Literature DB >> 28653120

Automation of radiation treatment planning : Evaluation of head and neck cancer patient plans created by the Pinnacle3 scripting and Auto-Planning functions.

Stefan Speer1, Andreas Klein2, Lukas Kober3, Alexander Weiss4, Indra Yohannes5, Christoph Bert4.   

Abstract

BACKGROUND: Intensity-modulated radiotherapy (IMRT) techniques are now standard practice. IMRT or volumetric-modulated arc therapy (VMAT) allow treatment of the tumor while simultaneously sparing organs at risk. Nevertheless, treatment plan quality still depends on the physicist's individual skills, experiences, and personal preferences. It would therefore be advantageous to automate the planning process. This possibility is offered by the Pinnacle3 treatment planning system (Philips Healthcare, Hamburg, Germany) via its scripting language or Auto-Planning (AP) module.
MATERIALS AND METHODS: AP module results were compared to in-house scripts and manually optimized treatment plans for standard head and neck cancer plans. Multiple treatment parameters were scored to judge plan quality (100 points = optimum plan). Patients were initially planned manually by different physicists and re-planned using scripts or AP. RESULTS AND DISCUSSION: Script-based head and neck plans achieved a mean of 67.0 points and were, on average, superior to manually created (59.1 points) and AP plans (62.3 points). Moreover, they are characterized by reproducibility and lower standard deviation of treatment parameters. Even less experienced staff are able to create at least a good starting point for further optimization in a short time. However, for particular plans, experienced planners perform even better than scripts or AP. Experienced-user input is needed when setting up scripts or AP templates for the first time. Moreover, some minor drawbacks exist, such as the increase of monitor units (+35.5% for scripted plans).
CONCLUSION: On average, automatically created plans are superior to manually created treatment plans. For particular plans, experienced physicists were able to perform better than scripts or AP; thus, the benefit is greatest when time is short or staff inexperienced.

Entities:  

Keywords:  Brainstem; Organs at risk; Parotid gland; Radiotherapy, intensity-modulated; Spinal cord

Mesh:

Year:  2017        PMID: 28653120     DOI: 10.1007/s00066-017-1150-9

Source DB:  PubMed          Journal:  Strahlenther Onkol        ISSN: 0179-7158            Impact factor:   3.621


  12 in total

1.  [The ICRU Report 83: prescribing, recording and reporting photon-beam intensity-modulated radiation therapy (IMRT)].

Authors:  N Hodapp
Journal:  Strahlenther Onkol       Date:  2012-01       Impact factor: 3.621

2.  Use of normal tissue complication probability models in the clinic.

Authors:  Lawrence B Marks; Ellen D Yorke; Andrew Jackson; Randall K Ten Haken; Louis S Constine; Avraham Eisbruch; Søren M Bentzen; Jiho Nam; Joseph O Deasy
Journal:  Int J Radiat Oncol Biol Phys       Date:  2010-03-01       Impact factor: 7.038

3.  Clinical knowledge-based inverse treatment planning.

Authors:  Yong Yang; Lei Xing
Journal:  Phys Med Biol       Date:  2004-11-21       Impact factor: 3.609

4.  Using overlap volume histogram and IMRT plan data to guide and automate VMAT planning: a head-and-neck case study.

Authors:  Binbin Wu; Dalong Pang; Patricio Simari; Russell Taylor; Giuseppe Sanguineti; Todd McNutt
Journal:  Med Phys       Date:  2013-02       Impact factor: 4.071

5.  Automated volumetric modulated Arc therapy treatment planning for stage III lung cancer: how does it compare with intensity-modulated radio therapy?

Authors:  Enzhuo M Quan; Joe Y Chang; Zhongxing Liao; Tingyi Xia; Zhiyong Yuan; Hui Liu; Xiaoqiang Li; Cody A Wages; Radhe Mohan; Xiaodong Zhang
Journal:  Int J Radiat Oncol Biol Phys       Date:  2012-09-01       Impact factor: 7.038

6.  Dose-dependent deterioration of swallowing function after induction chemotherapy and definitive chemoradiotherapy for laryngopharyngeal cancer.

