bluuebunny/crossref_metadata_2025 huggingface dataset:
MO‐G‐304‐03: Development of Knowledge Models for IMRT Treatment Planning Utilizing a Rapid Learning Approach
Authors: Yuan L , Ge Y , Sheng Y , Yin F , Wu Q J
Published: June 2015

Purpose:

We present an implementation of the knowledge‐based planning method utilizing a rapid learning approach which enables the accurate evolution of models as new data are collected during daily clinical practice.

Methods:

The efficacy of knowledge‐based planning depends on the accuracy of the knowledge models. Existing methods for knowledge modeling are all based on batch training approach using a static set of training cases. In this study, we investigate a rapid learning approach to train the models using plans from daily clinical practice. A base knowledge model is first established from an existing database. Then each subsequent case continuously evaluates and updates the knowledge model. At each step, evaluation criteria including the studentized residual and the leverage are calculated. They are used to identify outliers from the current model and their causes. Different learning actions are triggered by these criteria. The efficiency and accuracy of the learning approach is quantified by the learning curve. As an initial validation, 100 cases in the pelvic region, including low‐to‐intermediate risk (n=40), high risk (n=20) prostate, and anorectal (n=40) cases are used. The model‐predicted generalized equivalent uniform dose (gEUD) for bladder and rectum are compared with clinical values by a bootstrap validation method.

Results:

The Median Absolute Differences between the predicted OAR gEUD and the clinical values in all three types of cases gradually decrease as increasing number of training cases are added in training. The knowledge models learned by this method reach comparable level of prediction accuracy at 2.1% of prescription dose as in batch training mode but with less training cases.

Conclusion:

The rapid learning approach facilitates incremental learning of knowledge models from clinical cases of multiple cancer types with improved efficiency. This approach will realize effective implementation of knowledge‐based planning in clinics where new cases and cancer types continue to accumulate.

Partially supported by NIH/NCI under grant #R21CA161389 and a master research grant by Varian Medical Systems.
MO‐F‐CAMPUS‐J‐01: Acoustic Range Verification of Proton Beams: Simulation of Heterogeneity and Clinical Proton Pulses
Authors: Jones K , Sehgal C , Avery S
Published: June 2015

Purpose:

Through simulation, to assess acoustic‐based range verification of proton beams (protoacoustics) under clinical conditions.

Methods:

Pressure waves generated by the energy deposition of a 150 MeV, 8 mm FWHM pulsed pencil proton beam were numerically simulated through two

Methods:

1) For a homogeneous water medium, an analytical wave‐equation solution was used to calculate the time‐dependent pressure measured at detector points surrounding the proton Bragg peak. 2) For heterogeneity studies, a CT tissue image was used to calculate the proton dose deposition and define the acoustic properties of the voxels through which numerical pressure wave propagation was simulated with the k‐Wave matlab toolbox. The simulations were used to assess the dependence of the acoustic amplitude and range‐verification accuracy on proton pulse rise time and tissue heterogeneity.

Results:

As the proton pulse rise time is increased from 1 to 40 µs, the amplitude of the expected acoustic emission decreases (a 60% drop distal to the Bragg peak), the central frequency of the expected signal decreases (from 45 to 6 kHz), and the accuracy of the range‐verification decreases (from <1 mm to 16 mm at 5 cm distal to the Bragg peak). For a 300 nA pulse, the expected pressure range is on the order of 0.1 Pa, which is observable with commercial detectors. For the heterogeneous medium, our test case shows that pressure waves emitted by an anterior pencil beam directed into the abdomen and detected posteriorly can determine the Bragg peak range to an accuracy of <2mm for a 1 µs proton pulse.

Conclusion:

For proton pulses with fast rise‐times, protoacoustics is a promising potential method for monitoring penetration depth through heterogeneous tissue. The loss of range‐verification accuracy with increasing rise‐times, however, suggests the need for comparisons to modeling to improve accuracy for slower cyclotron proton sources.
MO‐F‐CAMPUS‐J‐04: Radiation Heat Load On the MR System of the Elekta Atlantic System
Authors: Towe S , Roberts D , Overweg J , Van Lanen E
Published: June 2015

Purpose:

The Elekta Atlantic system combines a digital linear accelerator system with a 1.5T Philips MRI machine.This study aimed to assess the energy deposited within the cryostat system when the radiation beam passes through the cryostat. The cryocooler on the magnet has a cooling capacity which is about 1 Watt in excess of the cryogenic heat leak into the magnet's cold mass. A pressure‐controlled heater inside the magnet balances the excess refrigeration power such that the helium pressure in the tank is kept slightly above ambient air pressure. If radiation power is deposited in the cold mass then this heater will need less power to maintain pressure equilibrium and if the radiation heat load exceeds the excess cryocooler capacity the pressure will rise.

Methods:

An in‐house CAD based Monte Carlo code based on Penelope was used to model the entire MR‐Linac system to quantify the heat load on the magnet's cold mass. These results were then compared to experimental results obtained from an Elekta Atlantic system installed in UMC‐Utrecht.

