Health and Medical Physics Commons^™

Developing Reference Plans For Evaluating Global Clinical Trials Credentialing And Psqa Systems, Fre'etta M D Brooks, Mohammad Hussein, Jessica Lye, Christopher L Nelson, Nakamura Mitsuhiro, Mallory C Glenn, Patricia Diez, Rushil Patel, Maddison Shaw, Ileana Silvestre Patallo, Miriam Barry, Catharine H Clark, Joerg Lehmann, Stephen F Kry

Faculty, Staff and Student Publications

Purpose: To develop a practical framework for creating a diverse set of validated reference plans (varying in complexity) and implement a workflow to introduce beam modeling, calibration, and delivery errors into the reference cohort to test and compare various dosimetry audit methodologies.

Methods: Sixteen IMRT and VMAT reference plans were created, using RayStation software, for four phantom geometries based on established credentialing cases from participating Global Harmonization Group (GHG) members. These reference plans were first validated in a multi-ion-chamber phantom. Nine dosimetric errors (perturbations) were introduced into the plans by modifying beam model and/or delivery parameters (MLC-offset, MLC-transmission, leaf-tip-width, PDD, …

Dose Prediction Via Deep Learning To Enhance Treatment Planning Of Lung Radiotherapy Including Simultaneous Integrated Boost Techniques, Wenhua Cao, Mary Gronberg, Stephen Bilton, Hana Baroudi, Skylar Gay, Christopher Peeler, Zhongxing Liao, Thomas J Whitaker, Karen Hoffman, Laurence E Court

Faculty, Staff and Student Publications

Background: Recent studies have shown deep learning techniques are able to predict three-dimensional (3D) dose distributions of radiotherapy treatment plans. However, their use in dose prediction for treatments with varied prescription doses including simultaneous integrated boost (SIB), that is, using multiple prescription doses within the same plan, and benefit in improving plan quality should be validated.

Purpose: To investigate the feasibility and potential benefit of using deep learning to predict dose distribution of volumetric modulated arc therapy (VMAT) including SIB techniques and improve treatment planning for patients with lung cancer.

Methods: The dose prediction model was trained with 93 retrospective …

An Automated Treatment Planning Portfolio For Whole Breast Radiotherapy, Hana Baroudi, Leonard Che Fru, Deborah Schofield, Dominique L Roniger, Callistus Nguyen, Donald Hancock, Christine Chung, Beth M Beadle, Kent A Gifford, Tucker Netherton, Joshua S Niedzielski, Adam Melancon, Manickam Muruganandham, Meena Khan, Simona F Shaitelman, Sanjay Shete, Patricia Murina, Daniel Venencia, Sheeba Thengumpallil, Conny Vrieling, Joy Zhang, Melissa P Mitchell, Laurence E Court

Faculty, Staff and Student Publications

Background: Automation in radiotherapy presents a promising solution to the increasing cancer burden and workforce shortages. However, existing automated methods for breast radiotherapy lack a comprehensive, end-to-end solution that meets varying standards of care.

Purpose: This study aims to develop a complete portfolio of automated radiotherapy treatment planning for intact breasts, tailored to individual patient factors, clinical approaches, and available resources.

Methods: We developed five automated conventional treatment approaches and utilized an established RapidPlan model for volumetric arc therapy. These approaches include conventional tangents for whole breast treatment, two variants for supraclavicular nodes (SCLV) treatment with/without axillary nodes, and two …

Robust Automated Method Of Spatial Resolution Measurement In Radiotherapy Ct Simulation Images, Pavel Govyadinov, Rick R Layman, Tucker Netherton, Raymond Mumme, Aaron K Jones, Laurence E Court, Moiz Ahmad

Faculty, Staff and Student Publications

Background: Variation in imaging protocol, patient positioning, and the presence of artifacts can vary image quality in CT images used for radiotherapy planning. Automated methods for spatial resolution (SR) estimation exist but require further investigation and validation for wider adoption.

Purpose: To validated previously existing algorithm for SR estimation and introduce improvements that make it robust to patient positioning, CT protocol, site, and artifacts.

