Literature DB >> 29485048

Improving Quality and Consistency in NRG Oncology Radiation Therapy Oncology Group 0631 for Spine Radiosurgery via Knowledge-Based Planning.

Kelly C Younge1, Robin B Marsh2, Dawn Owen2, Huaizhi Geng3, Ying Xiao3, Daniel E Spratt2, Joseph Foy2, Krithika Suresh2, Q Jackie Wu4, Fang-Fang Yin4, Samuel Ryu5, Martha M Matuszak2.   

Abstract

PURPOSE: To use knowledge-based planning (KBP) as a method of producing high-quality, consistent, protocol-compliant treatment plans in a complex setting of spine stereotactic body radiation therapy on NRG Oncology Radiation Therapy Oncology Group (RTOG) 0631. METHODS AND MATERIALS: An internally developed KBP model was applied to an external validation cohort of 22 anonymized cases submitted under NRG Oncology RTOG 0631. The original and KBP plans were compared via their protocol compliance, target conformity and gradient index, dose to critical structures, and dose to surrounding normal tissues.
RESULTS: The KBP model generated plans meeting all protocol objectives in a single optimization when tested on both internal and protocol-submitted NRG Oncology RTOG 0631 cases. Two submitted plans that were considered to have a protocol-unacceptable deviation were made protocol compliant through the use of the model. There were no statistically significant differences in protocol spinal cord metrics (D10% and D0.03cc) between the manually optimized plans and the KBP plans. The volume of planning target volume receiving prescription dose increased from 93.3% ± 3.2% to 98.3% ± 1.4% (P = .01) when using KBP. High-dose spillage to surrounding normal tissues (V105%) showed no significant differences (2.1 ± 7.3 cm3 for manual plans to 1.8 ± 0.6 cm3 with KBP), and dosimetric outliers with large amounts of spillage were eliminated through the use of KBP. Knowledge-based planning plans were also found to be significantly more consistent in several metrics, including target coverage and high dose outside of the target.
CONCLUSION: Incorporation of KBP models into the clinical trial setting may have a profound impact on the quality of trial results, owing to the increase in consistency and standardization of planning, especially for treatment sites or techniques that are nonstandard.
Copyright © 2017 Elsevier Inc. All rights reserved.

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Year:  2018        PMID: 29485048      PMCID: PMC5915303          DOI: 10.1016/j.ijrobp.2017.12.276

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   7.038


  25 in total

Review 1.  Stereotactic body radiation for the spine: a review.

Authors:  Sheema Chawla; Michael C Schell; Michael T Milano
Journal:  Am J Clin Oncol       Date:  2013-12       Impact factor: 2.339

2.  An analysis of knowledge-based planning for stereotactic body radiation therapy of the spine.

Authors:  Joseph J Foy; Robin Marsh; Randall K Ten Haken; Kelly C Younge; Matthew Schipper; Yilun Sun; Dawn Owen; Martha M Matuszak
Journal:  Pract Radiat Oncol       Date:  2017-03-02

Review 3.  Radiotherapy protocol deviations and clinical outcomes: a meta-analysis of cooperative group clinical trials.

Authors:  Nitin Ohri; Xinglei Shen; Adam P Dicker; Laura A Doyle; Amy S Harrison; Timothy N Showalter
Journal:  J Natl Cancer Inst       Date:  2013-03-06       Impact factor: 13.506

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.  On the pre-clinical validation of a commercial model-based optimisation engine: application to volumetric modulated arc therapy for patients with lung or prostate cancer.

Authors:  Antonella Fogliata; Francesca Belosi; Alessandro Clivio; Piera Navarria; Giorgia Nicolini; Marta Scorsetti; Eugenio Vanetti; Luca Cozzi
Journal:  Radiother Oncol       Date:  2014-11-21       Impact factor: 6.280

6.  Clinical validation and benchmarking of knowledge-based IMRT and VMAT treatment planning in pelvic anatomy.

Authors:  Mohammad Hussein; Christopher P South; Miriam A Barry; Elizabeth J Adams; Tom J Jordan; Alexandra J Stewart; Andrew Nisbet
Journal:  Radiother Oncol       Date:  2016-07-14       Impact factor: 6.280

Review 7.  Contemporary treatment with radiosurgery for spine metastasis and spinal cord compression in 2015.

Authors:  Samuel Ryu; Hannah Yoon; Alexander Stessin; Fred Gutman; Arthur Rosiello; Raphael Davis
Journal:  Radiat Oncol J       Date:  2015-03-31

8.  Development and evaluation of a clinical model for lung cancer patients using stereotactic body radiotherapy (SBRT) within a knowledge-based algorithm for treatment planning.

