Literature DB >> 33640315

Clinical Acceptability of Automated Radiation Treatment Planning for Head and Neck Cancer Using the Radiation Planning Assistant.

Adenike Olanrewaju1, Laurence E Court1, Lifei Zhang1, Komeela Naidoo2, Hester Burger3, Sameera Dalvie3, Julie Wetter3, Jeannette Parkes3, Christoph J Trauernicht2, Rachel E McCarroll1, Carlos Cardenas1, Christine B Peterson4, Kathryn R K Benson5, Monique du Toit2, Ricus van Reenen2, Beth M Beadle6.   

Abstract

PURPOSE: Radiation treatment planning for head and neck cancer is a complex process with much variability; automated treatment planning is a promising option to improve plan quality and efficiency. This study compared radiation plans generated from a fully automated radiation treatment planning system to plans generated manually that had been clinically approved and delivered. METHODS AND MATERIALS: The study cohort consisted of 50 patients treated by a specialized head and neck cancer team at a tertiary care center. An automated radiation treatment planning system, the Radiation Planning Assistant, was used to create autoplans for all patients using their original, approved contours. Common dose-volume histogram (DVH) criteria were used to compare the quality of autoplans to the clinical plans. Fourteen radiation oncologists, each from a different institution, then reviewed and compared the autoplans and clinical plans in a blinded fashion.
RESULTS: Autoplans and clinical plans were very similar with regard to DVH metrics for coverage and critical structure constraints. Physician reviewers found both the clinical plans and autoplans acceptable for use; overall, 78% of the clinical plans and 88% of the autoplans were found to be usable as is (without any edits). When asked to choose which plan would be preferred for approval, 27% of physician reviewers selected the clinical plan, 47% selected the autoplan, 25% said both were equivalent, and 0% said neither. Hence, overall, 72% of physician reviewers believed the autoplan or either the clinical or autoplan was preferable.
CONCLUSIONS: Automated radiation treatment planning creates consistent, clinically acceptable treatment plans that meet DVH criteria and are found to be appropriate on physician review.
Copyright © 2021 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

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Year:  2021        PMID: 33640315      PMCID: PMC9272530          DOI: 10.1016/j.prro.2020.12.003

Source DB:  PubMed          Journal:  Pract Radiat Oncol        ISSN: 1879-8500


  32 in total

Review 1.  Selection and delineation of target volumes in head and neck tumors: beyond ICRU definition.

Authors:  Vincent Grégoire; Jean-François Daisne; Xavier Geets; Peter Levendag
Journal:  Rays       Date:  2003 Jul-Sep

2.  Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System.

Authors:  Laurence E Court; Kelly Kisling; Rachel McCarroll; Lifei Zhang; Jinzhong Yang; Hannah Simonds; Monique du Toit; Chris Trauernicht; Hester Burger; Jeannette Parkes; Mike Mejia; Maureen Bojador; Peter Balter; Daniela Branco; Angela Steinmann; Garrett Baltz; Skylar Gay; Brian Anderson; Carlos Cardenas; Anuja Jhingran; Simona Shaitelman; Oliver Bogler; Kathleen Schmeller; David Followill; Rebecca Howell; Christopher Nelson; Christine Peterson; Beth Beadle
Journal:  J Vis Exp       Date:  2018-04-11       Impact factor: 1.355

3.  Radiation therapy infrastructure and human resources in low- and middle-income countries: present status and projections for 2020.

Authors:  Niloy R Datta; Massoud Samiei; Stephan Bodis
Journal:  Int J Radiat Oncol Biol Phys       Date:  2014-04-18       Impact factor: 7.038

4.  Point/Counterpoint: Within the next ten years treatment planning will become fully automated without the need for human intervention.

Authors:  Michael B Sharpe; Kevin L Moore; Colin G Orton
Journal:  Med Phys       Date:  2014-12       Impact factor: 4.071

5.  Radiotherapy plus cetuximab or cisplatin in human papillomavirus-positive oropharyngeal cancer (NRG Oncology RTOG 1016): a randomised, multicentre, non-inferiority trial.

Authors:  Maura L Gillison; Andy M Trotti; Jonathan Harris; Avraham Eisbruch; Paul M Harari; David J Adelstein; Richard C K Jordan; Weiqiang Zhao; Erich M Sturgis; Barbara Burtness; John A Ridge; Jolie Ringash; James Galvin; Min Yao; Shlomo A Koyfman; Dukagjin M Blakaj; Mohammed A Razaq; A Dimitrios Colevas; Jonathan J Beitler; Christopher U Jones; Neal E Dunlap; Samantha A Seaward; Sharon Spencer; Thomas J Galloway; Jack Phan; James J Dignam; Quynh Thu Le
Journal:  Lancet       Date:  2018-11-15       Impact factor: 79.321

6.  Institutional clinical trial accrual volume and survival of patients with head and neck cancer.

Authors:  Evan J Wuthrick; Qiang Zhang; Mitchell Machtay; David I Rosenthal; Phuc Felix Nguyen-Tan; André Fortin; Craig L Silverman; Adam Raben; Harold E Kim; Eric M Horwitz; Nancy E Read; Jonathan Harris; Qian Wu; Quynh-Thu Le; Maura L Gillison
Journal:  J Clin Oncol       Date:  2014-12-08       Impact factor: 44.544

7.  Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012.

Authors:  Jacques Ferlay; Isabelle Soerjomataram; Rajesh Dikshit; Sultan Eser; Colin Mathers; Marise Rebelo; Donald Maxwell Parkin; David Forman; Freddie Bray
Journal:  Int J Cancer       Date:  2014-10-09       Impact factor: 7.396

8.  3D Variation in delineation of head and neck organs at risk.

Authors:  Charlotte L Brouwer; Roel J H M Steenbakkers; Edwin van den Heuvel; Joop C Duppen; Arash Navran; Henk P Bijl; Olga Chouvalova; Fred R Burlage; Harm Meertens; Johannes A Langendijk; Aart A van 't Veld
Journal:  Radiat Oncol       Date:  2012-03-13       Impact factor: 3.481

9.  Tumor delineation: The weakest link in the search for accuracy in radiotherapy.

Authors:  C F Njeh
Journal:  J Med Phys       Date:  2008-10

10.  Automatic detection of contouring errors using convolutional neural networks.

Authors:  Dong Joo Rhee; Carlos E Cardenas; Hesham Elhalawani; Rachel McCarroll; Lifei Zhang; Jinzhong Yang; Adam S Garden; Christine B Peterson; Beth M Beadle; Laurence E Court
Journal:  Med Phys       Date:  2019-09-26       Impact factor: 4.071

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  4 in total

1.  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 2.  History of Technological Advancements towards MR-Linac: The Future of Image-Guided Radiotherapy.

Authors:  Nikhil Rammohan; James W Randall; Poonam Yadav
Journal:  J Clin Med       Date:  2022-08-12       Impact factor: 4.964

3.  Assessing the practicality of using a single knowledge-based planning model for multiple linac vendors.

Authors:  Raphael J Douglas; Adenike Olanrewaju; Lifei Zhang; Beth M Beadle; Laurence E Court
Journal:  J Appl Clin Med Phys       Date:  2022-07-05       Impact factor: 2.243

4.  Development and validation of a checklist for use with automatically generated radiotherapy plans.

Authors:  Kelly A Nealon; Laurence E Court; Raphael J Douglas; Lifei Zhang; Eun Young Han
Journal:  J Appl Clin Med Phys       Date:  2022-06-30       Impact factor: 2.243

  4 in total

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