SU-E-E-01: Hands-On Treatment Planning Education for Medical Physics Students and Radiation Oncology Residents

2011 ◽  
Vol 38 (6Part3) ◽  
pp. 3391-3391
Author(s):  
J Smilowitz ◽  
V Gondi ◽  
W Tome
Keyword(s):  
Treatment Planning ◽  
Radiation Oncology ◽  
Medical Physics ◽  
Physics Students ◽  
Planning Education ◽  
Hands On

scholarly journals The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning

Oncology ◽  
2020 ◽  
pp. 1-11
Author(s):  
Tucker J. Netherton ◽  
Carlos E. Cardenas ◽  
Dong Joo Rhee ◽  
Laurence E. Court ◽  
Beth M. Beadle
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Treatment Planning ◽  
Radiation Oncology ◽  
Radiation Treatment ◽  
Medical Physics ◽  
External Beam Radiotherapy ◽  
Radiation Treatment Planning ◽  
Planning Team ◽  
The Future

<b><i>Background:</i></b> The future of artificial intelligence (AI) heralds unprecedented change for the field of radiation oncology. Commercial vendors and academic institutions have created AI tools for radiation oncology, but such tools have not yet been widely adopted into clinical practice. In addition, numerous discussions have prompted careful thoughts about AI’s impact upon the future landscape of radiation oncology: How can we preserve innovation, creativity, and patient safety? When will AI-based tools be widely adopted into the clinic? Will the need for clinical staff be reduced? How will these devices and tools be developed and regulated? <b><i>Summary:</i></b> In this work, we examine how deep learning, a rapidly emerging subset of AI, fits into the broader historical context of advancements made in radiation oncology and medical physics. In addition, we examine a representative set of deep learning-based tools that are being made available for use in external beam radiotherapy treatment planning and how these deep learning-based tools and other AI-based tools will impact members of the radiation treatment planning team. <b><i>Key Messages:</i></b> Compared to past transformative innovations explored in this article, such as the Monte Carlo method or intensity-modulated radiotherapy, the development and adoption of deep learning-based tools is occurring at faster rates and promises to transform practices of the radiation treatment planning team. However, accessibility to these tools will be determined by each clinic’s access to the internet, web-based solutions, or high-performance computing hardware. As seen by the trends exhibited by many technologies, high dependence on new technology can result in harm should the product fail in an unexpected manner, be misused by the operator, or if the mitigation to an expected failure is not adequate. Thus, the need for developers and researchers to rigorously validate deep learning-based tools, for users to understand how to operate tools appropriately, and for professional bodies to develop guidelines for their use and maintenance is essential. Given that members of the radiation treatment planning team perform many tasks that are automatable, the use of deep learning-based tools, in combination with other automated treatment planning tools, may refocus tasks performed by the treatment planning team and may potentially reduce resource-related burdens for clinics with limited resources.

scholarly journals Targeted Needs Assessment of Dosimetry and Treatment Planning Education for United States Radiation Oncology Residents

2019 ◽  
Vol 105 (1) ◽  
pp. S14-S15 ◽  
Author(s):  
S.Y. Wu ◽  
J.M. Schuster ◽  
M.M. Dominello ◽  
J.W. Burmeister ◽  
D.W. Golden ◽  
...  
Keyword(s):  
United States ◽  
Needs Assessment ◽  
Treatment Planning ◽  
Radiation Oncology ◽  
Planning Education

Targeted Needs Assessment of Treatment Planning Education for United States Radiation Oncology Residents

2020 ◽  
Vol 106 (4) ◽  
pp. 677-682 ◽  
Author(s):  
Susan Y. Wu ◽  
Chhipo Sath ◽  
Jessica M. Schuster ◽  
Michael M. Dominello ◽  
Jay W. Burmeister ◽  
...  
Keyword(s):  
United States ◽  
Needs Assessment ◽  
Treatment Planning ◽  
Radiation Oncology ◽  
Planning Education

scholarly journals PLUNC 3D Radiation Treatment Planning System (TPS): An Educational Platform for Medical Physics Students

2018 ◽  
Vol 21 (1) ◽  
pp. 35-42
Author(s):  
Abdus Sattrar Mollah
Keyword(s):  
Treatment Planning ◽  
Radiation Oncology ◽  
Radiation Treatment ◽  
Medical Physics ◽  
Planning System ◽  
External Beam Radiation ◽  
Treatment Planning System ◽  
Target Tissue ◽  
Radiation Treatment Planning ◽  
3D Treatment Planning