Results:

For a field size of 25 cm × 22 cm and a dose rate of 107 mu.min‐1, the energy deposited by the radiation beam led to a reduction in heater power from 1.16 to 0.73 W. Simulations predicted a reduction to 0.69 W which is in good agreement. For the worst case field size (largest) and maximum dose rate the cryostat cooler capacity was exceeded. This resulted in a pressure rise within the system but was such that continuous irradiation for over 12 hours would be required before the magnet would start blowing off helium.

Conclusion:

The study concluded that the Atlantic system does not have to be duty cycle restricted, even for the worst case non‐clinical scenario and that there are no adverse effects on the MR system.

Stephen Towe and David Roberts Both work for Elekta; Ezra Van Lanen works for Philips Healthcare; Johan Overweg works for Philips Innovative Technologies
MO‐F‐CAMPUS‐J‐05: Verification for Prompt Gamma Ray Imaging During Proton Boron Fusion Therapy
Authors: Shin H , Yoon D , Jung J , Kim M , Jang H , Suh T
Published: June 2015

Purpose:

The purpose of this study is to verify the acquisition of the three dimensional single photon emission computed tomography (SPECT) image using prompt gamma ray originated from proton boron fusion therapy (PBFT).

Methods:

The real‐time imaging system during the PBFT was simulated to acquire the tomographic image of the prompt gamma ray from treated tumor by the proton boron reaction, using Monte Carlo simulation (MCNPX). We acquired percentage depth dose (PDD) of the proton beam in the water phantom including the boron uptake region (BUR), the energy spectra of the prompt gamma ray and tomographic image which can show treated tumor regions. The prompt gamma ray image was reconstructed using maximum likelihood estimation maximization (MLEM) reconstruction algorithm with 64 projections. In addition, in order to evaluate the reconstructed image, the image profiles between BURs were extracted from the image.

Results:

In the PDD results, the Bragg‐peak was amplified definitely when the proton's maximum dose level was located at the BUR. This amplification is based on the generation of alpha particles. In addition, the prompt gamma ray peak of 719 keV was observed from the energy spectrum. Through the previous process, the tomographic image of prompt gamma ray from the BUR was reconstructed. The line profile was extracted from image including BURs, and it shows high signal‐to‐noise ratio.

Conclusion:

We confirmed that the real‐time prompt gamma ray image during the PBFT was successfully deducted, and results of quantitative image analysis show good agreement with the original pattern of the BUR. This study first verified the imaging capability of the PBFT‐SPECT system. In conclusion, the PBFT‐SPECT system can realize the treated tumor monitoring during the PBFT.
MO‐F‐CAMPUS‐I‐03: GPU Accelerated Monte Carlo Technique for Fast Concurrent Image and Dose Simulation
Authors: Becchetti M , Tian X , Segars P , Samei E
Published: June 2015

Purpose:

To develop an accurate and fast Monte Carlo (MC) method of simulating CT that is capable of correlating dose with image quality using voxelized phantoms.

Methods:

A realistic voxelized phantom based on patient CT data, XCAT, was used with a GPU accelerated MC code for helical MDCT. Simulations were done with both uniform density organs and with textured organs. The organ doses were validated using previous experimentally validated simulations of the same phantom under the same conditions. Images acquired by tracking photons through the phantom with MC require lengthy computation times due to the large number of photon histories necessary for accurate representation of noise. A substantial speed up of the process was attained by using a low number of photon histories with kernel denoising of the projections from the scattered photons. These FBP reconstructed images were validated against those that were acquired in simulations using many photon histories by ensuring a minimal normalized root mean square error.

Results:

Organ doses simulated in the XCAT phantom are within 10% of the reference values. Corresponding images attained using projection kernel smoothing were attained with 3 orders of magnitude less computation time compared to a reference simulation using many photon histories.

Conclusion:

Combining GPU acceleration with kernel denoising of scattered photon projections in MC simulations allows organ dose and corresponding image quality to be attained with reasonable accuracy and substantially reduced computation time than is possible with standard simulation approaches.
TU‐AB‐BRD‐04: Development of Quality Management Program
Authors: Thomadsen B.
Published: June 2015

Current quality assurance and quality management guidelines provided by various professional organizations are prescriptive in nature, focusing principally on performance characteristics of planning and delivery devices. However, published analyses of events in radiation therapy show that most events are often caused by flaws in clinical processes rather than by device failures. This suggests the need for the development of a quality management program that is based on integrated approaches to process and equipment quality assurance. Industrial engineers have developed various risk assessment tools that are used to identify and eliminate potential failures from a system or a process before a failure impacts a customer. These tools include, but are not limited to, process mapping, failure modes and effects analysis, fault tree analysis. Task Group 100 of the American Association of Physicists in Medicine has developed these tools and used them to formulate an example risk‐based quality management program for intensity‐modulated radiotherapy. This is a prospective risk assessment approach that analyzes potential error pathways inherent in a clinical process and then ranks them according to relative risk, typically before implementation, followed by the design of a new process or modification of the existing process. Appropriate controls are then put in place to ensure that failures are less likely to occur and, if they do, they will more likely be detected before they propagate through the process, compromising treatment outcome and causing harm to the patient. Such a prospective approach forms the basis of the work of Task Group 100 that has recently been approved by the AAPM. This session will be devoted to a discussion of these tools and practical examples of how these tools can be used in a given radiotherapy clinic to develop a risk based quality management program.

