bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐G‐TeP4‐05: An Evaluation of a Low Dose Rate (LDR) Prostate Brachytherapy Procedure Using a Failure Modes and Effects Analysis (FMEA)
Authors: Cheong S‐K , Kim J 
Published: July 2016

Purpose:The aim of the study is the application of a Failure Modes and Effects Analysis (FMEA) to access the risks for patients undergoing a Low Dose Rate (LDR) Prostate Brachytherapy Treatment.Methods:FMEA was applied to identify all the sub processes involved in the stages of identifying patient, source handling, treatment preparation, treatment delivery, and post treatment. These processes characterize the radiation treatment associated with LDR Prostate Brachytherapy. The potential failure modes together with their causes and effects were identified and ranked in order of their importance. Three indexes were assigned for each failure mode: the occurrence rating (O), the severity rating (S), and the detection rating (D). A ten‐point scale was used to score each category, ten being the number indicating most severe, most frequent, and least detectable failure mode, respectively. The risk probability number (RPN) was calculated as a product of the three attributes: RPN = O X S x D. The analysis was carried out by a working group (WG) at UPMC.Results:The total of 56 failure modes were identified including 32 modes before the treatment, 13 modes during the treatment, and 11 modes after the treatment. In addition to the protocols already adopted in the clinical practice, the prioritized risk management will be implanted to the high risk procedures on the basis of RPN score.Conclusion:The effectiveness of the FMEA method was established. The FMEA methodology provides a structured and detailed assessment method for the risk analysis of the LDR Prostate Brachytherapy Procedure and can be applied to other radiation treatment modes.
SU‐G‐TeP4‐06: An Integrated Application for Radiation Therapy Treatment Plan Directives, Management, and Reporting
Authors: Matuszak M , Anderson C , Lee C , Vineberg K , Green M , Younge K , Moran J , Mayo C 
Published: July 2016

Purpose:With electronic medical records, patient information for the treatment planning process has become disseminated across multiple applications with limited quality control and many associated failure modes. We present the development of a single application with a centralized database to manage the planning process.Methods:The system was designed to replace current functionalities of (i) static directives representing the physician intent for the prescription and planning goals, localization information for delivery, and other information, (ii) planning objective reports, (iii) localization and image guidance documents and (iv) the official radiation therapy prescription in the medical record. Using the Eclipse Scripting Application Programming Interface, a plug‐in script with an associated domain‐specific SQL Server database was created to manage the information in (i)–(iv). The system's user interface and database were designed by a team of physicians, clinical physicists, database experts, and software engineers to ensure usability and robustness for clinical use.Results:The resulting system has been fully integrated within the TPS via a custom script and database. Planning scenario templates, version control, approvals, and logic‐based quality control allow this system to fully track and document the planning process as well as physician approval of tradeoffs while improving the consistency of the data. Multiple plans and prescriptions are supported along with non‐traditional dose objectives and evaluation such as biologically corrected models, composite dose limits, and management of localization goals. User‐specific custom views were developed for the attending physician review, physicist plan checks, treating therapists, and peer review in chart rounds.Conclusion:A method was developed to maintain cohesive information throughout the planning process within one integrated system by using a custom treatment planning management application that interfaces directly with the TPS. Future work includes quantifying the improvements in quality, safety and efficiency that are possible with the routine clinical use of this system.Supported in part by NIH‐P01‐CA‐059827
SU‐G‐TeP4‐14: Quality Control of Treatment Planning Using Knowledge‐Based Planning Across a System of Radiation Oncology Practices
Authors: Masi K , Ditman M , Marsh R , Dai J , Huberts M , Khadija M , Tatro D , Archer P , Matuszak M 
Published: July 2016