Technological innovations with modern planning and treatment techniques have transformed the way of radiation treatment for cancer patients. A tremendous evolution in radiation treatment process occurred in recent years. This allowed the delivery of the desired radiation dose distribution to target tissue, while delivering an acceptable radia­tion dose to the surrounding normal tissues with greater dose gradients and tighter margins. Evolution of the computers and computerized systems enabled the possibility to improve the basic two-dimensional (2D) radiotherapy treatment planning to a more accurate and more visualised three-dimensional (3D) treatment planning systems. Today there is now several commercialized planning system competitors used for external beam radiation therapy. PLUNC was one of the first operating 3D radiation treatment planning (RTP) systems’. This RTP system has been developed in the Department of Radiation Oncology at the University of North Carolina (UNC) since 1985 for research and educational purposes. PLUNC is freely distributed to the field of radiation oncology for research and educational use under special license agreement. In this study, PLUNC 3D treatment planning system has been installed and implemented for research and educational purpose in the field of medical physics. A 3D treatment plan has been created and analyzed in a typical patient CT image for educational demonstration purpose. Based on this analysis, it is concluded that the PLUNC 3D TPS could be successfully used for research and education purposes in M Sc/PhD thesis works of students from medical physics discipline. Bangladesh J. Nuclear Med. 21(1): 35-42, January 2018   

It is essential for Medical Physics Radiation Oncology Residents to acquire substantial treatment planning skills

2020 ◽  
Vol 47 (4) ◽  
pp. 1413-1416
Author(s):  
Muthana Al‐Ghazi ◽  
Colleen DesRosiers ◽  
Gerald White
Keyword(s):  
Treatment Planning ◽  
Radiation Oncology ◽  
Medical Physics

The Solo Practice of Medical Physics in Radiation Oncology

10.37206/80 ◽  
2003 ◽  
Author(s):  
Per H. Halvorsen ◽  
Julie F. Dawson ◽  
Martin W. Fraser ◽  
Geoffrey S. Ibbott ◽  
Bruce R. Thomadsen
Keyword(s):  
Radiation Oncology ◽  
Medical Physics ◽  
Solo Practice

Results of wavelet image compression on CT-based clinical radiation oncology treatment planning

1999 ◽  
Author(s):  
Charles L. Smith ◽  
Wei-Kom Chu ◽  
Randy Wobig ◽  
Hong-Yang Chao ◽  
Charles Enke
Keyword(s):  
Image Compression ◽  
Treatment Planning ◽  
Radiation Oncology ◽  
Oncology Treatment ◽  
Wavelet Image

scholarly journals Development, Implementation, and Initial Participant Feedback of an Online Anatomy and Radiology Contouring Bootcamp in Radiation Oncology

2021 ◽  
Vol 8 ◽  
pp. 238212052110377
Author(s):  
Paige Eansor ◽  
Madeleine E. Norris ◽  
Leah A. D’Souza ◽  
Glenn S. Bauman ◽  
Zahra Kassam ◽  
...  
Keyword(s):  
Learning Environment ◽  
Radiation Oncology ◽  
Medical Physics ◽  
Course Completion ◽  
Online Course ◽  
Design Development ◽  
Subject Knowledge ◽  
Course Content ◽  
Face To Face ◽  
The Creation

BACKGROUND The Anatomy and Radiology Contouring (ARC) Bootcamp was a face-to-face (F2F) course designed to ensure radiation oncology residents were equipped with the knowledge and skillset to use radiation therapy techniques properly. The ARC Bootcamp was proven to be a useful educational intervention for improving learners’ knowledge of anatomy and radiology and contouring ability. An online version of the course was created to increase accessibility to the ARC Bootcamp and provide a flexible, self-paced learning environment. This study aimed to describe the instructional design model used to create the online offering and report participants’ motivation to enroll in the course and the online ARC Bootcamp's strengths and improvement areas. METHODS The creation of the online course followed the analysis, design, development, implementation, and evaluation (ADDIE) framework. The course was structured in a linear progression of locked modules consisting of radiology and contouring lectures, anatomy labs, and integrated evaluations. RESULTS The online course launched on the platform Teachable in November 2019, and by January 2021, 140 participants had enrolled in the course, with 27 participants completing all course components. The course had broad geographic participation with learners from 19 different countries. Of the participants enrolled, 34% were female, and most were radiation oncology residents (56%), followed by other programs (24%), such as medical physics residents or medical students. The primary motivator for participants to enroll was to improve their subject knowledge/skill (44%). The most common strength identified by participants was the course's quality (41%), and the most common improvement area was to incorporate more course content (41%). CONCLUSIONS The creation of the online ARC Bootcamp using the ADDIE framework was feasible. The course is accessible to diverse geographic regions and programs and provides a flexible learning environment; however, the course completion rate was low. Participants’ feedback regarding their experiences will inform future offerings of the online course.

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