Method: A reference algorithm based on the previous gold standard was recreated and modified to improve robustness. The algorithms were tested on three different datasets: (1) a cylindrical ACR CT QC phantom scanned using a Siemens …

Landmark-Based Auto-Contouring Of Clinical Target Volumes For Radiotherapy Of Nasopharyngeal Cancer, Carlos Sjogreen, Tucker J Netherton, Anna Lee, Moaaz Soliman, Skylar S Gay, Callistus Nguyen, Raymond Mumme, Ivan Vazquez, Dong Joo Rhee, Carlos E Cardenas, Mary K Martel, Beth M Beadle, Laurence Edward Court

Faculty, Staff and Student Publications

Background: The delineation of clinical target volumes (CTVs) for radiotherapy for nasopharyngeal cancer is complex and varies based on the location and extent of disease.

Purpose: The current study aimed to develop an auto-contouring solution following one protocol guidelines (NRG-HN001) that can be adjusted to meet other guidelines, such as RTOG-0225 and the 2018 International guidelines.

Methods: The study used 2-channel 3-dimensional U-Net and nnU-Net framework to auto-contour 27 normal structures in the head and neck (H&N) region that are used to define CTVs in the protocol. To define the CTV-Expansion (CTV1 and CTV2) and CTV-Overall (the outer envelope of …

Evaluating Automatically Generated Normal Tissue Contours For Safe Use In Head And Neck And Cervical Cancer Treatment Planning, Raphael Douglas, Adenike Olanrewaju, Raymond Mumme, Lifei Zhang, Beth M Beadle, Laurence Edward Court

Faculty, Staff and Student Publications

Purpose: Volumetric-modulated arc therapy (VMAT) is a widely accepted treatment method for head and neck (HN) and cervical cancers; however, creating contours and plan optimization for VMAT plans is a time-consuming process. Our group has created an automated treatment planning tool, the Radiation Planning Assistant (RPA), that uses deep learning models to generate organs at risk (OARs), planning structures and automates plan optimization. This study quantitatively evaluates the quality of contours generated by the RPA tool.

Methods: For patients with HN (54) and cervical (39) cancers, we retrospectively generated autoplans using the RPA. Autoplans were generated using deep-learning and RapidPlan …

Optimized Scoring Of End-To-End Dosimetry Audits For Passive Motion Management – A Simulation Study Using The Iroc Thorax Phantom, Alex Burton, Mathieu Gaudreault, Nicholas Hardcastle, Jessica Lye, Sabeena Beveridge, Stephen F Kry, Rick Franich

Faculty, Staff and Student Publications

Dosimetry audits for passive motion management require dynamically-acquired measurements in a moving phantom to be compared to statically calculated planned doses. This study aimed to characterise the relationship between planning and delivery errors, and the measured dose in the Imaging and Radiation Oncology Core (IROC) thorax phantom, to assess different audit scoring approaches. Treatment plans were created using a 4DCT scan of the IROC phantom, equipped with film and thermoluminescent dosimeters (TLDs). Plans were created on the average intensity projection from all bins. Three levels of aperture complexity were explored: dynamic conformal arcs (DCAT), low-, and high-complexity volumetric modulated arcs …

Deep Learning-Based Dose Prediction To Improve The Plan Quality Of Volumetric Modulated Arc Therapy For Gynecologic Cancers, Mary P Gronberg, Anuja Jhingran, Tucker J Netherton, Skylar S Gay, Carlos E Cardenas, Christine Chung, David Fuentes, Clifton D Fuller, Rebecca M Howell, Meena Khan, Tze Yee Lim, Barbara Marquez, Adenike M Olanrewaju, Christine B Peterson, Ivan Vazquez, Thomas J Whitaker, Zachary Wooten, Ming Yang, Laurence E Court

Faculty, Staff and Student Publications

Background: In recent years, deep‐learning models have been used to predict entire three‐dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated.

Purpose: To develop a deep‐learning model to predict high‐quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements.

Methods: A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U‐Net models to predict 3D dose distributions. The models received the …

Intensity Modulated Proton Arc Therapy Via Geometry-Based Energy Selection For Ependymoma, Wenhua Cao, Yupeng Li, Xiaodong Zhang, Falk Poenisch, Pablo Yepes, Narayan Sahoo, David Grosshans, Susan Mcgovern, G Brandon Gunn, Steven J Frank, Xiaorong R Zhu

Faculty, Staff and Student Publications

PURPOSE: We developed and tested a novel method of creating intensity modulated proton arc therapy (IMPAT) plans that uses computing resources similar to those for regular intensity-modulated proton therapy (IMPT) plans and may offer a dosimetric benefit for patients with ependymoma or similar tumor geometries.

