Literature DB >> 24320490

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

Jun Lian1, Lulin Yuan, Yaorong Ge, Bhishamjit S Chera, David P Yoo, Sha Chang, FangFang Yin, Q Jackie Wu.   

Abstract

PURPOSE: To build a statistical model to quantitatively correlate the anatomic features of structures and the corresponding dose-volume histogram (DVH) of head and neck (HN) Tomotherapy (Tomo) plans. To study if the model built upon one intensity modulated radiation therapy (IMRT) technique (such as conventional Linac) can be used to predict anticipated organs-at-risk (OAR) DVH of patients treated with a different IMRT technique (such as Tomo). To study if the model built upon the clinical experience of one institution can be used to aid IMRT planning for another institution.
METHODS: Forty-four Tomotherapy intensity modulate radiotherapy plans of HN cases (Tomo-IMRT) from Institution A were included in the study. A different patient group of 53 HN fixed gantry IMRT (FG-IMRT) plans was selected from Institution B. The analyzed OARs included the parotid, larynx, spinal cord, brainstem, and submandibular gland. Two major groups of anatomical features were considered: the volumetric information and the spatial information. The volume information includes the volume of target, OAR, and overlapped volume between target and OAR. The spatial information of OARs relative to PTVs was represented by the distance-to-target histogram (DTH). Important anatomical and dosimetric features were extracted from DTH and DVH by principal component analysis. Two regression models, one for Tomotherapy plan and one for IMRT plan, were built independently. The accuracy of intratreatment-modality model prediction was validated by a leave one out cross-validation method. The intertechnique and interinstitution validations were performed by using the FG-IMRT model to predict the OAR dosimetry of Tomo-IMRT plans. The dosimetry of OARs, under the same and different institutional preferences, was analyzed to examine the correlation between the model prediction and planning protocol.
RESULTS: Significant patient anatomical factors contributing to OAR dose sparing in HN Tomotherapy plans have been analyzed and identified. For all the OARs, the discrepancies of dose indices between the model predicted values and the actual plan values were within 2.1%. Similar results were obtained from the modeling of FG-IMRT plans. The parotid gland was spared in a comparable fashion during the treatment planning of two institutions. The model based on FG-IMRT plans was found to predict the median dose of the parotid of Tomotherapy plans quite well, with a mean error of 2.6%. Predictions from the FG-IMRT model suggested the median dose of the larynx, median dose of the brainstem and D2 of the brainstem could be reduced by 10.5%, 12.8%, and 20.4%, respectively, in the Tomo-IMRT plans. This was found to be correlated to the institutional differences in OAR constraint settings. Re-planning of six Tomotherapy patients confirmed the potential of optimization improvement predicted by the FG-IMRT model was correct.
CONCLUSIONS: The authors established a mathematical model to correlate the anatomical features and dosimetric indexes of OARs of HN patients in Tomotherapy plans. The model can be used for the setup of patient-specific OAR dose sparing goals and quality control of planning results.The institutional clinical experience was incorporated into the model which allows the model from one institution to generate a reference plan for another institution, or another IMRT technique.

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Mesh:

Year:  2013        PMID: 24320490      PMCID: PMC3838428          DOI: 10.1118/1.4828788

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


  21 in total

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Authors:  Q Wu; R Mohan
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Review 2.  The physical basis of IMRT and inverse planning.

Authors:  S Webb
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3.  Variations in the contouring of organs at risk: test case from a patient with oropharyngeal cancer.

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

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5.  Predicting dose-volume histograms for organs-at-risk in IMRT planning.

Authors:  Lindsey M Appenzoller; Jeff M Michalski; Wade L Thorstad; Sasa Mutic; Kevin L Moore
Journal:  Med Phys       Date:  2012-12       Impact factor: 4.071

6.  Quantitative analysis of the factors which affect the interpatient organ-at-risk dose sparing variation in IMRT plans.

