Comprehensive MRI simulation methodology using a dedicated MRI scanner in radiation oncology for external beam radiation treatment planning

2014 ◽  
Vol 42 (1) ◽  
pp. 28-39 ◽  
Author(s):  
Eric S. Paulson ◽  
Beth Erickson ◽  
Chris Schultz ◽  
X. Allen Li
Keyword(s):  
Treatment Planning ◽  
Radiation Oncology ◽  
Radiation Treatment ◽  
External Beam Radiation ◽  
External Beam ◽  
Radiation Treatment Planning ◽  
Beam Radiation ◽  
Simulation Methodology ◽  
Dedicated Mri

Consensus opinion on MRI simulation for external beam radiation treatment planning

2016 ◽  
Vol 121 (2) ◽  
pp. 187-192 ◽  
Author(s):  
Eric S. Paulson ◽  
Sjoerd P.M. Crijns ◽  
Brian M. Keller ◽  
Jihong Wang ◽  
Maria A. Schmidt ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Radiation Treatment ◽  
External Beam Radiation ◽  
External Beam ◽  
Radiation Treatment Planning ◽  
Beam Radiation ◽  
Consensus Opinion

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   

Age and Radiation Response in Glioblastoma Multiforme

Neurosurgery ◽  
2001 ◽  
Vol 49 (6) ◽  
pp. 1288-1298 ◽  
Author(s):  
Fred G. Barker ◽  
Susan M. Chang ◽  
David A. Larson ◽  
Penny K. Sneed ◽  
William M. Wara ◽  
...  
Keyword(s):  
Scale Score ◽  
Radiation Treatment ◽  
External Beam Radiation Therapy ◽  
Radiation Response ◽  
External Beam Radiation ◽  
Karnofsky Performance Scale ◽  
External Beam ◽  
Beam Radiation ◽  
Karnofsky Performance Scale Score ◽  
Performance Scale

ABSTRACT OBJECTIVE Advanced age is a strong predictor of shorter survival in patients with glioblastoma multiforme (GM), especially for those who receive multimodality treatment. Radiographically assessed tumor response to external beam radiation therapy is an important prognostic factor in GM. We hypothesized that older GM patients might have more radioresistant tumors. METHODS We studied radiographically assessed response to external beam radiation treatment (five-level scale) in relation to age and other prognostic factors in a cohort of 301 GM patients treated on two prospective clinical protocols. A total of 223 patients (74%) were assessable for radiographically assessed radiation response. A proportional odds ordinal regression model was used for univariate and multivariate analysis. RESULTS Younger age (P = 0.006), higher Karnofsky Performance Scale score before radiotherapy (P = 0.027), and more extensive surgical resection (P = 0.028) predicted better radiation response in univariate analyses. Results were similar when clinical criteria were used to classify an additional 61 patients without radiographically assessed radiation response (stable versus progressive disease). In multivariate analyses, age and extent of resection were significant independent predictors of radiation response (P < 0.05); Karnofsky Performance Scale score was of borderline significance (P = 0.07). CONCLUSION Older GM patients are less likely to have good responses to postoperative external beam radiation therapy. Karnofsky Performance Scale score before radiation treatment and extent of surgical resection are additional predictors of radiographically assessed radiation response in GM.

Radiation Therapy

2019 ◽  
pp. 734-736
Author(s):  
Sameer R. Keole
Keyword(s):  
Radiation Therapy ◽  
Ionizing Radiation ◽  
Radiation Oncology ◽  
External Beam Radiation Therapy ◽  
External Beam Radiation ◽  
External Beam ◽  
Beam Radiation ◽  
Radiation Fields ◽  
Diagnostic Radiation ◽  
Oncology Treatment

Radiation oncology is the specialty of medicine in which ionizing radiation is used to treat both malignant and benign conditions. The term radiation therapy (RT) is used, in part, as a differentiator from diagnostic radiation. In radiation oncology, treatment is provided with a team-based approach by physicians, nurses, physicists, dosimetrists, and radiation therapists. Dosimetrists perform the initial planning and mapping of the radiation fields. Radiation therapists deliver the treatment with external beam radiation therapy machines.

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.

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