint was used for comparison.Results:We observe similar target coverage across all plans, with at least 97.5% of the population meeting the D98%>95%Dpres constraint. When looking at the probability of the population receiving D5<70Gy for the rectum, we observed median absolute increases of 23.61% (range, 2.15%–27.85%) and 6.97% (range, 0.65%–17.76%) using per‐beam margins with and without adaptation, respectively, relative to using 3D margins.Conclusion:We observed sufficient and similar target coverage using per‐beam margins. By adapting each per‐beam margin away from an OAR, we can further reduce OAR dose without significantly lowering target coverage probability by irradiating more less‐important tissues.This work is supported by Cancer Research UK under Programme C33589/A19908. Research at ICR is also supported by Cancer Research UK under Programme C33589/A19727 and NHS funding to the NIHR Biomedical Research Centre at RMH and ICR.
WE‐AB‐207B‐01: Dose Tolerance for SBRT/SABR
Authors: Grimm J 
Published: July 2016

Purpose:Stereotactic body radiation therapy (SBRT) / stereotactic ablative body radiotherapy (SABR) is gaining popularity, but quantitative dose tolerance has still been lacking. To improve this, the April 2016 issue of Seminars in Radiation Oncology will have normal tissue complication probability (NTCP) models for 10 critical structures: optic pathway, cochlea, oral mucosa, esophagus, chestwall, aorta, bronchi, duodenum, small bowel, and spinal cord.Methods:The project included more than 1500 treatments in 1–5 fractions using CyberKnife, Gamma Knife, or LINAC, with 60 authors from 15 institutions. NTCP models were constructed from the 97 grade 2–3 complications, predominantly scored using the common terminology criteria for adverse events (CTCAEv4). Dose volume histogram (DVH) data from each institutional dataset was loaded into the DVH Evaluator software (DiversiLabs, LLC, Huntingdon Valley, Pa) for modeling. The current state of the literature for the critical structures was depicted using DVH Risk Maps: comparative graphs of dose tolerance limits that can include estimated risk levels, reported complications, DVH data for study patients, as well as high‐ and low‐risk dose tolerance limits.Results:For relatively acceptable toxicity like grade 1–3 rib fractures and chestwall pain, the high‐risk limits have 50% risk and the low‐risk limits have 5% risk. Emami et al (IJROBP 1991 May 15;21(1):109–22) used 50% and 5% risk levels for all structures, whereas this effort used clinically acceptable ranges for each: in structures like aorta or spinal cord where complications must be avoided, the high‐ and low‐risk limits have about 3% and 1% risk, respectively, in this issue of Seminars. These statistically based guidelines can help ensure plan quality for each patient.Conclusion:NTCP for SBRT is now becoming available. Hypofractionated dose tolerance can be dramatically different than extrapolations of conventional fractionation so NTCP analysis of the SBRT/SBRT data is important to ensure safe clinical practice.Dr. Grimm, designed and holds intellectual property rights to the DVH Evaluator software tool which is an FDA‐cleared product in commercial use, and was used to analyze the data.
WE‐AB‐207B‐03: A Computational Methodology for Determination of CTV‐To‐PTV Margins with Inter Fractional Shape Variations Based On a Statistical Point Distribution Model for Prostate Cancer Radiation Therapy
Authors: Shibayama Y , Arimura H , Hirose T , Nakamura K , Sasaki T , Ohga S , Umezu Y , Nakamura Y , Honda H 
Published: July 2016