Authors:  Karen Chin Snyder; Jinkoo Kim; Anne Reding; Corey Fraser; James Gordon; Munther Ajlouni; Benjamin Movsas; Indrin J Chetty
Journal:  J Appl Clin Med Phys       Date:  2016-11-08       Impact factor: 2.102

9.  Evaluation of an automated knowledge based treatment planning system for head and neck.

Authors:  Jerome Krayenbuehl; Ian Norton; Gabriela Studer; Matthias Guckenberger
Journal:  Radiat Oncol       Date:  2015-11-10       Impact factor: 3.481

10.  A broad scope knowledge based model for optimization of VMAT in esophageal cancer: validation and assessment of plan quality among different treatment centers.

Authors:  Antonella Fogliata; Giorgia Nicolini; Alessandro Clivio; Eugenio Vanetti; Sarbani Laksar; Angelo Tozzi; Marta Scorsetti; Luca Cozzi
Journal:  Radiat Oncol       Date:  2015-10-31       Impact factor: 3.481

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  11 in total

1.  Artificial Intelligence in Radiation Therapy.

Authors:  Yabo Fu; Hao Zhang; Eric D Morris; Carri K Glide-Hurst; Suraj Pai; Alberto Traverso; Leonard Wee; Ibrahim Hadzic; Per-Ivar Lønne; Chenyang Shen; Tian Liu; Xiaofeng Yang
Journal:  IEEE Trans Radiat Plasma Med Sci       Date:  2021-08-24

2.  Evaluating the utility of knowledge-based planning for clinical trials using the TROG 08.03 post prostatectomy radiation therapy planning data.

Authors:  Kirsten van Gysen; Andrew Kneebone; Andrew Le; Kenny Wu; Annette Haworth; Regina Bromley; George Hruby; James O'Toole; Jeremy Booth; Chris Brown; Maria Pearse; Mark Sidhom; Kirsty Wiltshire; Colin Tang; Thomas Eade
Journal:  Phys Imaging Radiat Oncol       Date:  2022-05-13

3.  Multi-Institutional Validation of a Knowledge-Based Planning Model for Patients Enrolled in RTOG 0617: Implications for Plan Quality Controls in Cooperative Group Trials.

Authors:  James A Kavanaugh; Sarah Holler; Todd A DeWees; Clifford G Robinson; Jeffrey D Bradley; Puneeth Iyengar; Kristin A Higgins; Sasa Mutic; Lindsey A Olsen
Journal:  Pract Radiat Oncol       Date:  2018-12-15

4.  Development and clinical validation of a robust knowledge-based planning model for stereotactic body radiotherapy treatment of centrally located lung tumors.

Authors:  Justin Visak; Ronald C McGarry; Marcus E Randall; Damodar Pokhrel
Journal:  J Appl Clin Med Phys       Date:  2020-12-07       Impact factor: 2.102

5.  An Automated knowledge-based planning routine for stereotactic body radiotherapy of peripheral lung tumors via DCA-based volumetric modulated arc therapy.

Authors:  Justin Visak; Gary Y Ge; Ronald C McGarry; Marcus Randall; Damodar Pokhrel
Journal:  J Appl Clin Med Phys       Date:  2020-12-03       Impact factor: 2.102

6.  Offline Quality Assurance for Intensity Modulated Radiation Therapy Treatment Plans for NRG-HN001 Head and Neck Clinical Trial Using Knowledge-Based Planning.

Authors:  Tawfik Giaddui; Huaizhi Geng; Quan Chen; Nancy Linnemann; Marsha Radden; Nancy Y Lee; Ping Xia; Ying Xiao
Journal:  Adv Radiat Oncol       Date:  2020-05-22

7.  Knowledge-based planning for the radiation therapy treatment plan quality assurance for patients with head and neck cancer.

Authors:  Wenhua Cao; Mary Gronberg; Adenike Olanrewaju; Thomas Whitaker; Karen Hoffman; Carlos Cardenas; Adam Garden; Heath Skinner; Beth Beadle; Laurence Court
Journal:  J Appl Clin Med Phys       Date:  2022-04-30       Impact factor: 2.243

Review 8.  Machine learning applications in radiation oncology.

Authors:  Matthew Field; Nicholas Hardcastle; Michael Jameson; Noel Aherne; Lois Holloway
Journal:  Phys Imaging Radiat Oncol       Date:  2021-06-24

9.  Rolling out RapidPlan: What we've learnt.

Authors:  Kirsten van Gysen; James O'Toole; Andrew Le; Kenny Wu; Thilo Schuler; Brian Porter; John Kipritidis; John Atyeo; Chris Brown; Thomas Eade
Journal:  J Med Radiat Sci       Date:  2020-09-03

10.  Personalising treatment plan quality review with knowledge-based planning in the TROG 15.03 trial for stereotactic ablative body radiotherapy in primary kidney cancer.

Authors:  Nicholas Hardcastle; Olivia Cook; Xenia Ray; Alisha Moore; Kevin L Moore; David Pryor; Alana Rossi; Farshad Foroudi; Tomas Kron; Shankar Siva
Journal:  Radiat Oncol       Date:  2021-08-03       Impact factor: 3.481

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