Learning Objectives:

Learn how to design a process map for a radiotherapy process

Learn how to perform failure modes and effects analysis analysis for a given process

Learn what fault trees are all about

Learn how to design a quality management program based upon the information obtained from process mapping, failure modes and effects analysis and fault tree analysis.

Dunscombe: Director, TreatSafely, LLC and Center for the Assessment of Radiological Sciences; Consultant to IAEA and Varian

Thomadsen: President, Center for the Assessment of Radiological Sciences

Palta: Vice President of the Center for the Assessment of Radiological Sciences
TU‐A‐213‐01: MBAs for Medical Physicists: Business School Basics
Authors: Kim D.
Published: June 2015

A broad survey of business schools and their potential to augment the utility of practicing medical physicists will be presented in didactic format. Topics will include: 1) business education vs. medical and scientific education, 2) the MBA degree vs. alternative degrees/certificates, 3) distinguishing features among MBA programs, 4) the MBA admissions process and student experience, 5) a description of the typical MBA curriculum and its relevance to clinical practice, and 6) themes to consider when performing a cost‐benefit analysis of whether or not to enroll in an MBA program.

This session is the first of two consecutive sessions exploring the professional impact of supplementing medical physics credentials with formal business education, and is also part of a larger block of sessions concentrating on the Management, Business and Supervision aspects of Medical Physics.

Learning Objectives:

To consider medical physics credentials within a broader educational and economic context.

To gain a sense of what is taught in business schools and how medical physicists might become more effective with this knowledge.

To discern market segments among MBA students and product differentiation among MBA programs.
TU‐A‐304‐01: Introduction and Workflow of Image‐Guided SBRT
Authors: Salter B.
Published: June 2015

Increased use of SBRT and hypo fractionation in radiation oncology practice has posted a number of challenges to medical physicist, ranging from planning, image‐guided patient setup and on‐treatment monitoring, to quality assurance (QA) and dose delivery. This symposium is designed to provide updated knowledge necessary for the safe and efficient implementation of SBRT in various linac platforms, including the emerging digital linacs equipped with high dose rate FFF beams. Issues related to 4D CT, PET and MRI simulations, 3D/4D CBCT guided patient setup, real‐time image guidance during SBRT dose delivery using gated/un‐gated VMAT or IMRT, and technical advancements in QA of SBRT (in particular, strategies dealing with high dose rate FFF beams) will be addressed. The symposium will help the attendees to gain a comprehensive understanding of the SBRT workflow and facilitate their clinical implementation of the state‐of‐art imaging and planning techniques.

Learning Objectives:

Present background knowledge of SBRT, describe essential requirements for safe implementation of SBRT, and discuss issues specific to SBRT treatment planning and QA.

Update on the use of multi‐dimensional (3D and 4D) and multi‐modality (CT, beam‐level X‐ray imaging, pre‐ and on‐treatment 3D/4D MRI, PET, robotic ultrasound, etc.) for reliable guidance of SBRT.

Provide a comprehensive overview of emerging digital linacs and summarize the key geometric and dosimetric features of the new generation of linacs for substantially improved SBRT.

Discuss treatment planning and quality assurance issues specific to SBRT.

Research grant from Varian Medical Systems
TU‐A‐304‐03: Quality Assurance, Safety, and Other Practical Aspects of SBRT
Authors: Benedict S.
Published: June 2015

Increased use of SBRT and hypo fractionation in radiation oncology practice has posted a number of challenges to medical physicist, ranging from planning, image‐guided patient setup and on‐treatment monitoring, to quality assurance (QA) and dose delivery. This symposium is designed to provide updated knowledge necessary for the safe and efficient implementation of SBRT in various linac platforms, including the emerging digital linacs equipped with high dose rate FFF beams. Issues related to 4D CT, PET and MRI simulations, 3D/4D CBCT guided patient setup, real‐time image guidance during SBRT dose delivery using gated/un‐gated VMAT or IMRT, and technical advancements in QA of SBRT (in particular, strategies dealing with high dose rate FFF beams) will be addressed. The symposium will help the attendees to gain a comprehensive understanding of the SBRT workflow and facilitate their clinical implementation of the state‐of‐art imaging and planning techniques.

Learning Objectives:

Present background knowledge of SBRT, describe essential requirements for safe implementation of SBRT, and discuss issues specific to SBRT treatment planning and QA.

