bluuebunny/crossref_metadata_2025_split huggingface dataset:
WE‐AB‐206‐02: ACR Ultrasound Accreditation: Requirements and Pitfalls
Authors: Walter J. 
Published: July 2016

The involvement of medical physicists in diagnostic ultrasound imaging service is increasing due to QC and accreditation requirements. The goal of this ultrasound hands‐on workshop is to demonstrate quality control (QC) testing in diagnostic ultrasound and to provide updates in ACR ultrasound accreditation requirements. The first half of this workshop will include two presentations reviewing diagnostic ultrasound QA/QC and ACR ultrasound accreditation requirements. The second half of the workshop will include live demonstrations of basic QC tests. An array of ultrasound testing phantoms and ultrasound scanners will be available for attendees to learn diagnostic ultrasound QC in a hands‐on environment with live demonstrations and on‐site instructors. The targeted attendees are medical physicists in diagnostic imaging.

Learning Objectives:
Gain familiarity with common elements of a QA/QC program for diagnostic ultrasound imaging
dentify QC tools available for testing diagnostic ultrasound systems and learn how to use these tools
Learn ACR ultrasound accreditation requirementsJennifer Walter is an employee of American College of Radiology on Ultrasound Accreditation.
WE‐AB‐209‐11: Prostate Cancer Treatment Planning: Sensitivity and Representative Objective Function Weights
Authors: Goli A. , Boutilier J.J. , Craig T. , Sharpe M.B. , Chan T.C.Y. 
Published: July 2016

Purpose:To develop an automated planning methodology that exploits patient sensitivity to objective function weights.Methods:Given a treatment plan, we first create an acceptable treatment region that encompasses a set of treatment plans with similar clinical performance (e.g., +/−1% at V70Gy). We use inverse optimization to map this region in criterion space to the weight space and find a corresponding region of acceptable weight vectors (W). The shape and size of W describes how sensitive a patient is to perturbations in objective function weights. To exploit the information encoded by these regions, we approximate W for each patient by a polyhedron and we cluster patients using a novel integer programming model with cluster sizes from k=1,2,…,10. Each cluster centroid is a representative objective function weight vector and we use these weight vectors to generate k treatment plans for each patient (AUTO plans). Using 315 prostate cancer plans, we determine the number of patients that would have received an improved treatment plan using our automated approach.Results:Clustering patients into five groups produced a global set of representative weights such that for 88% of patients there exists at least one AUTO plan that improves upon the clinical treatment plan in terms of organ‐at‐risk mean dose and clinical acceptability criteria satisfaction (i.e., V54Gy50% and V70Gy30%). The AUTO plans provided bladder or rectum mean dose improvement over the clinical treatment plans for 296 (94%) patients, bladder mean dose improvement for 185 (59%) patients, rectum mean dose improvement for 273 (87%) patients, and mean dose improvements for both bladder and rectum in 162 (51%) patients. The AUTO plans provided fewer violations for bladder/rectum V54Gy50% and slightly more for bladder/rectum V70Gy30% when compared to clinical plans.Conclusion:A method combining inverse optimization and clustering automatically produces prostate treatment plans for 88% of patients.
WE‐AB‐207B‐02: A Bayesian Network Approach for Joint Prediction of Tumor Control and Radiation Pneumonitis (RP) in Non‐Small‐Cell Lung Cancer (NSCLC)
Authors: Luo Y , McShan D , Matuszak M , Hobson S , Jolly S , Ten Haken R , El Naqa I 
Published: July 2016

