scholarly journals Physical and Radiobiological Evaluation of Radiotherapy Treatment Plan

10.5772/60846 ◽  
2015 ◽  
Author(s):  
Suk Lee ◽  
Yuan Jie Cao ◽  
Chul Yong Kim
Keyword(s):  
Treatment Plan ◽  
Radiotherapy Treatment

scholarly journals A comparison of two methodologies for radiotherapy treatment plan optimization and QA for clinical trials

2021 ◽  
Vol 22 (10) ◽  
pp. 329-337
Author(s):  
Huaizhi Geng ◽  
Tawfik Giaddui ◽  
Chingyun Cheng ◽  
Haoyu Zhong ◽  
Samuel Ryu ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Treatment Plan ◽  
Plan Optimization ◽  
Treatment Plan Optimization ◽  
Radiotherapy Treatment

scholarly journals Outcomes of Peer Review for Radiotherapy Treatment Plans With Palliative Intent

2018 ◽  
Vol 14 (12) ◽  
pp. e794-e800
Author(s):  
Dina Thompson ◽  
Kimberly Cox ◽  
James Loudon ◽  
Ivan Yeung ◽  
Woodrow Wells
Keyword(s):  
Peer Review ◽  
Treatment Plan ◽  
Palliative Radiotherapy ◽  
Cancer Center ◽  
Palliative Intent ◽  
Treatment Plans ◽  
Important Quality ◽  
Radiation Medicine ◽  
Radiotherapy Treatment

Purpose: Peer review of a proposed treatment plan is increasingly recognized as an important quality activity in radiation medicine. Although peer review has been emphasized in the curative setting, applying peer review for treatment plans that have palliative intent is receiving increased attention. This study reports peer review outcomes for a regional cancer center that applied routine interprofessional peer review as a standard practice for palliative radiotherapy. Methods and Materials: Peer review outcomes for palliative radiotherapy plans were recorded prospectively for patients who began radiotherapy between October 1, 2015, and September 30, 2017. Recommended and implemented changes were recorded. The content of detailed discussions was recorded to gain insight into the complexities of palliative treatment plans considered during peer review. Results: Peer review outcomes were reviewed for 1,413 treatment plans with palliative intent. The proportions of detailed discussions and changes recommended were found to be 139 (9.8%) and 29 (2.1%), respectively. The content of detailed discussions and changes recommended was categorized. Major changes represented 75.9% of recommended changes, of which 84.2% were implemented clinically. Conclusion: Many complexities exist that are specific to palliative radiotherapy. Interprofessional peer review provides a forum for these complexities to be openly discussed and is an important activity to optimize the quality of care for patients with treatment plans that have palliative intent.

Optimization of the radiotherapy treatment plan

1988 ◽  
Vol 27 (2) ◽  
pp. 117-146 ◽  
Author(s):  
A.L. Fymat ◽  
M.A. Greenfield ◽  
S.C. Lo ◽  
D. Findley
Keyword(s):  
Treatment Plan ◽  
Radiotherapy Treatment

Understanding machine learning classifier decisions in automated radiotherapy quality assurance

2021 ◽  
Author(s):  
Yunsheng Chen ◽  
Dionne M Aleman ◽  
Thomas G Purdie ◽  
Chris McIntosh
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Treatment Plan ◽  
Region Of Interest ◽  
Expert Assessment ◽  
Learning Approaches ◽  
Ensure Patient Safety ◽  
Shapley Values ◽  
Clinically Significant ◽  
Radiotherapy Treatment

