Literature DB >> 22867890

Fully automated simultaneous integrated boosted-intensity modulated radiation therapy treatment planning is feasible for head-and-neck cancer: a prospective clinical study.

Binbin Wu1, Todd McNutt, Marianna Zahurak, Patricio Simari, Dalong Pang, Russell Taylor, Giuseppe Sanguineti.   

Abstract

PURPOSE: To prospectively determine whether overlap volume histogram (OVH)-driven, automated simultaneous integrated boosted (SIB)-intensity-modulated radiation therapy (IMRT) treatment planning for head-and-neck cancer can be implemented in clinics. METHODS AND MATERIALS: A prospective study was designed to compare fully automated plans (APs) created by an OVH-driven, automated planning application with clinical plans (CPs) created by dosimetrists in a 3-dose-level (70 Gy, 63 Gy, and 58.1 Gy), head-and-neck SIB-IMRT planning. Because primary organ sparing (cord, brain, brainstem, mandible, and optic nerve/chiasm) always received the highest priority in clinical planning, the study aimed to show the noninferiority of APs with respect to PTV coverage and secondary organ sparing (parotid, brachial plexus, esophagus, larynx, inner ear, and oral mucosa). The sample size was determined a priori by a superiority hypothesis test that had 85% power to detect a 4% dose decrease in secondary organ sparing with a 2-sided alpha level of 0.05. A generalized estimating equation (GEE) regression model was used for statistical comparison.
RESULTS: Forty consecutive patients were accrued from July to December 2010. GEE analysis indicated that in APs, overall average dose to the secondary organs was reduced by 1.16 (95% CI = 0.09-2.33) with P=.04, overall average PTV coverage was increased by 0.26% (95% CI = 0.06-0.47) with P=.02 and overall average dose to the primary organs was reduced by 1.14 Gy (95% CI = 0.45-1.8) with P=.004. A physician determined that all APs could be delivered to patients, and APs were clinically superior in 27 of 40 cases.
CONCLUSIONS: The application can be implemented in clinics as a fast, reliable, and consistent way of generating plans that need only minor adjustments to meet specific clinical needs.
Copyright © 2012 Elsevier Inc. All rights reserved.

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Mesh:

Year:  2012        PMID: 22867890     DOI: 10.1016/j.ijrobp.2012.06.047

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


  31 in total

1.  Quantifying Unnecessary Normal Tissue Complication Risks due to Suboptimal Planning: A Secondary Study of RTOG 0126.

Authors:  Kevin L Moore; Rachel Schmidt; Vitali Moiseenko; Lindsey A Olsen; Jun Tan; Ying Xiao; James Galvin; Stephanie Pugh; Michael J Seider; Adam P Dicker; Walter Bosch; Jeff Michalski; Sasa Mutic
Journal:  Int J Radiat Oncol Biol Phys       Date:  2015-04-03       Impact factor: 7.038

2.  Automated IMRT planning in Pinnacle : A study in head-and-neck cancer.

Authors:  J M A M Kusters; K Bzdusek; P Kumar; P G M van Kollenburg; M C Kunze-Busch; M Wendling; T Dijkema; J H A M Kaanders
Journal:  Strahlenther Onkol       Date:  2017-08-02       Impact factor: 3.621

3.  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

4.  Dose Prediction Model for Duodenum Sparing With a Biodegradable Hydrogel Spacer for Pancreatic Cancer Radiation Therapy.

Authors:  Ziwei Feng; Avani D Rao; Zhi Cheng; Eun Ji Shin; Joseph Moore; Lin Su; Seong-Hun Kim; John Wong; Amol Narang; Joseph M Herman; Todd McNutt; Dengwang Li; Kai Ding
Journal:  Int J Radiat Oncol Biol Phys       Date:  2018-07-19       Impact factor: 7.038

5.  A predictive model to guide management of the overlap region between target volume and organs at risk in prostate cancer volumetric modulated arc therapy.

Authors:  Malcolm D Mattes; Jennifer C Lee; Sara Elnaiem; Adel Guirguis; N C Ikoro; Hani Ashamalla
Journal:  Radiat Oncol J       Date:  2014-03-27

Review 6.  Efficacy of intensity-modulated radiotherapy combined with chemotherapy or surgery in locally advanced squamous cell carcinoma of the head-and-neck.

Authors:  Hua Yang; Li-Qiong Diao; Mei Shi; Rui Ma; Jian-Hua Wang; Jian-Ping Li; Feng Xiao; Ying Xue; Man Xu; Bin Zhou
Journal:  Biologics       Date:  2013-10-18

7.  Assessment of a model based optimization engine for volumetric modulated arc therapy for patients with advanced hepatocellular cancer.

Authors:  Antonella Fogliata; Po-Ming Wang; Francesca Belosi; Alessandro Clivio; Giorgia Nicolini; Eugenio Vanetti; Luca Cozzi
Journal:  Radiat Oncol       Date:  2014-10-28       Impact factor: 3.481

8.  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

9.  Feasibility and efficacy of simultaneous integrated boost intensity-modulated radiation therapy in patients with limited-disease small cell lung cancer.

Authors:  Dan Han; Qin Qin; Shaoyu Hao; Wei Huang; Yumei Wei; Zicheng Zhang; Zhongtang Wang; Baosheng Li
Journal:  Radiat Oncol       Date:  2014-12-11       Impact factor: 3.481

10.  Can knowledge-based DVH predictions be used for automated, individualized quality assurance of radiotherapy treatment plans?

Authors:  Jim P Tol; Max Dahele; Alexander R Delaney; Ben J Slotman; Wilko F A R Verbakel
Journal:  Radiat Oncol       Date:  2015-11-19       Impact factor: 3.481

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