Update on the use of multi‐dimensional (3D and 4D) and multi‐modality (CT, beam‐level X‐ray imaging, pre‐ and on‐treatment 3D/4D MRI, PET, robotic ultrasound, etc.) for reliable guidance of SBRT.

Provide a comprehensive overview of emerging digital linacs and summarize the key geometric and dosimetric features of the new generation of linacs for substantially improved SBRT.

Discuss treatment planning and quality assurance issues specific to SBRT.

Research grant from Varian Medical Systems
TU‐AB‐BRD‐01: Process Mapping
Authors: Palta J.
Published: June 2015

Current quality assurance and quality management guidelines provided by various professional organizations are prescriptive in nature, focusing principally on performance characteristics of planning and delivery devices. However, published analyses of events in radiation therapy show that most events are often caused by flaws in clinical processes rather than by device failures. This suggests the need for the development of a quality management program that is based on integrated approaches to process and equipment quality assurance. Industrial engineers have developed various risk assessment tools that are used to identify and eliminate potential failures from a system or a process before a failure impacts a customer. These tools include, but are not limited to, process mapping, failure modes and effects analysis, fault tree analysis. Task Group 100 of the American Association of Physicists in Medicine has developed these tools and used them to formulate an example risk‐based quality management program for intensity‐modulated radiotherapy. This is a prospective risk assessment approach that analyzes potential error pathways inherent in a clinical process and then ranks them according to relative risk, typically before implementation, followed by the design of a new process or modification of the existing process. Appropriate controls are then put in place to ensure that failures are less likely to occur and, if they do, they will more likely be detected before they propagate through the process, compromising treatment outcome and causing harm to the patient. Such a prospective approach forms the basis of the work of Task Group 100 that has recently been approved by the AAPM. This session will be devoted to a discussion of these tools and practical examples of how these tools can be used in a given radiotherapy clinic to develop a risk based quality management program.

Learning Objectives:

Learn how to design a process map for a radiotherapy process

Learn how to perform failure modes and effects analysis analysis for a given process

Learn what fault trees are all about

Learn how to design a quality management program based upon the information obtained from process mapping, failure modes and effects analysis and fault tree analysis.

Dunscombe: Director, TreatSafely, LLC and Center for the Assessment of Radiological Sciences; Consultant to IAEA and Varian

Thomadsen: President, Center for the Assessment of Radiological Sciences

Palta: Vice President of the Center for the Assessment of Radiological Sciences
TU‐AB‐BRD‐02: Failure Modes and Effects Analysis
Authors: Huq M.
Published: June 2015

Current quality assurance and quality management guidelines provided by various professional organizations are prescriptive in nature, focusing principally on performance characteristics of planning and delivery devices. However, published analyses of events in radiation therapy show that most events are often caused by flaws in clinical processes rather than by device failures. This suggests the need for the development of a quality management program that is based on integrated approaches to process and equipment quality assurance. Industrial engineers have developed various risk assessment tools that are used to identify and eliminate potential failures from a system or a process before a failure impacts a customer. These tools include, but are not limited to, process mapping, failure modes and effects analysis, fault tree analysis. Task Group 100 of the American Association of Physicists in Medicine has developed these tools and used them to formulate an example risk‐based quality management program for intensity‐modulated radiotherapy. This is a prospective risk assessment approach that analyzes potential error pathways inherent in a clinical process and then ranks them according to relative risk, typically before implementation, followed by the design of a new process or modification of the existing process. Appropriate controls are then put in place to ensure that failures are less likely to occur and, if they do, they will more likely be detected before they propagate through the process, compromising treatment outcome and causing harm to the patient. Such a prospective approach forms the basis of the work of Task Group 100 that has recently been approved by the AAPM. This session will be devoted to a discussion of these tools and practical examples of how these tools can be used in a given radiotherapy clinic to develop a risk based quality management program.

Learning Objectives:

Learn how to design a process map for a radiotherapy process

Learn how to perform failure modes and effects analysis analysis for a given process

Learn what fault trees are all about

Learn how to design a quality management program based upon the information obtained from process mapping, failure modes and effects analysis and fault tree analysis.

Dunscombe: Director, TreatSafely, LLC and Center for the Assessment of Radiological Sciences; Consultant to IAEA and Varian

Thomadsen: President, Center for the Assessment of Radiological Sciences

Palta: Vice President of the Center for the Assessment of Radiological Sciences
TU‐AB‐BRB‐00: New Methods to Ensure Target Coverage
Authors: Siebers Jeffrey
Published: June 2015

The accepted clinical method to accommodate targeting uncertainties inherent in fractionated external beam radiation therapy is to utilize GTV‐to‐CTV and CTV‐to‐PTV margins during the planning process to design a PTV‐conformal static dose distribution on the planning image set. Ideally, margins are selected to ensure a high (e.g. >95%) target coverage probability (CP) in spite of inherent inter‐ and intra‐fractional positional variations, tissue motions, and initial contouring uncertainties.