Purpose:NSCLC radiotherapy treatment is a trade‐off between controlling the tumor while limiting radiation‐induced toxicities. Here we identify hierarchical biophysical relationships that could simultaneously influence both local control (LC) and RP by using an integrated Bayesian Networks (BN) approach.Methods:We studied 79 NSCLC patients treated on prospective protocol with 56 cases of LC and 21 events of RP. Beyond dosimetric information, each patient had 193 features including 12 clinical factors, 60 circulating blood cytokines before and during radiotherapy, 62 microRNAs, and 59 single‐nucleotide polymorphisms (SNPs). The most relevant biophysical predictors for both LC and RP were identified using a Markov blanket local discovery algorithm and the corresponding BN was constructed using a score‐learning algorithm. The area under the free‐response receiver operating characteristics (AU‐FROC) was used for performance evaluation. Cross‐validation was employed to guard against overfitting pitfalls.Results:A BN revealing the biophysical interrelationships jointly in terms of LC and RP was developed and evaluated. The integrated BN included two SNPs, one microRNA, one clinical factor, three pre‐treatment cytokines, relative changes of two cytokines between pre and during‐treatment, and gEUDs of the GTV (a=‐20) and lung (a=1). On cross‐validation, the AUC prediction of independent LC was 0.85 (95% CI: 0.75–0.95) and RP was 0.83 (0.73–0.92). The AU‐FROC of the integrated BN to predict both LC/RP was 0.81 (0.71–0.90) based on 2000 stratified bootstrap, indicating minimal loss in joint prediction power.Conclusions:We developed a new approach for multiple outcome utility application in radiotherapy based on integrated BN techniques. The BN developed from large‐scale retrospective data is able to simultaneously predict LC and RP in NSCLC treatments based on individual patient characteristics. The joint prediction is only slightly compromised compared to independent predictions. Our approach shows promise for use in clinical decision support system for personalized radiotherapy subject to multiple endpoints.These studies were supported by a grant from the NCI/NIH P01‐CA59827.
WE‐AB‐207B‐04: A Preliminary Investigation of Indicators for Treatment Outcomes of CT Guided Cervical Cancer Brachytherapy
Authors: Meerschaert R , Paul A , Chen W , Miller S , Zhuang L 
Published: July 2016

Purpose:To identify indicators for recurrence in cervical cancer patients treated with high‐dose‐rate brachytherapy (HDR‐BT).Methods:A total of 37 biopsy proven uterine cervical cancer patients of stage IB‐IVA treated between 2011 and 2015 were included in this study. All patients were treated with 37.8–52.2Gy of external beam radiotherapy (EBRT) plus 5 × 5.0–6.5Gy of HDR‐BT. Patient age, standard deviation of high‐risk clinical tumor volume (HR‐CTVSD) collected throughout HDR‐BT, and D90 (EQD2, α/β=10Gy) of the HR‐CTV were investigated as potential indicators for local/distant recurrence using ROC analysis. The optimal cutoff value was identified through the Youden index and was subsequently used to obtain a group assignment for all patients. Another two comparing groups were defined per evidence of post‐EBRT tumor shrinkage based on the pre‐HDR MR scan. Kaplan‐Meier curves were generated for recurrence‐free proportions for comparing groups where time was measured from the final HDR treatment date to the date of the final follow‐up exam and compared through Cox regression.Results:Patients had a median follow‐up of 12 months, where 16% had local pelvic recurrence and 16% had distant recurrence. HR‐CTVSD was identified as a statistically significant indicator for recurrence (AUC=0.802, p=0.007) compared to D90 (AUC=0.655, p=0.167) and patient age (AUC=0.683, p=0.103). HR‐CTVSD of 6.26cc was chosen as the optimal cutoff value, which was used as the basis for patient group assignment. Patients with HR‐CTVSD>6.26cc had a hazard ratio of 3.92 (95% CI‐1.37, 11.24; p=0.011) for recurrence compared to HRCTVSD≤ 6.26cc. Patients without evidence of post‐EBRT tumor shrinkage compared to those with had a hazard ratio of 4.28 (95% CI‐1.14, 16.15; p=0.032) for recurrence.Conclusion:HR‐CTVSD was identified as an indicator for recurrence and an optimal value of 6.26cc was established in our study. In addition, patients without evidence of post‐EBRT tumor shrinkage demonstrated an increased recurrence rate.
WE‐B‐201‐02: Statistical Failings That Can Keep Us All in the Dark
Authors: Sloan J. 
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‐B‐BRC‐01: Current Methodologies in Risk Assessment
Authors: Rath F. 
Published: July 2016