Purpose:There is potentially a wide variation in plan quality for a certain disease site, even for clinics located in the same system of hospitals. We have used a prostate‐specific knowledge‐based planning (KBP) model as a quality control tool to investigate the variation in prostate treatment planning across a network of affiliated radiation oncology departments.Methods:A previously created KBP model was applied to 10 patients each from 4 community‐based clinics (Clinics A, B, C, and D). The KBP model was developed using RapidPlan (Eclipse v13.5, Varian Medical Systems) from 60 prostate/prostate bed IMRT plans that were originally planned using an in‐house treatment planning system at the central institution of the community‐based clinics. The dosimetric plan quality (target coverage and normal‐tissue sparing) of each model‐generated plan was compared to the respective clinically‐used plan. Each community‐based clinic utilized the same planning goals to develop the clinically‐used plans that were used at the main institution.Results:Across all 4 clinics, the model‐generated plans decreased the mean dose to the rectum by varying amounts (on average, 12.5, 2.6, 4.5, and 2.7 Gy for Clinics A, B, C, and D, respectively). The mean dose to the bladder also decreased with the model‐generated plans (5.4, 2.3, 3.0, and 4.1 Gy, respectively). The KBP model also identified that target coverage (D95%) improvements were possible for for Clinics A, B, and D (0.12, 1.65, and 2.75%) while target coverage decreased by 0.72% for Clinic C, demonstrating potentially different trade‐offs made in clinical plans at different institutions.Conclusion:Quality control of dosimetric plan quality across a system of radiation oncology practices is possible with knowledge‐based planning. By using a quality KBP model, smaller community‐based clinics can potentially identify the areas of their treatment plans that may be improved, whether it be in normal‐tissue sparing or improved target coverage.M. Matuszak has research funding for KBP from Varian Medical Systems
MO‐AB‐204‐03: Profile Development and IHE Process
Authors: Pauer C. 
Published: July 2016

You've experienced the frustration: vendor A's device claims to work with vendor B's device, but the practice doesn't match the promise. Getting devices working together is the hidden art that Radiology and Radiation Oncology staff have to master. To assist with that difficult process, the Integrating the Healthcare Enterprise (IHE) effort was established in 1998, with the coordination of the Radiological Society of North America.Integrating the Healthcare Enterprise (IHE) is a consortium of healthcare professionals and industry partners focused on improving the way computer systems interconnect and exchange information. This is done by coordinating the use of published standards like DICOM and HL7. Several clinical and operational IHE domains exist in the healthcare arena, including Radiology and Radiation Oncology. The ASTRO‐sponsored IHE Radiation Oncology (IHE‐RO) domain focuses on radiation oncology specific information exchange. This session will explore the IHE Radiology and IHE RO process for;

IHE solicitation process for new profiles.
Improving the way computer systems interconnect and exchange information in the healthcare enterprise
Supporting interconnectivity descriptions and proof of adherence by vendors
Testing and assuring the vendor solutions to connectivity problems.
Including IHE profiles in RFPs for future software and hardware purchases.
Learning Objectives:
Understand IHE role in improving interoperability in health care.
Understand process of profile development and implantation.
Understand how vendors prove adherence to IHE RO profiles.S. Hadley, ASTRO Supported Activity
MO‐A‐201‐00: A Cliff's Notes Version of Proton Therapy
Authors: Kruse Jon 
Published: July 2016

Proton therapy is a rapidly growing modality in the fight against cancer. From a high‐level perspective the process of proton therapy is identical to x‐ray based external beam radiotherapy. However, this course is meant to illustrate for x‐ray physicists the many differences between x‐ray and proton based practices.Unlike in x‐ray therapy, proton dose calculations use CT Hounsfield Units (HU) to determine proton stopping power and calculate the range of a beam in a patient. Errors in stopping power dominate the dosimetric uncertainty in the beam direction, while variations in patient position determine uncertainties orthogonal to the beam path. Mismatches between geometric and range errors lead to asymmetric uncertainties, and so while geometric uncertainties in x‐ray therapy are mitigated through the use of a Planning Target Volume (PTV), this approach is not suitable for proton therapy. Robust treatment planning and evaluation are critical in proton therapy, and will be discussed in this course.Predicting the biological effect of a proton dose distribution within a patient is also a complex undertaking. The proton therapy community has generally regarded the Radiobiological Effectiveness (RBE) of a proton beam to be 1.1 everywhere in the patient, but there are increasing data to suggest that the RBE probably climbs higher than 1.1 near the end of a proton beam when the energy deposition density increases. This lecture will discuss the evidence for variable RBE in proton therapy and describe how this is incorporated into current proton treatment planning strategies.Finally, there are unique challenges presented by the delivery process of proton therapy. Many modern systems use a spot scanning technique which has several advantages over earlier scattered beam designs. However, the time dependence of the dose deposition leads to greater concern with organ motion than with scattered protons or x‐rays. Image guidance techniques in proton therapy may also differ from standard x‐ray approaches, due to equipment design or the desire to maximize efficiency within a high‐cost proton therapy treatment room. Differences between x‐ray and proton therapy delivery will be described.