Authors:  M Haderlein; S Semrau; O Ott; S Speer; C Bohr; R Fietkau
Journal:  Strahlenther Onkol       Date:  2014-02       Impact factor: 3.621

7.  A new concept for interactive radiotherapy planning with multicriteria optimization: first clinical evaluation.

Authors:  Christian Thieke; Karl-Heinz Küfer; Michael Monz; Alexander Scherrer; Fernando Alonso; Uwe Oelfke; Peter E Huber; Jürgen Debus; Thomas Bortfeld
Journal:  Radiother Oncol       Date:  2007-09-24       Impact factor: 6.280

8.  Patient geometry-driven information retrieval for IMRT treatment plan quality control.

Authors:  Binbin Wu; Francesco Ricchetti; Giuseppe Sanguineti; Misha Kazhdan; Patricio Simari; Ming Chuang; Russell Taylor; Robert Jacques; Todd McNutt
Journal:  Med Phys       Date:  2009-12       Impact factor: 4.071

Review 9.  Sensorineural hearing loss in patients with head and neck cancer after chemoradiotherapy and radiotherapy: a systematic review of the literature.

Authors:  Eleonoor A R Theunissen; Sophie C J Bosma; Charlotte L Zuur; René Spijker; Sieberen van der Baan; Wouter A Dreschler; Jan Paul de Boer; Alfons J M Balm; Coen R N Rasch
Journal:  Head Neck       Date:  2014-01-29       Impact factor: 3.147

10.  Automated IMRT planning with regional optimization using planning scripts.

Authors:  Ilma Xhaferllari; Eugene Wong; Karl Bzdusek; Michael Lock; Jeff Chen
Journal:  J Appl Clin Med Phys       Date:  2013-01-07       Impact factor: 2.102

View more
  9 in total

Review 1.  Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations.

Authors:  Mohammad Hussein; Ben J M Heijmen; Dirk Verellen; Andrew Nisbet
Journal:  Br J Radiol       Date:  2018-09-04       Impact factor: 3.039

2.  Automatic replanning of VMAT plans for different treatment machines: A template-based approach using constrained optimization.

Authors:  Luise A Künzel; Oliver S Dohm; Markus Alber; Daniel Zips; Daniela Thorwarth
Journal:  Strahlenther Onkol       Date:  2018-05-30       Impact factor: 3.621

3.  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

4.  Automatic Planning for Nasopharyngeal Carcinoma Based on Progressive Optimization in RayStation Treatment Planning System.

Authors:  Yiwei Yang; Kainan Shao; Jie Zhang; Ming Chen; Yuanyuan Chen; Guoping Shan
Journal:  Technol Cancer Res Treat       Date:  2020 Jan-Dec

5.  Can the Student Outperform the Master? A Plan Comparison Between Pinnacle Auto-Planning and Eclipse knowledge-Based RapidPlan Following a Prostate-Bed Plan Competition.

Authors:  April Smith; Andrew Granatowicz; Cole Stoltenberg; Shuo Wang; Xiaoying Liang; Charles A Enke; Andrew O Wahl; Sumin Zhou; Dandan Zheng
Journal:  Technol Cancer Res Treat       Date:  2019 Jan-Dec

6.  Quantitative Comparison of Knowledge-Based and Manual Intensity Modulated Radiation Therapy Planning for Nasopharyngeal Carcinoma.

Authors:  Jiang Hu; Boji Liu; Weihao Xie; Jinhan Zhu; Xiaoli Yu; Huikuan Gu; Mingli Wang; Yixuan Wang; ZhenYu Qi
Journal:  Front Oncol       Date:  2021-01-07       Impact factor: 6.244

7.  A knowledge-based intensity-modulated radiation therapy treatment planning technique for locally advanced nasopharyngeal carcinoma radiotherapy.

Authors:  Penggang Bai; Xing Weng; Kerun Quan; Jihong Chen; Yitao Dai; Yuanji Xu; Fasheng Lin; Jing Zhong; Tianming Wu; Chuanben Chen
Journal:  Radiat Oncol       Date:  2020-08-03       Impact factor: 3.481

Review 8.  Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Authors:  Chunhao Wang; Xiaofeng Zhu; Julian C Hong; Dandan Zheng
Journal:  Technol Cancer Res Treat       Date:  2019-01-01

9.  Characterization of automatic treatment planning approaches in radiotherapy.

Authors:  Geert Wortel; Dave Eekhout; Emmy Lamers; René van der Bel; Karen Kiers; Terry Wiersma; Tomas Janssen; Eugène Damen
Journal:  Phys Imaging Radiat Oncol       Date:  2021-07-13
  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.