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

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Abstract

Purpose

To evaluate the performance of a model-based optimisation process for volumetric modulated arc therapy, VMAT, applied to whole breast irradiation.

Methods and Materials

A set of 150 VMAT dose plans with simultaneous integrated boost were selected to train a model for the prediction of dose-volume constraints. The dosimetric validation was done on different groups of patients from three institutes for single (50 cases) and bilateral breast (20 cases).

Results

Quantitative improvements were observed between the model-based and the reference plans, particularly for heart dose. Of 460 analysed dose-volume objectives, 13% of the clinical plans failed to meet the constraints while the respective model-based plans succeeded. Only in 5 cases did the reference plans pass while the respective model-based failed the criteria. For the bilateral breast analysis, the model-based plans resulted in superior or equivalent dose distributions to the reference plans in 96% of the cases.

Conclusions

Plans optimised using a knowledge-based model to determine the dose-volume constraints showed dosimetric improvements when compared to earlier approved clinical plans. The model was applicable to patients from different centres for both single and bilateral breast irradiation. The data suggests that the dose-volume constraint optimisation can be effectively automated with the new engine and could encourage its application to clinical practice.

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Most cited references 19

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

J-C Michalski, Eli Moore, Lindsey M Appenzoller … (2012)

The objective of this work was to develop a quality control (QC) tool to reduce intensity modulated radiotherapy (IMRT) planning variability and improve treatment plan quality using mathematical models that predict achievable organ-at-risk (OAR) dose-volume histograms (DVHs) based on individual patient anatomy. A mathematical framework to predict achievable OAR DVHs was derived based on the correlation of expected dose to the minimum distance from a voxel to the PTV surface. OAR voxels sharing a range of minimum distances were computed as subvolumes. A three-parameter, skew-normal probability distribution was used to fit subvolume dose distributions, and DVH prediction models were developed by fitting the evolution of the skew-normal parameters as a function of distance with polynomials. Cohorts of 20 prostate and 24 head-and-neck IMRT plans with identical clinical objectives were used to train organ-specific average models for rectum, bladder, and parotids. A sum of residuals analysis quantifying the integrated difference between the clinically approved DVH and predicted DVH evaluated similarity between DVHs. The ability of the average models to prospectively predict DVHs was evaluated on an independent validation cohort of 20 prostate plans. Statistical comparison of the sums of residuals between training and validation cohorts quantified the accuracy of the average model. Restricted sums of residuals (RSR) were used to identify potential outliers, where large values of RSR indicate a clinical DVH that exceeds the predicted DVH by a considerable amount. A refined model was obtained for each organ by excluding outliers with large RSR values from the training cohort. The refined model was applied to the original training cohort and restricted sums of residuals were utilized to estimate potential DVH improvements. All cases were replanned and evaluated by the physician that approved the original plan. The ability of the refined models to correctly identify outliers was assessed using the residual sum between the original and replanned DVHs to quantify dosimetric gains realized under replanning. Statistical analysis of average sum of residuals for rectum (SR(rectum)=0.003±0.037), bladder (SR(bladder)=-0.008±0.037), and parotid (SR(parotid)=-0.003±0.060) training cohorts yielded mean values near zero and small with respect to the standard deviations, indicating that the average models are capturing the essential behavior of the training cohorts. The predictive abilities of the average rectum and bladder models were statistically indistinguishable between the training and validation sets, with SR(rectum)=0.002±0.044 and SR(bladder)=-0.018±0.058 for the validation set. The refined models' ability to detect outliers and predict achievable OAR DVHs was demonstrated by a strong correlation between predicted gains (RSR) and realized gains after replanning with sample correlation coefficients of r = 0.92 for the rectum, r = 0.88 for the bladder, and r = 0.84 for the parotid glands. The results demonstrate that our mathematical framework and modest training cohorts successfully predict achievable OAR DVHs based on individual patient anatomy. The models correctly identified suboptimal plans that demonstrated further OAR sparing after replanning. This modeling technique requires no manual intervention except for appropriate selection of a training set with identical evaluation criteria. Clinical implementation is in progress to evaluate impact on real-time IMRT QC.