Purpose:Our assumption was that interfractional shape variations of target volumes could not be negligible for determination of clinical target volume (CTV)‐to‐planning target volume (PTV) margins. The aim of this study was to investigate this assumption as a simulation study by developing a computational framework of CTV‐to‐PTV margins with taking the interfractional shape variations into account based on point distribution model (PDM)Methods:The systematic and random errors for interfractional shape variations and translations of target volumes were evaluated for four types of CTV regions (only a prostate, a prostate plus proximal 1‐cm seminal vesicles, a prostate plus proximal 2‐cm seminal vesicles, and a prostate plus whole seminal vesicles). The CTV regions were delineated depending on prostate cancer risk groups on planning computed tomography (CT) and cone beam CT (CBCT) images of 73 fractions of 10 patients. The random and systematic errors for shape variations of CTV regions were derived from PDMs of CTV surfaces for all fractions of each patient. Systematic errors of shape variations of CTV regions were derived by comparing PDMs between planning CTV surfaces and average CTV surfaces. Finally, anisotropic CTV‐to‐PTV margins with shape variations in 6 directions (anterior, posterior, superior, inferior, right, and left) were computed by using a van Herk margin formula.Results:Differences between CTV‐to‐PTV margins with and without shape variations ranged from 0.7 to 1.7 mm in anterior direction, 1.0 to 2.8 mm in posterior direction, 0.8 to 2.8 mm in superior direction, 0.6 to 1.6 mm in inferior direction, 1.4 to 4.4 mm in right direction, and 1.3 to 5.2 mm in left direction.Conclusion:More than 1.0 mm additional margins were needed at least in 3 directions to guarantee CTV coverage due to shape variations. Therefore, shape variations should be taken into account for the determination of CTV‐to‐PTV margins.
WE‐AB‐207B‐06: Dose and Biological Uncertainties in Sarcoma
Authors: Marteinsdottir M , Schuemann J , Paganetti H 
Published: July 2016

Purpose:To understand the clinical impact of key uncertainties in proton therapy potentially affecting the analysis of clinical trials, namely the assumption of using a constant relative biological effectiveness (RBE) of 1.1 compared to variable RBE for proton therapy and the use of analytical dose calculation (ADC) methods.Methods:Proton dose distributions were compared for analytical and Monte Carlo (TOPAS) dose calculations. In addition, differences between using a constant RBE of 1.1 (RBE‐constant) were compared with four different RBE models (to assess model variations). 10 patients were selected from an ongoing clinical trial on IMRT versus scanned protons for sarcoma. Comparisons were performed using dosimetric indices based on dose‐volume histogram analyses and γ‐index analyses.Results:For three of the RBE‐models the mean dose, D95, D50 and D02 (dose values covering 95%, 50% and 2% of the target volume, respectively) were up to 5% lower than for RBE‐constant. The dosimetric indices for one of the RBE‐models were around 9% lower than for the RBE‐constant model. The differences for V90 (the percentage of the target volume covered by 90% of the prescription dose) were up to 40% for three RBE‐models, whereas for one the difference was around 95%. All ADC dosimetric indices were up to 5% larger than for RBE‐constant. The γ‐index passing rate for the target volume with a 3%/3mm criterion was above 97% for all models except for one, which was below 24%.Conclusion:Interpretation of clinical trials on sarcoma may depend on dose calculation uncertainties (as assessed by Monte Carlo). In addition, the biological dose distribution depends notably on which RBE model is utilized. The current practice of using a constant RBE of 1.1 may overestimate the target dose by as much as 5% for biological dose calculations. Performing an RBE uncertainty analysis is recommended for trial analysis.U19 projects ‐ U19 CA 021239. PI: Delaney
WE‐AB‐207B‐07: Dose Cloud: Generating “Big Data” for Radiation Therapy Treatment Plan Optimization Research
Authors: Folkerts MM , Long T , Radke RJ , Tian Z , Jia X , Chen M , Lu W , Jiang SB 
Published: July 2016

