scholarly journals Handbook of Treatment Planning in Radiation Oncology.

2012 ◽  
Vol 39 (4) ◽  
pp. 2315-2315
Author(s):  
Chengyu Shi
Keyword(s):  
Treatment Planning ◽  
Radiation Oncology

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 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.

Whiteboard patient tracking system improves radiation oncology treatment planning workflow

2019 ◽  
Vol 8 (2) ◽  
pp. 177-183
Author(s):  
Christopher Freese ◽  
Neil Forster ◽  
Brittany Prater ◽  
Meredith Amlung ◽  
Michael Lamba ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Radiation Oncology ◽  
Tracking System ◽  
Patient Tracking ◽  
Oncology Treatment

Improving the efficiency of a block schedule for treatment planning simulation in a radiation oncology department.

2014 ◽  
Vol 32 (30_suppl) ◽  
pp. 158-158
Author(s):  
Neil E. Martin ◽  
Spyros Potiris ◽  
Robert Mersereau ◽  
Mark J. Mackin ◽  
Barbara A. Jaehn ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Treatment Planning ◽  
Radiation Oncology ◽  
Block Schedule ◽  
Cancer Center ◽  
Time Schedule ◽  
Patient Dissatisfaction ◽  
Schedule Flexibility ◽  
Block Time ◽  
The Mathematical Model

158 Background: The use of a weekly assigned block time schedule to allocate appointment slots to providers for treatment planning simulations caused appointment delays, provider frustration, and a perceived lack of capacity at the Dana-Farber/Brigham and Women's Cancer Center Department of Radiation Oncology. While providing predictability for physician schedules, the slots assigned to individual providers often could not accommodate their patient volume or patient availability. Consequently, providers in need of additional slots had to ‘borrow’ them from other providers. To increase schedule flexibility and reduce the need to ‘borrow’ slots, we proposed opening part of the weekly appointment slots for use by any provider. Methods: Historical data from the scheduling system were obtained to identify the weekly volume of appointment slots used by each provider within and outside their assigned time. Using these data we developed a mathematical model that allowed clinicians to convert a desired number of weekly assigned slots to slots open for use by any provider and examine the resulting effect on the number of ‘borrowed’ slots. The model illustrated the availability and usage of weekly assigned and open slots, as well as the number of ‘borrowed’ slots. Results: In the original schedule, 40% of the weekly appointment slots are ‘borrowed’. The mathematical model revealed that converting 38% of the weekly assigned slots to open slots would completely eliminate the need to ‘borrow’ slots. Conclusions: Data-driven, simple models can address complex problems in clinic operations. A mathematical model that illustrates the effect of opening slots in a block time schedule can help end-users increase efficiency in clinic, as well as eliminate provider and staff frustration, patient dissatisfaction, and delays. [Table: see text]

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