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Evaluation of a knowledge-based planning solution for head and neck cancer.

Jim Tol, Alexander R Delaney, Max Dahele … (2015)

Automated and knowledge-based planning techniques aim to reduce variations in plan quality. RapidPlan uses a library consisting of different patient plans to make a model that can predict achievable dose-volume histograms (DVHs) for new patients and uses those models for setting optimization objectives. We benchmarked RapidPlan versus clinical plans for 2 patient groups, using 3 different libraries.

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A planning quality evaluation tool for prostate adaptive IMRT based on machine learning.

Danthai Thongphiew, Qiwei Wu, Fang Yin … (2011)

To ensure plan quality for adaptive IMRT of the prostate, we developed a quantitative evaluation tool using a machine learning approach. This tool generates dose volume histograms (DVHs) of organs-at-risk (OARs) based on prior plans as a reference, to be compared with the adaptive plan derived from fluence map deformation. Under the same configuration using seven-field 15 MV photon beams, DVHs of OARs (bladder and rectum) were estimated based on anatomical information of the patient and a model learned from a database of high quality prior plans. In this study, the anatomical information was characterized by the organ volumes and distance-to-target histogram (DTH). The database consists of 198 high quality prostate plans and was validated with 14 cases outside the training pool. Principal component analysis (PCA) was applied to DVHs and DTHs to quantify their salient features. Then, support vector regression (SVR) was implemented to establish the correlation between the features of the DVH and the anatomical information. DVH/DTH curves could be characterized sufficiently just using only two or three truncated principal components, thus, patient anatomical information was quantified with reduced numbers of variables. The evaluation of the model using the test data set demonstrated its accuracy approximately 80% in prediction and effectiveness in improving ART planning quality. An adaptive IMRT plan quality evaluation tool based on machine learning has been developed, which estimates OAR sparing and provides reference in evaluating ART.

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Author and article information

Contributors

Kenneth G.A. Gilhuijs: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 21 December 2015

Publication date Collection: 2015

Volume: 10

Issue: 12

Electronic Location Identifier: e0145137

Affiliations

[1 ]Radiotherapy and Radiosurgery Department, Humanitas Clinical and Research Center, Milan-Rozzano, Italy

[2 ]Oncology Institute of Southern Switzerland, Bellinzona, Switzerland

[3 ]Radiotherapy Department, ICM-Val d’Aurelle, Montpellier, France

University Medical Centre Utrecht, NETHERLANDS

Author notes

Competing Interests: Luca Cozzi acts as Scientific Advisor to Varian Medical Systems and is Clinical Research Scientist at Humanitas Cancer Center. All other co-authors have no conflicts of interest.

Conceived and designed the experiments: LC AF GN. Performed the experiments: CB AC FDR PF FL PM ST EV MS. Analyzed the data: AC LC FL. Wrote the paper: AC LC GN PF.

* E-mail: antonella.fogliata@ 123456humanitas.it

Article

Publisher ID: PONE-D-15-32941

DOI: 10.1371/journal.pone.0145137

PMC ID: 4686991

PubMed ID: 26691687

SO-VID: 1c6d689e-bad0-47d6-b502-c93f536fd1a2

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

History

Date received : 27 July 2015

Date accepted : 26 November 2015

Page count

Figures: 2, Tables: 4, Pages: 12

Funding

The funder Varian Medical Systems provided support in the form of salaries for author LC, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.

Custom metadata

Data Availability All data were used in completely anonymised way for an in silico simulation study. The Humanitas Research Hospital Ethical Committee requires that database access or sharing should be allowed conditionally, since public availability would compromise patient confidentiality and participant privacy. The relevant data of this study are available upon explicit request to all interested researchers. L. Cozzi can be contacted to request data.

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

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Abstract

Purpose

Methods and Materials

Results

Conclusions

Related collections

PLOS Climate

Most cited references 19

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

Evaluation of a knowledge-based planning solution for head and neck cancer.

A planning quality evaluation tool for prostate adaptive IMRT based on machine learning.

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