Prospective quality management techniques, long used by engineering and industry, have become a growing aspect of efforts to improve quality management and safety in healthcare. These techniques are of particular interest to medical physics as scope and complexity of clinical practice continue to grow, thus making the prescriptive methods we have used harder to apply and potentially less effective for our interconnected and highly complex healthcare enterprise, especially in imaging and radiation oncology.An essential part of most prospective methods is the need to assess the various risks associated with problems, failures, errors, and design flaws in our systems. We therefore begin with an overview of risk assessment methodologies used in healthcare and industry and discuss their strengths and weaknesses. The rationale for use of process mapping, failure modes and effects analysis (FMEA) and fault tree analysis (FTA) by TG‐100 will be described, as well as suggestions for the way forward. This is followed by discussion of radiation oncology specific risk assessment strategies and issues, including the TG‐100 effort to evaluate IMRT and other ways to think about risk in the context of radiotherapy. Incident learning systems, local as well as the ASTRO/AAPM ROILS system, can also be useful in the risk assessment process. Finally, risk in the context of medical imaging will be discussed. Radiation (and other) safety considerations, as well as lack of quality and certainty all contribute to the potential risks associated with suboptimal imaging.The goal of this session is to summarize a wide variety of risk analysis methods and issues to give the medical physicist access to tools which can better define risks (and their importance) which we work to mitigate with both prescriptive and prospective risk‐based quality management methods.

Learning Objectives:
Description of risk assessment methodologies used in healthcare and industry
Discussion of radiation oncology‐specific risk assessment strategies and issues
Evaluation of risk in the context of medical imaging and image qualityE. Samei: Research grants from Siemens and GE
WE‐B‐BRC‐03: Risk in the Context of Medical Imaging
Authors: Samei E. 
Published: July 2016

Prospective quality management techniques, long used by engineering and industry, have become a growing aspect of efforts to improve quality management and safety in healthcare. These techniques are of particular interest to medical physics as scope and complexity of clinical practice continue to grow, thus making the prescriptive methods we have used harder to apply and potentially less effective for our interconnected and highly complex healthcare enterprise, especially in imaging and radiation oncology.An essential part of most prospective methods is the need to assess the various risks associated with problems, failures, errors, and design flaws in our systems. We therefore begin with an overview of risk assessment methodologies used in healthcare and industry and discuss their strengths and weaknesses. The rationale for use of process mapping, failure modes and effects analysis (FMEA) and fault tree analysis (FTA) by TG‐100 will be described, as well as suggestions for the way forward. This is followed by discussion of radiation oncology specific risk assessment strategies and issues, including the TG‐100 effort to evaluate IMRT and other ways to think about risk in the context of radiotherapy. Incident learning systems, local as well as the ASTRO/AAPM ROILS system, can also be useful in the risk assessment process. Finally, risk in the context of medical imaging will be discussed. Radiation (and other) safety considerations, as well as lack of quality and certainty all contribute to the potential risks associated with suboptimal imaging.The goal of this session is to summarize a wide variety of risk analysis methods and issues to give the medical physicist access to tools which can better define risks (and their importance) which we work to mitigate with both prescriptive and prospective risk‐based quality management methods.

Learning Objectives:
Description of risk assessment methodologies used in healthcare and industry
Discussion of radiation oncology‐specific risk assessment strategies and issues
Evaluation of risk in the context of medical imaging and image qualityE. Samei: Research grants from Siemens and GE
WE‐DE‐201‐05: Evaluation of a Windowless Extrapolation Chamber Design and Monte Carlo Based Corrections for the Calibration of Ophthalmic Applicators
Authors: Hansen J , Culberson W , Soares C , DeWerd L 
Published: July 2016