Learning Objectives:
Understand how CT HU are calibrated to provide proton stopping power, and the sources of uncertainty in this process.
Understand why a PTV is not suitable for proton therapy, and how robust treatment planning and evaluation are used to mitigate uncertainties.
Understand the source and implications of variable RBE in proton therapy
Learn about proton specific challenges and approaches in beam delivery and image guidanceJon Kruse has a research grant from Varian Medical Systems related to proton therapy treatment plannning.; J. Kruse, Jon Kruse has a research grant with Varian Medical Systems related to proton therapy planning.
MO‐A‐201‐01: A Cliff's Notes Version of Proton Therapy
Authors: Kruse J. 
Published: July 2016

Proton therapy is a rapidly growing modality in the fight against cancer. From a high‐level perspective the process of proton therapy is identical to x‐ray based external beam radiotherapy. However, this course is meant to illustrate for x‐ray physicists the many differences between x‐ray and proton based practices.Unlike in x‐ray therapy, proton dose calculations use CT Hounsfield Units (HU) to determine proton stopping power and calculate the range of a beam in a patient. Errors in stopping power dominate the dosimetric uncertainty in the beam direction, while variations in patient position determine uncertainties orthogonal to the beam path. Mismatches between geometric and range errors lead to asymmetric uncertainties, and so while geometric uncertainties in x‐ray therapy are mitigated through the use of a Planning Target Volume (PTV), this approach is not suitable for proton therapy. Robust treatment planning and evaluation are critical in proton therapy, and will be discussed in this course.Predicting the biological effect of a proton dose distribution within a patient is also a complex undertaking. The proton therapy community has generally regarded the Radiobiological Effectiveness (RBE) of a proton beam to be 1.1 everywhere in the patient, but there are increasing data to suggest that the RBE probably climbs higher than 1.1 near the end of a proton beam when the energy deposition density increases. This lecture will discuss the evidence for variable RBE in proton therapy and describe how this is incorporated into current proton treatment planning strategies.Finally, there are unique challenges presented by the delivery process of proton therapy. Many modern systems use a spot scanning technique which has several advantages over earlier scattered beam designs. However, the time dependence of the dose deposition leads to greater concern with organ motion than with scattered protons or x‐rays. Image guidance techniques in proton therapy may also differ from standard x‐ray approaches, due to equipment design or the desire to maximize efficiency within a high‐cost proton therapy treatment room. Differences between x‐ray and proton therapy delivery will be described.

Learning Objectives:
Understand how CT HU are calibrated to provide proton stopping power, and the sources of uncertainty in this process.
Understand why a PTV is not suitable for proton therapy, and how robust treatment planning and evaluation are used to mitigate uncertainties.
Understand the source and implications of variable RBE in proton therapy
Learn about proton specific challenges and approaches in beam delivery and image guidanceJon Kruse has a research grant from Varian Medical Systems related to proton therapy treatment plannning.; J. Kruse, Jon Kruse has a research grant with Varian Medical Systems related to proton therapy planning.
MO‐A‐BRC‐01: TG167 Report ‐ Introduction
Authors: Nath R. 
Published: July 2016