Purpose:To provide a tool to generate large sets of realistic virtual patient geometries and beamlet doses for treatment optimization research. This tool enables countless studies exploring the fundamental interplay between patient geometry, objective functions, weight selections, and achievable dose distributions for various algorithms and modalities.Methods:Generating realistic virtual patient geometries requires a small set of real patient data. We developed a normalized patient shape model (PSM) which captures organ and target contours in a correspondence‐preserving manner. Using PSM‐processed data, we perform principal component analysis (PCA) to extract major modes of variation from the population. These PCA modes can be shared without exposing patient information. The modes are re‐combined with different weights to produce sets of realistic virtual patient contours. Because virtual patients lack imaging information, we developed a shape‐based dose calculation (SBD) relying on the assumption that the region inside the body contour is water. SBD utilizes a 2D fluence‐convolved scatter kernel, derived from Monte Carlo simulations, and can compute both full dose for a given set of fluence maps, or produce a dose matrix (dose per fluence pixel) for many modalities. Combining the shape model with SBD provides the data needed for treatment plan optimization research.Results:We used PSM to capture organ and target contours for 96 prostate cases, extracted the first 20 PCA modes, and generated 2048 virtual patient shapes by randomly sampling mode scores. Nearly half of the shapes were thrown out for failing anatomical checks, the remaining 1124 were used in computing dose matrices via SBD and a standard 7‐beam protocol. As a proof of concept, and to generate data for later study, we performed fluence map optimization emphasizing PTV coverage.Conclusions:We successfully developed and tested a tool for creating customizable sets of virtual patients suitable for large‐scale radiation therapy optimization research.
WE‐AB‐207B‐09: Margin Reduction for Planning Target Volume (PTV) in Patients with Localized Prostate Cancer: Impact On Delivered Dose and Quality of Life
Authors: Kumarasiri A , Liu C , Brown S , Glide‐Hurst C , Elshaikh M , Chetty I , Movsas B 
Published: July 2016

Purpose:To estimate the delivered (cumulative) dose to targets and organs at risk for localized prostate cancer patients treated with reduced PTV margins and to evaluate preliminary patient reported quality‐of‐life (QOL).Methods:Under an IRB‐approved protocol, 20 prostate cancer patients (including 11 control patients) were treated with reduced planning margins (5 mm uniform with 4 mm at prostate/rectum interface). Control patients had standard margin (10/6 mm)‐based treatments. A parameter‐optimized Elastix algorithm along with energy‐mass mapping was used to deform and resample dose of the day onto the planning CT for each fraction to estimate the delivered dose over all fractions. QOL data were collected via Expanded Prostate cancer Index Composite (EPIC‐26) questionnaires at time points pre‐treatment, post‐treatment, and at 2, 6, 12, 18 month follow‐ups. Standardized QOL scores [range: 0–100] were determined and baseline‐corrected by subtracting pre‐treatment QOL data. Mean QOL differences between the margin reduced group and control group (QOLmr‐QOLcontrol) were calculated for first 18 months.Results:The difference between the cumulative mean dose (Dmean) and the planned mean dose (±SD) for PTV, prostate, bladder, and rectum were −2.2±1.0, 0.3±0.5, −0.7±2.6, and −2.1±1.3 Gy respectively for the margin‐reduced group, and −0.8±2.0, 0.9±1.4, ‐ 0.7±3.1 and −1.0±2.4 Gy for the control group. Difference between the two groups was statistically insignificant (p=0.1). Standardized and baseline corrected QOLmr‐QOLcontrol for EPIC domains categorized as “Urinary Incontinence”, “Urinary Irritative/Obstructive”, “Bowel”, “Sexual”, and “Hormonal” were 0.6, 12.1, 9.1, 13.3, and −0.9 for the 18 months following radiation therapy (higher values better). Delivered dose to rectum showed a weak correlation to “Bowel” domain (Pearson's coefficient −0.24, p<0.001), while bladder dose did not correlate to Urinary Incontinence/Irritative/Obstructive QOL domains.Conclusion:The margin‐reduced group exhibited clinically meaningful improvement of QOL without compromising the PTV dose. A larger number of patients and greater follow‐up is needed to draw unequivocal conclusions.This work was supported in part by a research grant from Varian Medical Systems, Palo Alto, CA
WE‐AB‐207B‐11: Optimizing Tumor Control Probability in Radiation Therapy Treatment ‐ Application to HDR Cervical Cancer
Authors: Lee E , Yuan F , Templeton A , Yao R , Chu J 
Published: July 2016