Robust optimization techniques, also known as probabilistic treatment planning techniques, explicitly incorporate the dosimetric consequences of targeting uncertainties by including CP evaluation into the planning optimization process along with coverage‐based planning objectives. The treatment planner no longer needs to use PTV and/or PRV margins; instead robust optimization utilizes probability distributions of the underlying uncertainties in conjunction with CP‐evaluation for the underlying CTVs and OARs to design an optimal treated volume.

This symposium will describe CP‐evaluation methods as well as various robust planning techniques including use of probability‐weighted dose distributions, probability‐weighted objective functions, and coverage optimized planning. Methods to compute and display the effect of uncertainties on dose distributions will be presented. The use of robust planning to accommodate inter‐fractional setup uncertainties, organ deformation, and contouring uncertainties will be examined as will its use to accommodate intra‐fractional organ motion. Clinical examples will be used to inter‐compare robust and margin‐based planning, highlighting advantages of robust‐plans in terms of target and normal tissue coverage. Robust‐planning limitations as uncertainties approach zero and as the number of treatment fractions becomes small will be presented, as well as the factors limiting clinical implementation of robust planning.

Learning Objectives:

To understand robust‐planning as a clinical alternative to using margin‐based planning.

To understand conceptual differences between uncertainty and predictable motion.

To understand fundamental limitations of the PTV concept that probabilistic planning can overcome.

To understand the major contributing factors to target and normal tissue coverage probability.

To understand the similarities and differences of various robust planning techniques

To understand the benefits and limitations of robust planning techniques
TU‐B‐210‐02: MRg HIFU ‐ Advanced Approaches for Ablation and Hyperthermia
Authors: Moonen C.
Published: June 2015

MR guided focused ultrasound (MRgFUS), or alternatively high‐intensity focused ultrasound (MRgHIFU), is approved for thermal ablative treatment of uterine fibroids and pain palliation in bone metastases. Ablation of malignant tumors is under active investigation in sites such as breast, prostate, brain, liver, kidney, pancreas, and soft tissue. Hyperthermia therapy with MRgFUS is also feasible, and may be used in conjunction with radiotherapy and for local targeted drug delivery. MRI allows in situ target definition and provides continuous temperature monitoring and subsequent thermal dose mapping during HIFU. Although MRgHIFU can be very precise, treatment of mobile organs is challenging and advanced techniques are required because of artifacts in MR temperature mapping, the need for intercostal firing, and need for gated HIFU or tracking of the lesion in real time. The first invited talk, “MR guided Focused Ultrasound Treatment of Tumors in Bone and Soft Tissue”, will summarize the treatment protocol and review results from treatment of bone tumors. In addition, efforts to extend this technology to treat both benign and malignant soft tissue tumors of the extremities will be presented. The second invited talk, “MRI guided High Intensity Focused Ultrasound – Advanced Approaches for Ablation and Hyperthermia”, will provide an overview of techniques that are in or near clinical trials for thermal ablation and hyperthermia, with an emphasis of applications in abdominal organs and breast, including methods for MRTI and tracking targets in moving organs.

Learning Objectives:

Learn background on devices and techniques for MR guided HIFU for cancer therapy

Understand issues and current status of clinical MRg HIFU

Understand strategies for compensating for organ movement during MRgHIFU

Understand strategies for strategies for delivering hyperthermia with MRgHIFU

CM ‐ research collaboration with Philips
TU‐CD‐213‐01: Communication, Negotiation, and Persuasion: Approaches for Better Results
Authors: Johnson J.
Published: June 2015

As part of the AAPM's Scope of Practice, medical physicists are expected to collaborate effectively with practioners and allied health care providers. Interpersonal skills such as communication, negotiation and persuasion are vital for successful collaboration to achieve shared goals. This session will provide some theoretical background of these interpersonal skills as well as specific techniques and practical tools to influence others. Applications of these interpersonal skills for administrative and human resource management purposes vital to medical physicists will be shared. Session attendees will gain knowledge and tools to help them effectively collaborate with administrative and physician leaders in areas such as capital and human resource selection, prioritization, and implementation. Participants will hear methods of how to articulate their goals and to understand the goals of administration, helping ensure alignment of purpose.

Session speakers will present one of the topics: equipment selection, budget creation, contracts, and program‐related policy development. Specifics may include designing a business case in language that administrators understand, calculating the prioritization of budget requests, and influencing policies for safe and effective care. Human resource topics may include staffing justification, recruitment for fit, employment contracts, and benefits. Speakers will provide examples in both radiation therapy and diagnostic imaging departments and will share experiences and outcomes of their approaches for better results.

Learning Objectives:

After this course attendees will be better able to

Understand the shared goal between administrative and physicist leadership.

Articulate the “why” of the technical or human resource need.

Utilize communication, negotiation and persuasion tools to improve collaboration.
TU‐CD‐304‐02: Planning and Delivery of Fully Dynamic Trajectory Modulated Arc Therapy On Pituitary Adenoma: Dosimetric Advantages Over Conventional Volumetric Modulated Arc Therapy
Authors: Liang J , Hristov D , Otto K , Kim S
Published: June 2015

Purpose:

To assess the potential benefit of trajectory modulated arc therapy (TMAT) for treatments of small benign intracranial tumor, pituitary adenoma.