Authors:  Lulin Yuan; Yaorong Ge; W Robert Lee; Fang Fang Yin; John P Kirkpatrick; Q Jackie Wu
Journal:  Med Phys       Date:  2012-11       Impact factor: 4.071

7.  Geometric factors influencing dosimetric sparing of the parotid glands using IMRT.

Authors:  Margie A Hunt; Andrew Jackson; Ashwatha Narayana; Nancy Lee
Journal:  Int J Radiat Oncol Biol Phys       Date:  2006-09-01       Impact factor: 7.038

8.  Significant improvement in normal tissue sparing and target coverage for head and neck cancer by means of helical tomotherapy.

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Journal:  Radiother Oncol       Date:  2006-03-20       Impact factor: 6.280

9.  Principal component analysis-based pattern analysis of dose-volume histograms and influence on rectal toxicity.

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Journal:  Int J Radiat Oncol Biol Phys       Date:  2007-09-01       Impact factor: 7.038

10.  Tomotherapy-like versus VMAT-like treatments: a multicriteria comparison for a prostate geometry.

Authors:  Juan Pardo-Montero; John D Fenwick
Journal:  Med Phys       Date:  2012-12       Impact factor: 4.071

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  37 in total

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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.  Knowledge-Based Tradeoff Hyperplanes for Head and Neck Treatment Planning.

Authors:  Jiahan Zhang; Yaorong Ge; Yang Sheng; Chunhao Wang; Jiang Zhang; Yuan Wu; Qiuwen Wu; Fang-Fang Yin; Q Jackie Wu
Journal:  Int J Radiat Oncol Biol Phys       Date:  2020-01-24       Impact factor: 7.038

3.  Predicting the dose absorbed by organs at risk during intensity modulated radiation therapy for nasopharyngeal carcinoma.

Authors:  Haowen Pang; Xiaoyang Sun; Bo Yang; Jingbo Wu
Journal:  Br J Radiol       Date:  2018-08-10       Impact factor: 3.039

4.  Utilizing pre-determined beam orientation information in dose prediction by 3D fully-connected network for intensity modulated radiotherapy.

Authors:  Hui Yan; Shoulin Liu; Jingjing Zhang; Jianfei Liu; Teng Li
Journal:  Quant Imaging Med Surg       Date:  2021-12

5.  Outlier identification in radiation therapy knowledge-based planning: A study of pelvic cases.

Authors:  Yang Sheng; Yaorong Ge; Lulin Yuan; Taoran Li; Fang-Fang Yin; Qingrong Jackie Wu
Journal:  Med Phys       Date:  2017-09-30       Impact factor: 4.071

6.  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

7.  Atlas-guided prostate intensity modulated radiation therapy (IMRT) planning.

Authors:  Yang Sheng; Taoran Li; You Zhang; W Robert Lee; Fang-Fang Yin; Yaorong Ge; Q Jackie Wu
Journal:  Phys Med Biol       Date:  2015-09-08       Impact factor: 3.609

8.  A comparison of Monte Carlo dropout and bootstrap aggregation on the performance and uncertainty estimation in radiation therapy dose prediction with deep learning neural networks.

Authors:  Dan Nguyen; Azar Sadeghnejad Barkousaraie; Gyanendra Bohara; Anjali Balagopal; Rafe McBeth; Mu-Han Lin; Steve Jiang
Journal:  Phys Med Biol       Date:  2021-02-24       Impact factor: 3.609

9.  Performance of a Knowledge-Based Model for Optimization of Volumetric Modulated Arc Therapy Plans for Single and Bilateral Breast Irradiation.

Authors:  Antonella Fogliata; Giorgia Nicolini; Celine Bourgier; Alessandro Clivio; Fiorenza De Rose; Pascal Fenoglietto; Francesca Lobefalo; Pietro Mancosu; Stefano Tomatis; Eugenio Vanetti; Marta Scorsetti; Luca Cozzi
Journal:  PLoS One       Date:  2015-12-21       Impact factor: 3.240

10.  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

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