In the future, disruptive innovation in radiation oncology technology will be initiated mostly by entrepreneurs

2019 ◽  
Vol 46 (5) ◽  
pp. 1949-1952
Author(s):  
Cedric X. Yu ◽  
Thomas Bortfeld ◽  
Jing Cai
Keyword(s):  
Radiation Oncology ◽  
Disruptive Innovation ◽  
The Future

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.

Focusing on the “Person” in Personalized Medicine: The Future of Patient-Centered Care in Radiation Oncology

2016 ◽  
Vol 13 (12) ◽  
pp. 1571-1578 ◽  
Author(s):  
Abigail T. Berman ◽  
Seth A. Rosenthal ◽  
Drew Moghanaki ◽  
Kristina D. Woodhouse ◽  
Benjamin Movsas ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Radiation Oncology ◽  
Patient Centered Care ◽  
Patient Centered ◽  
The Future ◽  
Centered Care

scholarly journals Nanotechnology in Radiation Oncology

2014 ◽  
Vol 32 (26) ◽  
pp. 2879-2885 ◽  
Author(s):  
Andrew Z. Wang ◽  
Joel E. Tepper
Keyword(s):  
Small Molecules ◽  
Treatment Planning ◽  
Radiation Oncology ◽  
Clinical Medicine ◽  
Future Directions ◽  
Normal Tissues ◽  
Therapeutic Ratio ◽  
Preferential Accumulation ◽  
The Future

Nanotechnology, the manipulation of matter on atomic and molecular scales, is a relatively new branch of science. It has already made a significant impact on clinical medicine, especially in oncology. Nanomaterial has several characteristics that are ideal for oncology applications, including preferential accumulation in tumors, low distribution in normal tissues, biodistribution, pharmacokinetics, and clearance, that differ from those of small molecules. Because these properties are also well suited for applications in radiation oncology, nanomaterials have been used in many different areas of radiation oncology for imaging and treatment planning, as well as for radiosensitization to improve the therapeutic ratio. In this article, we review the unique properties of nanomaterials that are favorable for oncology applications and examine the various applications of nanotechnology in radiation oncology. We also discuss the future directions of nanotechnology within the context of radiation oncology.

The future of predictive models in radiation oncology: from extensive data mining to reliable modeling of the results

2013 ◽  
Vol 9 (3) ◽  
pp. 311-313 ◽  
Author(s):  
Vincenzo Valentini ◽  
Nicola Dinapoli ◽  
Andrea Damiani
Keyword(s):  
Data Mining ◽  
Predictive Models ◽  
Radiation Oncology ◽  
Extensive Data ◽  
The Future

scholarly journals A VISÃO DE FUTURO POR MEIO DE CENÁRIOS PROSPECTIVOS: UMA FERRAMENTA PARA A ANTECIPAÇÃO DA INOVAÇÃO DISRUPTIVA

2012 ◽  
Vol 4 (2) ◽  
pp. 102-131
Author(s):  
Ivan Domicio Da Silva Souza ◽  
Vania Passarini Takahashi
Keyword(s):  
Strategic Planning ◽  
Disruptive Innovation ◽  
Scenario Planning ◽  
Traditional Methods ◽  
Advantages And Disadvantages ◽  
Turbulent Environments ◽  
The Future ◽  
Future Events ◽  
Simple Activity

Future events are unknown, unexpected and even if forecasts may offer some estimation, there is no way to predict the behavior of unprecedented events. Therefore, looking into the future and drafting a strategy is not a simple activity. All this process is even more fastidious in a period of uncertainties, changes and world crises. However, a method named Scenario Planning may contribute to the formulation of strategies in turbulent environments. In this paper it is reviewed and consolidated the theories and reports in the literature, in order to elucidate the use of prospective scenarios as a tool to anticipate disruptive innovation. In this sense, it is presented and discussed some considerations about the origin of scenarios, the relation between scenarios and strategy, the typologies of scenarios, the tools for scenarios construction, the traditional methods in scenarios development and the advantages and disadvantages of this method. Indeed, scenario planning is a flexible and stimulative method which allows one to identify opportunities for innovation, so as to favor resilient strategic planning and future visioning in threatening environments.

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