METHODS: Our IMPAT planning method consists of a geometry-based energy selection step with major scanning spot contributions as inputs computed using ray-tracing and single-Gaussian approximation of lateral spot profiles. Based on the geometric relation of scanning spots and dose voxels, our energy selection module selects a minimum set of energy layers at each gantry angle …

A Virtual Audit System For Intensity-Modulated Radiation Therapy Credentialing In Japan Clinical Oncology Group Clinical Trials: A Pilot Study, Mitsuhiro Nakamura, Dejun Zhou, Toshiyuki Minemura, Satoshi Kito, Hiroyuki Okamoto, Naoki Tohyama, Masahiko Kurooka, Yu Kumazaki, Masayori Ishikawa, Catharine H Clark, Elizabeth Miles, Joerg Lehmann, Nicolaus Andratschke, Stephen Kry, Satoshi Ishikura, Takashi Mizowaki, Teiji Nishio

Faculty, Staff and Student Publications

Purpose: The Medical Physics Working Group of the Radiation Therapy Study Group at the Japan Clinical Oncology Group is currently developing a virtual audit system for intensity-modulated radiation therapy dosimetry credentialing. The target dosimeters include films and array detectors, such as ArcCHECK (Sun Nuclear Corporation, Melbourne, Florida, USA) and Delta4 (ScandiDos, Uppsala, Sweden). This pilot study investigated the feasibility of our virtual audit system using previously acquired data.

Methods: We analyzed 46 films (32 and 14 in the axial and coronal planes, respectively) from 29 institutions. Global gamma analysis between measured and planned dose distributions used the following settings: 3%/3 …

Customizable Landmark-Based Field Aperture Design For Automated Whole-Brain Radiotherapy Treatment Planning, Yao Xiao, Carlos Cardenas, Dong Joo Rhee, Tucker Netherton, Lifei Zhang, Callistus Nguyen, Raphael Douglas, Raymond Mumme, Stephen Skett, Tina Patel, Chris Trauernicht, Caroline Chung, Hannah Simonds, Ajay Aggarwal, Laurence Court

Faculty, Staff and Student Publications

Purpose: To develop and evaluate an automated whole-brain radiotherapy (WBRT) treatment planning pipeline with a deep learning-based auto-contouring and customizable landmark-based field aperture design.

Methods: The pipeline consisted of the following steps: (1) Auto-contour normal structures on computed tomography scans and digitally reconstructed radiographs using deep learning techniques, (2) locate the landmark structures using the beam's-eye-view, (3) generate field apertures based on eight different landmark rules addressing different clinical purposes and physician preferences. Two parallel approaches for generating field apertures were developed for quality control. The performance of the generated field shapes and dose distributions were compared with the original …

Dosimetric Analysis Of Mr-Linac Treatment Plans For Salvage Spine Sbrt Re-Irradiation, Eun Young Han, Debra N Yeboa, Tina M Briere, Jinzhong Yang, He Wang

Faculty, Staff and Student Publications

PURPOSE: We investigated the feasibility of thoracic spine stereotactic body radiotherapy (SBRT) using the Elekta Unity magnetic resonance-guided linear accelerator (MRL) in patients who received prior radiotherapy. We hypothesized that Monaco treatment plans can improve the gross tumor volume minimum dose (GTVmin) with spinal cord preservation and maintain consistent plan quality during daily adaptation.

METHODS: Pinnacle clinical plans for 10 patients who underwent thoracic spine SBRT (after prior radiotherapy) were regenerated in the Monaco treatment planning system for the Elekta Unity MRL using 9 and 13 intensity-modulated radiotherapy (IMRT) beams. Monaco adapt-to-position (ATP) and adapt-to-shape (ATS) workflow plans were generated …

Clinical Acceptability Of Fully Automated External Beam Radiotherapy For Cervical Cancer With Three Different Beam Delivery Techniques, Dong Joo Rhee, Anuja Jhingran, Kai Huang, Tucker J Netherton, Nazia Fakie, Ingrid White, Alicia Sherriff, Carlos E Cardenas, Lifei Zhang, Surendra Prajapati, Stephen F Kry, Beth M Beadle, William Shaw, Frederika O'Reilly, Jeannette Parkes, Hester Burger, Chris Trauernicht, Hannah Simonds, Laurence E Court

Faculty, Staff and Student Publications

Purpose: To fully automate CT-based cervical cancer radiotherapy by automating contouring and planning for three different treatment techniques.

