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      Photon Optimizer (PO) prevails over Progressive Resolution Optimizer (PRO) for VMAT planning with or without knowledge‐based solution

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          Abstract

          The enhanced dosimetric performance of knowledge‐based volumetric modulated arc therapy ( VMAT) planning might be jointly contributed by the patient‐specific optimization objectives, as estimated by the RapidPlan model, and by the potentially improved Photon Optimizer ( PO) algorithm than the previous Progressive Resolution Optimizer ( PRO) engine. As PO is mandatory for RapidPlan estimation but optional for conventional manual planning, appreciating the two optimizers may provide practical guidelines for the algorithm selection because knowledge‐based planning may not replace the current method completely in a short run. Using a previously validated dose–volume histogram ( DVH) estimation model which can produce clinically acceptable plans automatically for rectal cancer patients without interactive manual adjustment, this study reoptimized 30 historically approved plans (referred as clinical plans that were created manually with PRO) with RapidPlan solution ( PO plans). Then the PRO algorithm was utilized to optimize the plans again using the same dose–volume constraints as PO plans, where the line objectives were converted as a series of point objectives automatically ( PRO plans). On the basis of comparable target dose coverage, the combined applications of new objectives and PO algorithm have significantly reduced the organs‐at‐risk ( OAR) exposure by 23.49–32.72% than the clinical plans. These discrepancies have been largely preserved after substituting PRO for PO, indicating the dosimetric improvements were mostly attributable to the refined objectives. Therefore, Eclipse users of earlier versions may instantly benefit from adopting the model‐generated objectives from other RapidPlan‐equipped centers, even with PRO algorithm. However, the additional contribution made by the PO relative to PRO accounted for 1.54–3.74%, suggesting PO should be selected with priority whenever available, with or without RapidPlan solution as a purchasable package. Significantly increased monitor units were associated with the model‐generated objectives but independent from the optimizers, indicating higher modulation in these plans. As a summary, PO prevails over PRO algorithm for VMAT planning with or without knowledge‐based technique.

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

          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.

            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.

              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
                ybzhang77@gmail.com
                Journal
                J Appl Clin Med Phys
                J Appl Clin Med Phys
                10.1002/(ISSN)1526-9914
                ACM2
                Journal of Applied Clinical Medical Physics
                John Wiley and Sons Inc. (Hoboken )
                1526-9914
                24 January 2017
                March 2017
                : 18
                : 2 ( doiID: 10.1002/acm2.2017.18.issue-2 )
                : 9-14
                Affiliations
                [ 1 ] Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing) Department of Radiation Oncology Peking University Cancer Hospital & Institute Beijing Cancer Hospital Beijing China
                [ 2 ] Department of Radiation Oncology The First Affiliated Hospital of Zhengzhou University and Department of Radiation Oncology Basic Medical College of Zhengzhou University Zhengzhou Henan China
                Author notes
                [*] [* ] Author to whom correspondence should to be addressed. Yibao Zhang

                E‐mail: ybzhang77@ 123456gmail.com ;

                Telephone: 8610 88196033; Fax: 8610 88196033.

                Article
                ACM212038
                10.1002/acm2.12038
                5689948
                28300375
                25fdb14f-8ee9-4edb-84bd-29fba1994c05
                © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 May 2016
                : 13 November 2016
                Page count
                Figures: 1, Tables: 3, Pages: 6, Words: 4523
                Funding
                Funded by: Beijing Natural Science Foundation
                Award ID: 7172048
                Funded by: National Natural Science Foundation of China
                Award ID: 11505012
                Funded by: Beijing Municipal Administration of Hospitals' Youth Programme
                Award ID: QML20151004
                Funded by: Quality Scientific Research in the Public Welfare
                Award ID: 201510001‐02
                Categories
                87.55.de
                87.55.dk
                Radiation Oncology Physics
                Radiation Oncology Physics
                Custom metadata
                2.0
                acm212038
                March 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.2.5 mode:remove_FC converted:16.11.2017

                knowledge‐based planning,photon optimizer,progressive resolution optimizer,rapidplan,vmat

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