Purpose:The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor‐control‐probability(TCP) with an acceptable normal‐tissue‐complication probability(NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. We design treatment plans that optimize TCP directly and contrast them with the clinical dose‐based plans. PET image is incorporated to evaluate gain in TCP for dose escalation.Methods:We build a nonlinear mixed integer programming optimization model that maximizes TCP directly while satisfying the dose requirements on the targeted organ and healthy tissues. The solution strategy first fits the TCP function with a piecewise‐linear approximation, then solves the problem that maximizes the piecewise linear approximation of TCP, and finally performs a local neighborhood search to improve the TCP value. To gauge the feasibility, characteristics, and potential benefit of PET‐image guided dose escalation, initial validation consists of fifteen cervical cancer HDR patient cases. These patients have all received prior 45Gy of external radiation dose. For both escalated strategies, we consider 35Gy PTV‐dose, and two variations (37Gy‐boost to BTV vs 40Gy‐boost) to PET‐image‐pockets.Results:TCP for standard clinical plans range from 59.4% ‐ 63.6%. TCP for dose‐based PET‐guided escalated‐dose‐plan ranges from 63.8%–98.6% for all patients; whereas TCP‐optimized plans achieves over 91% for all patients. There is marginal difference in TCP among those with 37Gy‐boosted vs 40Gy‐boosted. There is no increase in rectum and bladder dose among all plans.Conclusion:Optimizing TCP directly results in highly conformed treatment plans. The TCP‐optimized plan is individualized based on the biological PET‐image of the patients. The TCP‐optimization framework is generalizable and has been applied successfully to other external‐beam delivery modalities. A clinical trial is on‐going to gauge the clinical significance.Partially supported by the National Science Foundation.
WE‐B‐201‐00: Statistical Failings That Keep Us All in the Dark
Authors: Schlesinger David 
Published: July 2016

Evidence is growing to suggest that many published clinical results cannot be replicated. At the same time, the number of published clinical papers is steadily increasing and most if not all base their conclusions on evidence provided by formal statistical tests. Medical physicists have a critical need to understand and be able to interpret the methods and results of these studies in order to judge their scientific quality and relevance. However many medical physicists have minimal or no training in the sort of practical statistical methods commonly found in the literature. They may therefore have an inadequate ability to detect statistical errors and limitations that are an unfortunately too frequent occurrence in clinical papers.In this session we will use published examples to demonstrate common statistical errors frequently encountered in peer‐reviewed literature, distinctive symptoms that can help detect these errors, and explanations for how they might have been corrected. In the process, we will explain some of the basic concepts of inferential statistics. Some specific case studies we will cover will include misunderstanding the meaning of p‐values and clinical significance, misinterpreting statistical power, not accounting for multiple‐hypothesis testing, ignoring missing data, and faulty survival analysis.

Learning Objectives:
Learn about the presence of statistical problems in published studies
Identify common signs and symptoms of potential problems in various types of statistical tests
Learn methods for correctly implementing statistical analyses of the type commonly found in clinical publications
WE‐D‐204‐00: Session in Memory of Franca Kuchnir: Excellence in Medical Physics Residency Education
Authors: Hendrickson Kristi 
Published: July 2016

Speakers in this session will present overview and details of a specific rotation or feature of their Medical Physics Residency Program that is particularly exceptional and noteworthy. The featured rotations include foundational topics executed with exceptional acumen and innovative educational rotations perhaps not commonly found in Medical Physics Residency Programs. A site‐specific clinical rotation will be described, where the medical physics resident follows the physician and medical resident for two weeks into patient consultations, simulation sessions, target contouring sessions, planning meetings with dosimetry, patient follow up visits, and tumor boards, to gain insight into the thought processes of the radiation oncologist. An incident learning rotation will be described where the residents learns about and practices evaluating clinical errors and investigates process improvements for the clinic. The residency environment at a Canadian medical physics residency program will be described, where the training and interactions with radiation oncology residents is integrated. And the first month rotation will be described, where the medical physics resident rotates through the clinical areas including simulation, dosimetry, and treatment units, gaining an overview of the clinical flow and meeting all the clinical staff to begin the residency program. This session will be of particular interest to residency programs who are interested in adopting or adapting these curricular ideas into their programs and to residency candidates who want to learn about programs already employing innovative practices.