Methods:

A TMAT planning platform that incorporates complex source motion trajectory involving synchronized gantry rotation with translational and rotational couch movement was used for the study. The platform couples an interactive trajectory generation tool with a VMAT algorithm that performs multi‐resolution, progressive sampling MLC optimization on a user‐designed trajectory. A continuous couch rotation of 160° angular span with ±20° mini gantry arcs was used to emulate a non‐coplanar horizontal arc‐like trajectory. Compared to conventional non‐coplanar gantry arcs (60°‐100° gantry rotation with couch kicks), TMAT limited the unnecessary low to medium dose spread in the anterior and posterior directions, where primary OARs (e.g., brainstem, optic chiasm, optic nerves, and lens) are in close proximity to the targeted pituitary tumor volume. For 5 standard fractionation pituitary adenoma cases (50.4Gy/28fractions), TMAT and non‐coplanar VMAT plans were generated and compared under equivalent objectives/constraints. TMAT delivery was implemented and demonstrated on Varian TrueBeam via XML scripts.

Results:

Both techniques showed good target coverage while OARs were able to meet the constraints on QUANTEC guidelines. Notably, TMAT decreased the dose deposition in the anterior‐to‐posterior direction surrounding PTV. TMAT significantly reduced the mean doses on brainstem, optic nerves, eyes and lens by 47.29%±13.17%, 28.51%±8.68%, 80.82%±8.71% and 65.38%±19.99% compared with VMAT, all p≤0.01. Percentage reductions of maximum point dose in eyes and lens were 75.68%±10.30% and 70.72%±18.62% respectively for TMAT versus VMAT, all p≤0.01. A representative isocentric TMAT pituitary plan was delivered via an XML script with 200 control points and 282 MUs.

Conclusion:

Deliverable TMAT plans were achieved in developer mode in TrueBeam. TMAT was shown to be superior for pituitary adenoma irradiation in terms of OARs sparing.
TU‐CD‐304‐04: Scanning Field Total Body Irradiation Using Dynamic Arc with Variable Dose Rate and Gantry Speed
Authors: Yi B , Xu H , Mutaf Y , Prado K
Published: June 2015

Purpose:

Enable a scanning field total body irradiation (TBI) technique, using dynamic arcs, which is biologically equivalent to a moving couch TBI.

Methods:

Patient is treated slightly above the floor and the treatment field scans across the patient by a moving gantry. MLC positions change during gantry motion to keep same field opening at the level of the treatment plane (170 cm). This is done to mimic the same geometry as the moving couch TBI technique which has been used in our institution for over 10 years. The dose rate and the gantry speed are determined considering a constant speed of the moving field, variations in SSD and slanted depths resulting from oblique gantry angles. An Eclipse (Varian) planning system is commissioned to accommodate the extended SSD. The dosimetric foundations of the technique have been thoroughly investigated using phantom measurements.

Results:

Dose uniformity better than 2% across 180 cm length at 10cm depth is achieved by moving the gantry from −55 to +55 deg. Treatment range can be extended by increasing gantry range. No device such as a gravity‐oriented compensator is needed to achieve a uniform dose. It is feasible to modify the dose distribution by adjusting the dose rate at each gantry angle to compensate for body thickness differences. Total treatment time for 2 Gy AP/PA fields is 40–50 minutes excluding patient set up time, at the machine dose rate of 100 MU/min.

Conclusion:

This novel yet transportable moving field technique enables TBI treatment in a small treatment room with less program development preparation than other techniques. Treatment length can be extended per need, and. MLC‐based thickness compensation and partial lung blocking are also possible.
TU‐CD‐304‐05: 4Ï€ Non‐Coplanar Radiotherapy: From Mathematical Modeling to Clinical Implementation
Authors: Yu V , Nguyen D , Tran A , Ruan D , Cao M , Kaprealian T , Kupelian P , Low D , Sheng K
Published: June 2015

Purpose:

To develop and clinically implement 4π radiotherapy, an inverse optimization platform that maximally utilizes non‐coplanar intensity modulated radiotherapy (IMRT) beams to significantly improve critical organ sparing.

Methods:

A 3D scanner was used to digitize the human and phantom subject surfaces, which were positioned in the computer assisted design (CAD) model of a TrueBeam machine to create a virtual geometrical model, based on which, the feasible beam space was calculated for different tumor locations. Beamlets were computed for all feasible beams using convolution/superposition. A column generation algorithm was employed to optimize patient specific beam orientations and fluence maps. Optimal routing through all selected beams were calculated by a level set method. The resultant plans were converted to XML files and delivered to phantoms in the TrueBeam developer mode. Finally, 4π plans were recomputed in Eclipse and manually delivered to recurrent GBM patients.