Although a multicenter, Phase III, prospective, randomized trial is the gold standard for evidence‐based medicine, it is rarely used to evaluate innovative radiotherapy devices because of many practical and ethical reasons. It is usually sufficient to compare the dose distributions and dose rates for determining equivalence of the innovative device to an existing one. Thus, quantitative evaluation of the dosimetric characteristics of an innovative brachytherapy device or application is a critical part in which physicists are actively involved. The physicist's role, along with physician colleagues, in this process is highlighted for innovative products or applications and includes evaluation of 1) dosimetric considerations for clinical implementation (including calibrations, dose calculations, and radiobiological aspects) to comply with existing societal dosimetric prerequisites for sources in routine clinical use, 2) risks and benefits from regulatory and safety perspectives, and 3) resource assessment and preparedness. Further, calibration methods should be traceable to a primary standards dosimetry laboratory such as NIST in the U.S. or to other primary standards dosimetry laboratory located elsewhere. Clinical users should follow standards as approved by their country's regulatory agencies that approved such a brachytherapy device. Integration of this system into the medical source calibration infrastructure of secondary standard dosimetry laboratories such as the ADCLs is encouraged before a source is introduced into widespread routine clinical use. The AAPM and GEC‐ESTRO have developed guidelines for the safe and consistent application of brachytherapy using innovative brachytherapy devices and applications. The current report covers regulatory approvals, calibration, dose calculations, radiobiological issues, and overall safety concerns that should be addressed during the commissioning stage preceding clinical use. These guidelines are based on review of requirements of the U.S. NRC, FDA, Department of Transportation, International Electrotechnical Commission Medical Electrical Equipment Standard 60601, European Commission for CE Marking, and institutional review boards and radiation safety committees.

Learning Objectives:
Understand the necessary dosimetric considerations for clinical implementation (including calibrations, dose calculations, and radiobiological aspects) to comply with existing societal dosimetric prerequisites for sources in routine clinical use.
Evaluate risks and benefits from regulatory and safety perspectives.
Identify necessary resources and create a plan for clinical introduction of innovative brachytherapy device or applications.Consultant for Theragenics Corp.; R. Nath, Consultant to Theragenics Corp.
MO‐A‐BRB‐00: TG191: Clinical Use of Luminescent Dosimeters
Authors: Kry Stephen 
Published: July 2016

This presentation will highlight the upcoming TG‐191 report: Clinical Use of Luminescent Dosimeters. Luminescent dosimetry based on TLD and OSLD is a practical, accurate, and precise technique for point dosimetry in medical physics applications. The charges of Task Group 191 were to detail the methodologies for practical and optimal luminescent dosimetry in a clinical setting. This includes (1) To review the variety of TLD/OSL materials available, including features and limitations of each. (2) To outline the optimal steps to achieve accurate and precise dosimetry with luminescent detectors and to evaluate the uncertainty induced when less rigorous procedures are used. (3) To develop consensus guidelines on the optimal use of luminescent dosimeters for clinical practice. (4) To develop guidelines for special medically relevant uses of TLDs/OSLs (e.g., mixed field i.e. photon/neutron dosimetry, particle beam dosimetry, skin dosimetry). While this report provides general guidelines for arbitrary TLD and OSLD processes, the report, and therefore this presentation, provide specific guidance for TLD‐100 (LiF:Ti,Mg) and nanoDot (Al2O3:C) dosimeters because of their prevalence in clinical practice.

Learning Objectives:
Understand the available dosimetry systems, and basic theory of their operation
Understand the range of dose determination methodologies and the uncertainties associated with them
Become familiar with special considerations for TLD/OSLD relevant for special clinical situations
Learn recommended commissioning and QA procedures for these dosimetry systems.
MO‐AB‐BRA‐05: [18F]NaF PET/CT Imaging Biomarkers in Metastatic Prostate Cancer
Authors: Harmon S , Perk T , Lin C , Eickhoff J , Choyke P , Dahut W , Apolo A , Humm J , Larson S , Morris MJ , Perlman S , Liu G , Jeraj R 
Published: July 2016

