Literature DB >> 20095262

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

Binbin Wu1, Francesco Ricchetti, Giuseppe Sanguineti, Misha Kazhdan, Patricio Simari, Ming Chuang, Russell Taylor, Robert Jacques, Todd McNutt.   

Abstract

PURPOSE: Intensity modulated radiation therapy (IMRT) treatment plan quality depends on the planner's level of experience and the amount of time the planner invests in developing the plan. Planners often unwittingly accept plans when further sparing of the organs at risk (OARs) is possible. The authors propose a method of IMRT treatment plan quality control that helps planners to evaluate the doses of the OARs upon completion of a new plan.
METHODS: It is achieved by comparing the geometric configurations of the OARs and targets of a new patient with those of prior patients, whose plans are maintained in a database. They introduce the concept of a shape relationship descriptor and, specifically, the overlap volume histogram (OVH) to describe the spatial configuration of an OAR with respect to a target. The OVH provides a way to infer the likely DVHs of the OARs by comparing the relative spatial configurations between patients. A database of prior patients is built to serve as an external reference. At the conclusion of a new plan, planners search through the database and identify related patients by comparing the OAR-target geometric relationships of the new patient with those of prior patients. The treatment plans of these related patients are retrieved from the database and guide planners in determining whether lower doses delivered to the OARs in the new plan are feasible.
RESULTS: Preliminary evaluation is promising. In this evaluation, they applied the analysis to the parotid DVHs of 32 prior head-and-neck patients, whose plans are maintained in a database. Each parotid was queried against the other 63 parotids to determine whether a lower dose was possible. The 17 parotids that promised the greatest reduction in D50 (DVH dose at 50% volume) were flagged. These 17 parotids came from 13 patients. The method also indicated that the doses of the other nine parotids of the 13 patients could not be reduced, so they were included in the replanning process as controls. Replanning with an effort to reduce D50 was conducted on these 26 parotids. After replanning, the average reductions for D50 of the 17 flagged parotids and nine unflagged parotids were 6.6 and 1.9 Gy, respectively. These results demonstrate that the quality control method has accurately identified not only the parotids that require dose reductions but also those for which dose reductions are marginal. Originally, 11 of out the 17 flagged parotids did not meet the Radiation Therapy Oncology Group sparing goal of V(30 Gy) < 50%. Replanning reduced them to three. Additionally, PTV coverage and OAR sparing of the original plans were compared to those of the replans by using pairwise Wilcoxon p test. The statistical comparisons show that replanning compromised neither PTV coverage nor OAR sparing.
CONCLUSIONS: This method provides an effective quality control mechanism for evaluating the DVHs of the OARs. Adoption of such a method will advance the quality of current IMRT planning, providing better treatment plan consistency.

Entities:  

Mesh:

Year:  2009        PMID: 20095262     DOI: 10.1118/1.3253464

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  77 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.  Radiotherapy dose distribution prediction for breast cancer using deformable image registration.

Authors:  Xue Bai; Binbing Wang; Shengye Wang; Zhangwen Wu; Chengjun Gou; Qing Hou
Journal:  Biomed Eng Online       Date:  2020-05-29       Impact factor: 2.819

3.  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

4.  Automated prediction of dosimetric eligibility of patients with prostate cancer undergoing intensity-modulated radiation therapy using a convolutional neural network.

Authors:  Tomohiro Kajikawa; Noriyuki Kadoya; Kengo Ito; Yoshiki Takayama; Takahito Chiba; Seiji Tomori; Ken Takeda; Keiichi Jingu
Journal:  Radiol Phys Technol       Date:  2018-08-14

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

6.  A novel approach to SBRT patient quality assurance using EPID-based real-time transit dosimetry : A step to QA with in vivo EPID dosimetry.

Authors:  Christos Moustakis; Fatemeh Ebrahimi Tazehmahalleh; Khaled Elsayad; Francis Fezeu; Sergiu Scobioala
Journal:  Strahlenther Onkol       Date:  2020-01-10       Impact factor: 3.621

7.  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

8.  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

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

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.