Results:

Compared to IMRT utilizing manually selected beams and volumetric modulated arc therapy plans, markedly improved dosimetry was observed using 4π for the brain, head and neck, liver, lung, and prostate patients. The improvements were due to significantly improved conformality and reduced high dose spillage to organs mediolateral to the PTV. The virtual geometrical model was experimentally validated. Safety margins with 99.9% confidence in collision avoidance were included to the model based model accuracy estimates determined via 300 physical machine to phantom distance measurements. Automated delivery in the developer mode was completed in 10 minutes and collision free. Manual 4 π treatment on the GBM cases resulted in significant brainstem sparing and took 35–45 minutes including multiple images, which showed submillimeter cranial intrafractional motion.

Conclusion:

The mathematical modeling utilized in 4π is accurate to create and guide highly complex non‐coplanar IMRT treatments that consistently and significantly outperform human‐operator‐created plans. Deliverability of such plans is clinically demonstrated.

This work is funded by Varian Medical Systems and the NSF Graduate Research Fellowship DGE‐1144087.
TU‐CD‐BRD‐00: Incident Learning / RO‐ILS
Authors: Ezzell Gary
Published: June 2015

It has long been standard practice in radiation oncology to report internally when a patient's treatment has not gone as planned and to report events to regulatory agencies when legally required. Most potential errors are caught early and never affect the patient. Quality assurance steps routinely prevent errors from reaching the patient, and these “near misses” are much more frequent than treatment errors. A growing number of radiation oncology facilities have implemented incident learning systems to report and analyze both errors and near misses. Using the term “incident learning” instead of “event reporting” emphasizes the need to use these experiences to change the practice and make future errors less likely and promote an educational, non‐punitive environment.

There are challenges in making such a system practical and effective. Speakers from institutions of different sizes and practice environments will share their experiences on how to make such a system work and what benefits their clinics have accrued. Questions that will be addressed include:

How to create a system that is easy for front line staff to access

How to motivate staff to report

How to promote the system as positive and educational and not punitive or demeaning

How to organize the team for reviewing and responding to reports

How to prioritize which reports to discuss in depth

How not to dismiss the rest

How to identify underlying causes

How to design corrective actions and implement change

How to develop useful statistics and analysis tools

How to coordinate a departmental system with a larger risk management system

How to do this without a dedicated quality manager

Some speakers’ experience is with in‐house systems and some will share experience with the AAPM/ASTRO national Radiation Oncology Incident Learning System (RO‐ILS). Reports intended to be of value nationally need to be comprehensible to outsiders; examples of useful reports will be shown.

There will be ample time set aside for audience members to contribute to the discussion.

Learning Objectives:

Learn how to promote the use of an incident learning system in a clinic.

Learn how to convert “event reporting” into “incident learning”.

See examples of practice changes that have come out of learning systems.

Learn how the RO‐ILS system can be used as a primary internal learning system.

Learn how to create succinct, meaningful reports useful to outside readers.

Gary Ezzell chairs the AAPM committee overseeing RO‐ILS and has received an honorarium from ASTRO for working on the committee reviewing RO‐ILS reports.

Derek Brown is a director of TreatSafely.org.

Brett Miller has previously received travel expenses and an honorarium from Varian.

Phillip Beron has nothing to report.
TU‐CD‐BRD‐03: UCSD Experience, with Focus On Implementing Change
Authors: Brown D.
Published: June 2015

It has long been standard practice in radiation oncology to report internally when a patient's treatment has not gone as planned and to report events to regulatory agencies when legally required. Most potential errors are caught early and never affect the patient. Quality assurance steps routinely prevent errors from reaching the patient, and these “near misses” are much more frequent than treatment errors. A growing number of radiation oncology facilities have implemented incident learning systems to report and analyze both errors and near misses. Using the term “incident learning” instead of “event reporting” emphasizes the need to use these experiences to change the practice and make future errors less likely and promote an educational, non‐punitive environment.

There are challenges in making such a system practical and effective. Speakers from institutions of different sizes and practice environments will share their experiences on how to make such a system work and what benefits their clinics have accrued. Questions that will be addressed include:

How to create a system that is easy for front line staff to access

How to motivate staff to report

How to promote the system as positive and educational and not punitive or demeaning

How to organize the team for reviewing and responding to reports

How to prioritize which reports to discuss in depth

How not to dismiss the rest

How to identify underlying causes

How to design corrective actions and implement change

How to develop useful statistics and analysis tools

How to coordinate a departmental system with a larger risk management system

How to do this without a dedicated quality manager

Some speakers’ experience is with in‐house systems and some will share experience with the AAPM/ASTRO national Radiation Oncology Incident Learning System (RO‐ILS). Reports intended to be of value nationally need to be comprehensible to outsiders; examples of useful reports will be shown.

There will be ample time set aside for audience members to contribute to the discussion.

Learning Objectives:

Learn how to promote the use of an incident learning system in a clinic.

Learn how to convert “event reporting” into “incident learning”.

See examples of practice changes that have come out of learning systems.

