Empirical evaluation of workload of the radiation oncology physicist during radiation treatment planning and delivery

2011 ◽  
Author(s):  
Prithima R. Mosaly ◽  
Lukasz M. Mazur ◽  
Marianne Jackson ◽  
Sha X. Chang ◽  
Katharin Deschesne Burkhardt ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Radiation Oncology ◽  
Radiation Treatment ◽  
Empirical Evaluation ◽  
Radiation Treatment Planning

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 A survey of radiation treatment planning peer-review activities in a provincial radiation oncology programme: current practice and future directions

BMJ Open ◽  
2013 ◽  
Vol 3 (7) ◽  
pp. e003241 ◽  
Author(s):  
Michael Brundage ◽  
Sophie Foxcroft ◽  
Tom McGowan ◽  
Eric Gutierrez ◽  
Michael Sharpe ◽  
...  
Keyword(s):  
Peer Review ◽  
Treatment Planning ◽  
Radiation Oncology ◽  
Current Practice ◽  
Radiation Treatment ◽  
Radiation Treatment Planning ◽  
Future Directions

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   

Stereotactic radiation treatment planning and follow-up studies involving fused multimodality imaging

2004 ◽  
Vol 101 (Supplement3) ◽  
pp. 326-333 ◽  
Author(s):  
Klaus D. Hamm ◽  
Gunnar Surber ◽  
Michael Schmücking ◽  
Reinhard E. Wurm ◽  
Rene Aschenbach ◽  
...  
Keyword(s):  
Image Fusion ◽  
Treatment Planning ◽  
Radiation Treatment ◽  
Data Sets ◽  
Slice Thickness ◽  
Radiation Treatment Planning ◽  
Content Type ◽  
Follow Up Studies ◽  
Fine Print

Object. Innovative new software solutions may enable image fusion to produce the desired data superposition for precise target definition and follow-up studies in radiosurgery/stereotactic radiotherapy in patients with intracranial lesions. The aim is to integrate the anatomical and functional information completely into the radiation treatment planning and to achieve an exact comparison for follow-up examinations. Special conditions and advantages of BrainLAB's fully automatic image fusion system are evaluated and described for this purpose. Methods. In 458 patients, the radiation treatment planning and some follow-up studies were performed using an automatic image fusion technique involving the use of different imaging modalities. Each fusion was visually checked and corrected as necessary. The computerized tomography (CT) scans for radiation treatment planning (slice thickness 1.25 mm), as well as stereotactic angiography for arteriovenous malformations, were acquired using head fixation with stereotactic arc or, in the case of stereotactic radiotherapy, with a relocatable stereotactic mask. Different magnetic resonance (MR) imaging sequences (T1, T2, and fluid-attenuated inversion-recovery images) and positron emission tomography (PET) scans were obtained without head fixation. Fusion results and the effects on radiation treatment planning and follow-up studies were analyzed. The precision level of the results of the automatic fusion depended primarily on the image quality, especially the slice thickness and the field homogeneity when using MR images, as well as on patient movement during data acquisition. Fully automated image fusion of different MR, CT, and PET studies was performed for each patient. Only in a few cases was it necessary to correct the fusion manually after visual evaluation. These corrections were minor and did not materially affect treatment planning. High-quality fusion of thin slices of a region of interest with a complete head data set could be performed easily. The target volume for radiation treatment planning could be accurately delineated using multimodal information provided by CT, MR, angiography, and PET studies. The fusion of follow-up image data sets yielded results that could be successfully compared and quantitatively evaluated. Conclusions. Depending on the quality of the originally acquired image, automated image fusion can be a very valuable tool, allowing for fast (∼ 1–2 minute) and precise fusion of all relevant data sets. Fused multimodality imaging improves the target volume definition for radiation treatment planning. High-quality follow-up image data sets should be acquired for image fusion to provide exactly comparable slices and volumetric results that will contribute to quality contol.

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