Methods: We automated three different radiotherapy planning techniques for locally advanced cervical cancer: 2D 4-field-box (4-field-box), 3D conformal radiotherapy (3D-CRT), and volumetric modulated arc therapy (VMAT). These auto-planning algorithms were combined with a previously developed auto-contouring system. To improve the quality of the 4-field-box and 3D-CRT plans, we used an in-house, field-in-field (FIF) automation program. Thirty-five plans were generated for each technique on CT scans from multiple institutions and evaluated by five experienced radiation oncologists from three different countries. Every plan was …

Development And Validation Of A Checklist For Use With Automatically Generated Radiotherapy Plans, Kelly A Nealon, Laurence E Court, Raphael J Douglas, Lifei Zhang, Eun Young Han

Faculty, Staff and Student Publications

Purpose: To develop a checklist that improves the rate of error detection during the plan review of automatically generated radiotherapy plans.

Methods: A custom checklist was developed using guidance from American Association of Physicists in Medicine task groups 275 and 315 and the results of a failure modes and effects analysis of the Radiation Planning Assistant (RPA), an automated contouring and treatment planning tool. The preliminary checklist contained 90 review items for each automatically generated plan. In the first study, eight physicists were recruited from our institution who were familiar with the RPA. Each physicist reviewed 10 artificial intelligence-generated resident …

Assessing The Practicality Of Using A Single Knowledge-Based Planning Model For Multiple Linac Vendors, Raphael J Douglas, Adenike Olanrewaju, Lifei Zhang, Beth M Beadle, Laurence E Court

Faculty, Staff and Student Publications

Purpose: Knowledge-based planning (KBP) has been shown to be an effective tool in quality control for intensity-modulated radiation therapy treatment planning and generating high-quality plans. Previous studies have evaluated its ability to create consistent plans across institutions and between planners within the same institution as well as its use as teaching tool for inexperienced planners. This study evaluates whether planning quality is consistent when using a KBP model to plan across different treatment machines.

Materials and methods: This study used a RapidPlan model (Varian Medical Systems) provided by the vendor, to which we added additional planning objectives, maximum dose limits, …

Knowledge-Based Planning For The Radiation Therapy Treatment Plan Quality Assurance For Patients With Head And Neck Cancer, Wenhua Cao, Mary Gronberg, Adenike Olanrewaju, Thomas Whitaker, Karen Hoffman, Carlos Cardenas, Adam Garden, Heath Skinner, Beth Beadle, Laurence Court

Faculty, Staff and Student Publications

This study aimed to investigate the feasibility of using a knowledge-based planning technique to detect poor quality VMAT plans for patients with head and neck cancer. We created two dose-volume histogram (DVH) prediction models using a commercial knowledge-based planning system (RapidPlan, Varian Medical Systems, Palo Alto, CA) from plans generated by manual planning (MP) and automated planning (AP) approaches. DVHs were predicted for evaluation cohort 1 (EC1) of 25 patients and compared with achieved DVHs of MP and AP plans to evaluate prediction accuracy. Additionally, we predicted DVHs for evaluation cohort 2 (EC2) of 25 patients for which we intentionally …

Brain Stereotactic Radiosurgery Using Mr-Guided Online Adaptive Planning For Daily Setup Variation: An End-To-End Test, Eun Young Han, He Wang, Tina Marie Briere, Debra Nana Yeboa, Themistoklis Boursianis, Georgios Kalaitzakis, Evangelos Pappas, Pamela Castillo, Jinzhong Yang

Faculty, Staff and Student Publications

Online magnetic resonance (MR)-guided radiotherapy is expected to benefit brain stereotactic radiosurgery (SRS) due to superior soft tissue contrast and capability of daily adaptive planning. The purpose of this study was to investigate daily adaptive plan quality with setup variations and to perform an end-to-end test for brain SRS with multiple metastases treated with a 1.5-Tesla MR-Linac (MRL). The RTsafe PseudoPatient Prime brain phantom was used with a delineation insert that includes two predefined structures mimicking gadolinium contrast-enhanced brain lesions. Daily adaptive plans were generated using six preset and six random setup variations. Two adaptive plans per daily MR image …