Literature DB >> 22177879

Quantitative metrics for assessing plan quality.

Kevin L Moore1, R Scott Brame, Daniel A Low, Sasa Mutic.   

Abstract

Despite many studies over the last 3 decades that have attempted to explicitly quantify the decision-making process for radiotherapy treatment plan evaluation, judgments of an individual plan's degree of quality are still largely subjective and can show inter- and intra-practitioner variability even if the clinical treatment goals are the same. Several factors conspire to confound the full quantification of treatment plan quality, including uncertainties in dose response of cancerous and normal tissue, the rapid pace of new technology adoption, and the human component of treatment planning. However, new developments in clinical informatics and automation are lowering the bar for developing and implementing quantitative metrics into the treatment planning process. This review discusses general strategies for using quantitative metrics in the treatment planning process and presents a case study in intensity-modulated radiation therapy planning whereby control was established on a variable system via such techniques.
Copyright © 2012 Elsevier Inc. All rights reserved.

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Year:  2012        PMID: 22177879     DOI: 10.1016/j.semradonc.2011.09.005

Source DB:  PubMed          Journal:  Semin Radiat Oncol        ISSN: 1053-4296            Impact factor:   5.934


  14 in total

1.  SBRT planning for spinal metastasis: indications from a large multicentric study.

Authors:  Marco Esposito; Laura Masi; Margherita Zani; Raffaela Doro; David Fedele; Cristina Garibaldi; Stefania Clemente; Christian Fiandra; Francesca Romana Giglioli; Carmelo Marino; Laura Orsingher; Serenella Russo; Michele Stasi; Lidia Strigari; Elena Villaggi; Pietro Mancosu
Journal:  Strahlenther Onkol       Date:  2018-10-23       Impact factor: 3.621

2.  A novel approach to SBRT patient quality assurance using EPID-based real-time transit dosimetry : A step to QA with in vivo EPID dosimetry.

Authors:  Christos Moustakis; Fatemeh Ebrahimi Tazehmahalleh; Khaled Elsayad; Francis Fezeu; Sergiu Scobioala
Journal:  Strahlenther Onkol       Date:  2020-01-10       Impact factor: 3.621

3.  Intelligent inverse treatment planning via deep reinforcement learning, a proof-of-principle study in high dose-rate brachytherapy for cervical cancer.

Authors:  Chenyang Shen; Yesenia Gonzalez; Peter Klages; Nan Qin; Hyunuk Jung; Liyuan Chen; Dan Nguyen; Steve B Jiang; Xun Jia
Journal:  Phys Med Biol       Date:  2019-05-29       Impact factor: 3.609

4.  Highly Efficient Training, Refinement, and Validation of a Knowledge-based Planning Quality-Control System for Radiation Therapy Clinical Trials.

Authors:  Nan Li; Ruben Carmona; Igor Sirak; Linda Kasaova; David Followill; Jeff Michalski; Walter Bosch; William Straube; Loren K Mell; Kevin L Moore
Journal:  Int J Radiat Oncol Biol Phys       Date:  2016-10-13       Impact factor: 7.038

5.  ORBIT-RT: A Real-Time, Open Platform for Knowledge-Based Quality Control of Radiotherapy Treatment Planning.

Authors:  Brent M Covele; Kartikeya S Puri; Karoline Kallis; James D Murphy; Kevin L Moore
Journal:  JCO Clin Cancer Inform       Date:  2021-01

6.  Pareto Optimal Projection Search (POPS): Automated Radiation Therapy Treatment Planning by Direct Search of the Pareto Surface.

Authors:  Charles Huang; Yong Yang; Neil Panjwani; Stephen Boyd; Lei Xing
Journal:  IEEE Trans Biomed Eng       Date:  2021-09-20       Impact factor: 4.756

7.  A dose-volume histogram based decision-support system for dosimetric comparison of radiotherapy treatment plans.

Authors:  J C L Alfonso; M A Herrero; L Núñez
Journal:  Radiat Oncol       Date:  2015-12-29       Impact factor: 3.481

8.  Automatic planning of head and neck treatment plans.

Authors:  Irene Hazell; Karl Bzdusek; Prashant Kumar; Christian R Hansen; Anders Bertelsen; Jesper G Eriksen; Jørgen Johansen; Carsten Brink
Journal:  J Appl Clin Med Phys       Date:  2016-01-08       Impact factor: 2.102

9.  Quantitative comparison of automatic and manual IMRT optimization for prostate cancer: the benefits of DVH prediction.

Authors:  Yun Yang; Taoran Li; Lunlin Yuan; Yaorong Ge; Fang-Fang Yin; W Robert Lee; Q Jackie Wu
Journal:  J Appl Clin Med Phys       Date:  2015-03-08       Impact factor: 2.102

10.  An Automated Treatment Plan Quality Control Tool for Intensity-Modulated Radiation Therapy Using a Voxel-Weighting Factor-Based Re-Optimization Algorithm.

Authors:  Ting Song; Nan Li; Masoud Zarepisheh; Yongbao Li; Quentin Gautier; Linghong Zhou; Loren Mell; Steve Jiang; Laura Cerviño
Journal:  PLoS One       Date:  2016-03-01       Impact factor: 3.240

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