Purpose:To test the validity of a windowless extrapolation chamber used to measure surface dose rate from planar ophthalmic applicators and to compare different Monte Carlo based codes for deriving correction factors.Methods:Dose rate measurements were performed using a windowless, planar extrapolation chamber with a 90Sr/90Y Tracerlab RA‐1 ophthalmic applicator previously calibrated at the National Institute of Standards and Technology (NIST). Capacitance measurements were performed to estimate the initial air gap width between the source face and collecting electrode. Current was measured as a function of air gap, and Bragg‐Gray cavity theory was used to calculate the absorbed dose rate to water. To determine correction factors for backscatter, divergence, and attenuation from the Mylar entrance window found in the NIST extrapolation chamber, both EGSnrc Monte Carlo user code and Monte Carlo N‐Particle Transport Code (MCNP) were utilized. Simulation results were compared with experimental current readings from the windowless extrapolation chamber as a function of air gap. Additionally, measured dose rate values were compared with the expected result from the NIST source calibration to test the validity of the windowless chamber design.Results:Better agreement was seen between EGSnrc simulated dose results and experimental current readings at very small air gaps (<100 µm) for the windowless extrapolation chamber, while MCNP results demonstrated divergence at these small gap widths. Three separate dose rate measurements were performed with the RA‐1 applicator. The average observed difference from the expected result based on the NIST calibration was −1.88% with a statistical standard deviation of 0.39% (k=1).Conclusion:EGSnrc user code will be used during future work to derive correction factors for extrapolation chamber measurements. Additionally, experiment results suggest that an entrance window is not needed in order for an extrapolation chamber to provide accurate dose rate measurements for a planar ophthalmic applicator.
WE‐DE‐201‐11: Sensitivity and Specificity of Verification Methods Based On Total Reference Air Kerma (TRAK) Or On User Provided Dose Points for Graphically Planned Skin HDR Brachytherapy
Authors: Damato A , Devlin P , Bhagwat M , Buzurovic I , Hansen J , O'Farrell D , Cormack R 
Published: July 2016

Purpose:To investigate the sensitivity and specificity of a novel verification methodology for image‐guided skin HDR brachytherapy plans using a TRAK‐based reasonableness test, compared to a typical manual verification methodology.Methods:Two methodologies were used to flag treatment plans necessitating additional review due to a potential discrepancy of 3 mm between planned dose and clinical target in the skin. Manual verification was used to calculate the discrepancy between the average dose to points positioned at time of planning representative of the prescribed depth and the expected prescription dose. Automatic verification was used to calculate the discrepancy between TRAK of the clinical plan and its expected value, which was calculated using standard plans with varying curvatures, ranging from flat to cylindrically circumferential. A plan was flagged if a discrepancy >10% was observed. Sensitivity and specificity were calculated using as a criteria for true positive that >10% of plan dwells had a distance to prescription dose >1 mm different than prescription depth (3 mm + size of applicator). All HDR image‐based skin brachytherapy plans treated at our institution in 2013 were analyzed.Results:108 surface applicator plans to treat skin of the face, scalp, limbs, feet, hands or abdomen were analyzed. Median number of catheters was 19 (range, 4 to 71) and median number of dwells was 257 (range, 20 to 1100). Sensitivity/specificity were 57%/78% for manual and 70%/89% for automatic verification.Conclusion:A check based on expected TRAK value is feasible for irregularly shaped, image‐guided skin HDR brachytherapy. This test yielded higher sensitivity and specificity than a test based on the identification of representative points, and can be implemented with a dedicated calculation code or with pre‐calculated lookup tables of ideally shaped, uniform surface applicators.
WE‐DE‐BRC‐06: Exercise
Authors: Dunscombe P. 
Published: July 2016

This SAMs Therapy Educational Course will introduce participants to the tools recommended by TG 100 for use in the development of a risk‐based Quality Management Program. The Course will start with an Overview of the background and rationale behind the TG 100 initiative, from which its charge was developed. After setting the scene, four 15 minute presentations will introduce the principal components of the soon to be published Report of TG 100. These are Process Mapping, Failure Modes and Effects Analysis, Fault Tree Analysis and the development of a QA/QM risk‐based Program. There will be time for two short exercises based on Failure Modes and Effects Analysis and a Discussion before the SAMs questions which will conclude the session.