Learning Objectives:
To learn about exceptional and innovative clinical rotations or program features within existing Medical Physics Residency Programs.
To understand how to adopt/adapt innovative curricular designs into your own Medical Physics Residency Program, if appropriate.
WE‐D‐204‐02: Errors and Process Improvements in Radiation Therapy
Authors: Fontenla D. 
Published: July 2016

Speakers in this session will present overview and details of a specific rotation or feature of their Medical Physics Residency Program that is particularly exceptional and noteworthy. The featured rotations include foundational topics executed with exceptional acumen and innovative educational rotations perhaps not commonly found in Medical Physics Residency Programs. A site‐specific clinical rotation will be described, where the medical physics resident follows the physician and medical resident for two weeks into patient consultations, simulation sessions, target contouring sessions, planning meetings with dosimetry, patient follow up visits, and tumor boards, to gain insight into the thought processes of the radiation oncologist. An incident learning rotation will be described where the residents learns about and practices evaluating clinical errors and investigates process improvements for the clinic. The residency environment at a Canadian medical physics residency program will be described, where the training and interactions with radiation oncology residents is integrated. And the first month rotation will be described, where the medical physics resident rotates through the clinical areas including simulation, dosimetry, and treatment units, gaining an overview of the clinical flow and meeting all the clinical staff to begin the residency program. This session will be of particular interest to residency programs who are interested in adopting or adapting these curricular ideas into their programs and to residency candidates who want to learn about programs already employing innovative practices.

Learning Objectives:
To learn about exceptional and innovative clinical rotations or program features within existing Medical Physics Residency Programs.
To understand how to adopt/adapt innovative curricular designs into your own Medical Physics Residency Program, if appropriate.
WE‐DE‐201‐02: A Statistical Analysis Tool for Plan Quality Verification in HDR Brachytherapy Forward Planning for Cervix Cancer
Authors: Ma R , Zhu X , Li S , Zheng D , Lei Y , Wang S , Verma V , Bennion N , Wahl A , Zhou S 
Published: July 2016

Purpose:High Dose Rate (HDR) brachytherapy forward planning is principally an iterative process; hence, plan quality is affected by planners’ experiences and limited planning time. Thus, this may lead to sporadic errors and inconsistencies in planning. A statistical tool based on previous approved clinical treatment plans would help to maintain the consistency of planning quality and improve the efficiency of second checking.Methods:An independent dose calculation tool was developed from commercial software. Thirty‐three previously approved cervical HDR plans with the same prescription dose (550cGy), applicator type, and treatment protocol were examined, and ICRU defined reference point doses (bladder, vaginal mucosa, rectum, and points A/B) along with dwell times were collected. Dose calculation tool then calculated appropriate range with a 95% confidence interval for each parameter obtained, which would be used as the benchmark for evaluation of those parameters in future HDR treatment plans. Model quality was verified using five randomly selected approved plans from the same dataset.Results:Dose variations appears to be larger at the reference point of bladder and mucosa as compared with rectum. Most reference point doses from verification plans fell between the predicted range, except the doses of two points of rectum and two points of reference position A (owing to rectal anatomical variations & clinical adjustment in prescription points, respectively). Similar results were obtained for tandem and ring dwell times despite relatively larger uncertainties.Conclusion:This statistical tool provides an insight into clinically acceptable range of cervical HDR plans, which could be useful in plan checking and identifying potential planning errors, thus improving the consistency of plan quality.