Purpose:Clinical use of 18F‐Sodium Fluoride (NaF) PET/CT in metastatic settings often lacks technology to quantitatively measure full disease dynamics due to high tumor burden. This study assesses radiomics‐based extraction of NaF PET/CT measures, including global metrics of overall burden and local metrics of disease heterogeneity, in metastatic prostate cancer for correlation to clinical outcomes.Methods:Fifty‐six metastatic Castrate‐Resistant Prostate Cancer (mCRPC) patients had NaF PET/CT scans performed at baseline and three cycles into chemotherapy (N=16) or androgen‐receptor (AR) inhibitors (N=39). A novel technology, Quantitative Total Bone Imaging (QTBI), was used for analysis. Employing hybrid PET/CT segmentation and articulated skeletal‐registration, QTBI allows for response assessment of individual lesions. Various SUV metrics were extracted from each lesion (iSUV). Global metrics were extracted from composite lesion‐level statistics for each patient (pSUV). Proportion of detected lesions and those with significant response (%‐increase or %‐decrease) was calculated for each patient based on test‐retest limits for iSUV metrics. Cox proportional hazard regression analyses were conducted between imaging metrics and progression‐free survival (PFS).Results:Functional burden (pSUVtotal) assessed mid‐treatment was the strongest univariate predictor of PFS (HR=2.03; p<0.0001). Various global metrics outperformed baseline clinical markers, including fraction of skeletal burden, mean uptake (pSUVmean), and heterogeneity of average lesion uptake (pSUVhetero). Of 43 patients with paired baseline/mid‐treatment imaging, 40 showed heterogeneity in lesion‐level response, containing populations of lesions with both increasing/decreasing metrics. Proportion of lesions with significantly increasing iSUVmean was highly predictive of clinical PFS (HR=2.0; p=0.0002). Patients exhibiting higher proportion of lesions with decreasing iSUVtotal saw prolonged radiographic PFS (HR=0.51; p=0.02).Conclusion:Technology presented here provides comprehensive disease quantification on NaF PET/CT imaging, showing strong correlation to clinical outcomes. Total functional burden as well as proportions of similarly responding lesions was predictive of PFS. This supports ongoing development of NaF PET/CT based imaging biomarkers in mCRPC.Prostate Cancer Foundation
MO‐AB‐BRA‐09: Development and Evaluation of a Biomechanical Modeling‐Assisted CBCT Reconstruction Technique (Bio‐Recon)
Authors: Zhang Y , Nasehi Tehrani J , Wang J 
Published: July 2016

Purpose:To develop a Bio‐recon technique by incorporating the biomechanical properties of anatomical structures into the deformation‐based CBCT reconstruction process.Methods:Bio‐recon reconstructs the CBCT by deforming a prior high‐quality CT/CBCT using a deformation‐vector‐field (DVF). The DVF is solved through two alternating steps: 2D–3D deformation and finite‐element‐analysis based biomechanical modeling. 2D–3D deformation optimizes the DVF through an ‘intensity‐driven’ approach, which updates the DVF to minimize intensity mismatches between the acquired projections and the simulated projections from the deformed CBCT. In contrast, biomechanical modeling optimizes the DVF through a ‘biomechanical‐feature‐driven’ approach, which updates the DVF based on the biophysical properties of anatomical structures. In general, Biorecon extracts the 2D–3D deformation‐optimized DVF at high‐contrast structure boundaries, and uses it as the boundary condition to drive biomechanical modeling to optimize the overall DVF, especially at low‐contrast regions. The optimized DVF is fed back into the 2D–3D deformation for further optimization, which forms an iterative loop. The efficacy of Bio‐recon was evaluated on 11 lung patient cases, each with a prior CT and a new CT. Cone‐beam projections were generated from the new CTs to reconstruct CBCTs, which were compared with the original new CTs for evaluation. 872 anatomical landmarks were also manually identified by a clinician on both the prior and new CTs to track the lung motion, which was used to evaluate the DVF accuracy.Results:Using 10 projections for reconstruction, the average (± s.d.) relative errors of reconstructed CBCTs by the clinical FDK technique, the 2D–3D deformation‐only technique and Bio‐recon were 46.5±5.9%, 12.0±2.3% and 10.4±1.3%, respectively. The average residual errors of DVF‐tracked landmark motion by the 2D–3D deformation‐only technique and Bio‐recon were 5.6±4.3mm and 3.1±2.4mm, respectively.Conclusion:Bio‐recon improved accuracy for both the reconstructed CBCT and the DVF. The accurate DVF can benefit multiple clinical practices, such as image‐guided adaptive radiotherapy.We acknowledge funding support from the American Cancer Society (RSG‐13‐326‐01‐CCE), from the US National Institutes of Health (R01 EB020366), and from the Cancer Prevention and Research Institute of Texas (RP130109).