Learning Objectives:
To appreciate the underlying philosophy of the TG 100 initiative.
To gain a brief overview of the principal risk‐based tools recommended by TG 100.
In an informal workshop format, to explore, at an introductory level, the use of risk analysis as proposed by TG 100 as a prelude to the development of a Quality Management Program.
WE‐DE‐BRA‐10: Development of a Novel Scanning Beam Low‐Energy Intraoperative Radiation Therapy (SBIORT) System for Pancreatic Cancer
Authors: Wears B , Mohiuddin I , Flynn R , Waldron T , Kim Y , Allen B , Xia J 
Published: July 2016

Purpose:Developing a compact collimator system and validating a 3D surface imaging module for a scanning beam low‐energy x‐ray radiation therapy (SBIORT) system that enables delivery of non‐uniform radiation dose to targets with irregular shapes intraoperatively.Methods:SBIORT consists of a low energy x‐ray source, a custom compact collimator module, a robotic arm, and a 3D surface imaging module. The 3D surface imaging system (structure sensor) is utilized for treatment planning and motion monitoring of the surgical cavity. SBIORT can deliver non‐uniform dose distributions by dynamically moving the x‐ray source assembly along optimal paths with various collimator apertures. The compact collimator utilizes a dynamic shutter mechanism to form a variable square aperture. The accuracy and reproducibility of the collimator were evaluated using a high accuracy encoder and a high resolution camera platform. The dosimetrical characteristics of the collimator prototype were evaluated using EBT3 films with a Pantak Therapax unit. The accuracy and clinical feasibility of the 3D imaging system were evaluated using a phantom and a cadaver cavity.Results:The SBIORT collimator has a compact size: 66 mm diameter and 10 mm thickness with the maximum aperture of 20 mm. The mechanical experiment indicated the average accuracy of leaf position was 0.08 mm with a reproducibility of 0.25 mm at 95% confidence level. The dosimetry study indicated the collimator had a penumbra of 0.35 mm with a leaf transmission of 0.5%. 3D surface scans can be acquired in 5 seconds. The average difference between the acquired 3D surface and the ground truth is 1 mm with a standard deviation of 0.6 mm.Conclusion:This work demonstrates the feasibility of the compact collimator and 3D scanning system for the SBIORT. SBIORT is a way of delivering IORT with a compact system that requires minimum shielding of the procedure room.This research is supported by the University of Iowa Internal Funding Initiatives
WE‐DE‐207B‐04: Quantitative Contrast‐Enhanced Spectral Mammography Based On Photon‐Counting Detectors: A Feasibility Study
Authors: Ding H , Zhou B , Beidokhti D , Molloi S 
Published: July 2016

Purpose:To investigate the feasibility of accurate quantification of iodine mass thickness in contrast‐enhanced spectral mammography.Methods:Experimental phantom studies were performed on a spectral mammography system based on Si strip photon‐counting detectors. Dual‐energy images were acquired using 40 kVp and a splitting energy of 34 keV with 3 mm Al pre‐filtration. The initial calibration was done with glandular and adipose tissue equivalent phantoms of uniform thicknesses and iodine disk phantoms of various concentrations. A secondary calibration was carried out using the iodine signal obtained from the dual‐energy decomposed images and the known background phantom thicknesses and densities. The iodine signal quantification method was validated using phantoms composed of a mixture of glandular and adipose materials, for various breast thicknesses and densities. Finally, the traditional dual‐energy weighted subtraction method was also studied as a comparison. The measured iodine signal from both methods was compared to the known iodine concentrations of the disk phantoms to characterize the quantification accuracy.Results:There was good agreement between the iodine mass thicknesses measured using the proposed method and the known values. The root‐mean‐square (RMS) error was estimated to be 0.2 mg/cm2. The traditional weighted subtraction method also predicted a linear correlation between the measured signal and the known iodine mass thickness. However, the correlation slope and offset values were strongly dependent on the total breast thickness and density.Conclusion:The results of the current study suggest that iodine mass thickness can be accurately quantified with contrast‐enhanced spectral mammography. The quantitative information can potentially improve the differentiation between benign and malignant legions.Grant funding from Philips Medical Systems.