Literature DB >> 21277097

Experience-based quality control of clinical intensity-modulated radiotherapy planning.

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

Abstract

PURPOSE: To incorporate a quality control tool, according to previous planning experience and patient-specific anatomic information, into the intensity-modulated radiotherapy (IMRT) plan generation process and to determine whether the tool improved treatment plan quality. METHODS AND MATERIALS: A retrospective study of 42 IMRT plans demonstrated a correlation between the fraction of organs at risk (OARs) overlapping the planning target volume and the mean dose. This yielded a model, predicted dose = prescription dose (0.2 + 0.8 [1 - exp(-3 overlapping planning target volume/volume of OAR)]), that predicted the achievable mean doses according to the planning target volume overlap/volume of OAR and the prescription dose. The model was incorporated into the planning process by way of a user-executable script that reported the predicted dose for any OAR. The script was introduced to clinicians engaged in IMRT planning and deployed thereafter. The script's effect was evaluated by tracking δ = (mean dose-predicted dose)/predicted dose, the fraction by which the mean dose exceeded the model.
RESULTS: All OARs under investigation (rectum and bladder in prostate cancer; parotid glands, esophagus, and larynx in head-and-neck cancer) exhibited both smaller δ and reduced variability after script implementation. These effects were substantial for the parotid glands, for which the previous δ = 0.28 ± 0.24 was reduced to δ = 0.13 ± 0.10. The clinical relevance was most evident in the subset of cases in which the parotid glands were potentially salvageable (predicted dose <30 Gy). Before script implementation, an average of 30.1 Gy was delivered to the salvageable cases, with an average predicted dose of 20.3 Gy. After implementation, an average of 18.7 Gy was delivered to salvageable cases, with an average predicted dose of 17.2 Gy. In the prostate cases, the rectum model excess was reduced from δ = 0.28 ± 0.20 to δ = 0.07 ± 0.15. On surveying dosimetrists at the end of the study, most reported that the script both improved their IMRT planning (8 of 10) and increased their efficiency (6 of 10).
CONCLUSIONS: This tool proved successful in increasing normal tissue sparing and reducing interclinician variability, providing effective quality control of the IMRT plan development process.
Copyright © 2011 Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21277097     DOI: 10.1016/j.ijrobp.2010.11.030

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


  99 in total

1.  Evaluating inter-campus plan consistency using a knowledge based planning model.

Authors:  Sean L Berry; Rongtao Ma; Amanda Boczkowski; Andrew Jackson; Pengpeng Zhang; Margie Hunt
Journal:  Radiother Oncol       Date:  2016-07-06       Impact factor: 6.280

2.  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

3.  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

4.  Radiobiological characteristics of cancer stem cells from esophageal cancer cell lines.

Authors:  Jian-Lin Wang; Jing-Ping Yu; Zhi-Qiang Sun; Su-Ping Sun
Journal:  World J Gastroenterol       Date:  2014-12-28       Impact factor: 5.742

5.  An atlas-based method to predict three-dimensional dose distributions for cancer patients who receive radiotherapy.

Authors:  S A Yoganathan; Rui Zhang
Journal:  Phys Med Biol       Date:  2019-04-12       Impact factor: 3.609

Review 6.  Interobserver variation in parotid gland delineation: a study of its impact on intensity-modulated radiotherapy solutions with a systematic review of the literature.

Authors:  S W Loo; W M C Martin; P Smith; S Cherian; T W Roques
Journal:  Br J Radiol       Date:  2012-08       Impact factor: 3.039

7.  Process-based quality management for clinical implementation of adaptive radiotherapy.

Authors:  Camille E Noel; Lakshmi Santanam; Parag J Parikh; Sasa Mutic
Journal:  Med Phys       Date:  2014-08       Impact factor: 4.071

8.  Incorporating single-side sparing in models for predicting parotid dose sparing in head and neck IMRT.

Authors:  Lulin Yuan; Q Jackie Wu; Fang-Fang Yin; Yuliang Jiang; David Yoo; Yaorong Ge
Journal:  Med Phys       Date:  2014-02       Impact factor: 4.071

9.  Modeling the dosimetry of organ-at-risk in head and neck IMRT planning: an intertechnique and interinstitutional study.

Authors:  Jun Lian; Lulin Yuan; Yaorong Ge; Bhishamjit S Chera; David P Yoo; Sha Chang; FangFang Yin; Q Jackie Wu
Journal:  Med Phys       Date:  2013-12       Impact factor: 4.071

10.  Machine learning and modeling: Data, validation, communication challenges.

Authors:  Issam El Naqa; Dan Ruan; Gilmer Valdes; Andre Dekker; Todd McNutt; Yaorong Ge; Q Jackie Wu; Jung Hun Oh; Maria Thor; Wade Smith; Arvind Rao; Clifton Fuller; Ying Xiao; Frank Manion; Matthew Schipper; Charles Mayo; Jean M Moran; Randall Ten Haken
Journal:  Med Phys       Date:  2018-08-24       Impact factor: 4.071

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