Abstract The complexity of generating radiotherapy treatments demands a rigorous quality assurance (QA) process to ensure patient safety and to avoid clinically significant errors. Machine learning classifiers have been explored to augment the scope and efficiency of the traditional radiotherapy treatment planning QA process. However, one important gap in relying on classifiers for QA of radiotherapy treatment plans is the lack of understanding behind a specific classifier prediction. We develop explanation methods to understand the decisions of two automated QA classifiers: (1) a region of interest (ROI) segmentation/labeling classifier, and (2) a treatment plan acceptance classifier. For each classifier, a local interpretable model-agnostic explanation (LIME) framework and a novel adaption of team-based Shapley values framework are constructed. We test these methods in datasets for two radiotherapy treatment sites (prostate and breast), and demonstrate the importance of evaluating QA classifiers using interpretable machine learning approaches. We additionally develop a notion of explanation consistency to assess classifier performance. Our explanation method allows for easy visualization and human expert assessment of classifier decisions in radiotherapy QA. Notably, we find that our team-based Shapley approach is more consistent than LIME. The ability to explain and validate automated decision-making is critical in medical treatments. This analysis allows us to conclude that both QA classifiers are moderately trustworthy and can be used to confirm expert decisions, though the current QA classifiers should not be viewed as a replacement for the human QA process.

Knowledge-based radiotherapy treatment planning

2015 ◽  
Author(s):  
◽  
Lindsey Appenzoller Olsen
Keyword(s):  
Quality Control ◽  
Treatment Planning ◽  
Treatment Plan ◽  
Radiotherapy Planning ◽  
Patient Specific ◽  
Plan Quality ◽  
Radiotherapy Treatment Planning ◽  
Knowledge Based ◽  
Knowledge Based Planning ◽  
Radiotherapy Treatment

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Knowledge-based planning (KBP) has become a prominent area of research in radiation oncology in the last five years. The development of KBP aims to address the lack of systematic quality control and plan quality variability in radiotherapy treatment planning by providing achievable, patient-specific optimization objectives derived from a model trained with a cohort of previously treated, site-specific plans. This dissertation intended to develop, evaluate, and implement a knowledge-based planning system to reduce variability and improve radiotherapy treatment plan quality. The project aimed to 1) develop and validate an algorithm to train mathematical models that predict dose-volume histograms for organs at risk in radiotherapy planning, 2) implement the algorithm into a software application in order to transfer the technology into clinical practice, and 3) evaluate the impact of the software system (algorithm + application) on reducing variability and improving radiotherapy treatment plan quality through knowledge transfer. The presented work demonstrates that a KBP model is beneficial to radiotherapy planning. The developed models adequately describe what is dosimetrically achievable for patient specific anatomy and have proven useful in outlier detection for quality control of radiotherapy planning. The KBP paradigm has also demonstrated ability to improve treatment plan quality through benchmarking and transfer of knowledge between institutions.

scholarly journals Feasibility of using glass-bead thermoluminescent dosimeters for radiotherapy treatment plan verification

2015 ◽  
Vol 88 (1055) ◽  
pp. 20140804 ◽  
Author(s):  
Shakardokht M Jafari ◽  
Tom J Jordan ◽  
Gail Distefano ◽  
David A Bradley ◽  
Nicholas M Spyrou ◽  
...  
Keyword(s):  
Glass Bead ◽  
Treatment Plan ◽  
Thermoluminescent Dosimeters ◽  
Radiotherapy Treatment

Radiotherapy

2019 ◽  
pp. 193-204
Author(s):  
Richard Brown ◽  
Paula Horne
Keyword(s):  
Side Effects ◽  
Treatment Plan ◽  
Radiotherapy Planning ◽  
Optimum Treatment ◽  
Recent Advances ◽  
Palliative Setting ◽  
Radiotherapy Treatment

This chapter examines the manner in which radiotherapy is employed to treat cancers and the general principles which govern its use. Radiotherapy can be used in a curative or palliative setting and is frequently combined with surgery or chemotherapy to produce an optimum treatment plan. The underpining radiobiology to treatment fractionation is briefly explored, before the differing modalities by which radiotherapy can be delivered are outlined. The principles of radiotherapy planning are laid out, before recent advances in radiotherapy treatment are also covered. Radiotherapy toxicities and side effects and the management of these are analysed in some detail.

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