Learn how the RO‐ILS system can be used as a primary internal learning system.

Learn how to create succinct, meaningful reports useful to outside readers.

Gary Ezzell chairs the AAPM committee overseeing RO‐ILS and has received an honorarium from ASTRO for working on the committee reviewing RO‐ILS reports.

Derek Brown is a director of TreatSafely.org.

Brett Miller has previously received travel expenses and an honorarium from Varian.

Phillip Beron has nothing to report.
TU‐CD‐BRB‐02: BEST IN PHYSICS (JOINT IMAGING‐THERAPY): Identification of Molecular Phenotypes by Integrating Radiomics and Genomics
Authors: Grossmann P , Grove O , El‐Hachem N , Velazquez E Rios , Parmar C , Leijenaar R , Haibe‐Kains B , Lambin P , Gillies R , Aerts H
Published: June 2015

Purpose:

To uncover the mechanistic connections between radiomic features, molecular pathways, and clinical outcomes, to develop radiomic based predictors of pathway activation states in individual patients, and to assess whether combining radiomic with clinical and genomic data improves prognostication.

Methods:

We analyzed two independent lung cancer cohorts totaling 351 patients, for whom diagnostic computed tomography (CT) scans, gene‐expression profiles, and clinical outcomes were available. The tumor phenotype was characterized based on 636 radiomic features describing tumor intensity, texture, shape and size. We performed an integrative analysis by developing and independently validating association modules of coherently expressed radiomic features and molecular pathways. These modules were statistically tested for significant associations to overall survival (OS), TNM stage, and pathologic histology.

Results:

We identified thirteen radiomic‐pathway association modules (p < 0.05), the most prominent of which were associated with the immune system, p53 pathway, and other pathways involved in cell cycle regulation. Eleven modules were significantly associated with clinical outcomes (p < 0.05). Strong predictive power for pathway activation states in individual patients was observed using radiomics; the strongest per module predictions ranged from an intra‐tumor heterogeneity feature predicting RNA III polymerase transcription (AUC 0.62, p = 0.03), to a tumor intensity dispersion feature predicting pyruvate metabolism and citric acid TCA cycle (AUC 0.72, p < 10−⁶). Stepwise combinations of radiomic data with clinical outcomes and gene expression profiles resulted in consistent increases of prognostic power to predict OS (concordance index max = 0.73, p < 10−9).

Conclusion:

This study demonstrates that radiomic approaches permit a non‐invasive assessment of molecular and clinical characteristics of tumors, and therefore have the unprecedented potential to cost‐effectively advance clinical decision‐making using routinely acquired, standard‐of‐care imaging data. We show that prognostic value complementary to clinical and genomic information can be obtained by radiomic strategies.
TU‐CD‐BRB‐03: Radiomics Investigation in the Distinction Between in Situ and Invasive Breast Cancers
Authors: Drukker K , Schram J , Burda S , Li H , Lan L , Giger M
Published: June 2015

Purpose:

To determine the capability of machine learning and computer‐extracted image phenotypes to distinguish between ductal carcinoma in situ and invasive breast cancers, which may facilitate timely assessment of disease prognosis and treatment.

Methods:

This study used a HIPAA compliant data set of 248 breast dynamic contrast‐enhanced MR images of breast cancers collected under IRB‐approved protocols (58 in situ and 190 invasive cancers). After automated 3D tumor segmentation, 38 image phenotypes were computer‐extracted, describing tumor size, shape, morphology, enhancement texture, variance kinetics, and kinetic curve characteristics. A random forest classifier was used in a leave‐one‐case‐out training/testing paradigm for the distinction between in situ and invasive breast cancer. Performance for this task was assessed using the area under the receiver operating characteristic curve (AUC). The random forest classifier also automatically determined the relevance of all 38 phenotypes to the task at hand.

Results:

The random forest classifier obtained an AUC of 0.90 (95% confidence interval [0.86;0.95]) in the task of distinguishing between in situ and invasive breast cancers. Phenotypes most important here were shape (sphericity), enhancement texture (variance, difference variance, heterogeneity, contrast), and kinetic curve characteristics (normalized total rate variance, washout rate, time to peak). Phenotypes least effective were size (maximum diameter, surface area), morphology (margin gradient, margin sharpness), variance kinetics, and a kinetic curve characteristic (maximum enhancement).

Conclusion:

We obtained promising results in automated MR image‐based assessment of breast tumor invasiveness. In the current era of personalized medicine such analysis may positively impact patient care by identifying which breast tumors require timely work‐up because of invasive components, and which findings could potentially be monitored without aggressive treatment. The image‐based phenotypes that emerged in this study are markedly different from those relevant to the distinction between benign and malignant breast tumors, where mainly size and morphology influenced diagnostic decision making.

Funding: NIH S10 RR021039, University of Chicago Dean Bridge Fund. and Carole Segal. COI: M.L.G. is a stockholder in R2 Technology/Hologic and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi and Toshiba. MLG is a cofounder of and stockholder in Quantitative Insights.