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      Effects of model size and composition on quality of head‐and‐neck knowledge‐based plans

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          Abstract

          Purpose

          Knowledge‐based planning (KBP) aims to automate and standardize treatment planning. New KBP users are faced with many questions: How much does model size matter, and are multiple models needed to accommodate specific physician preferences? In this study, six head‐and‐neck KBP models were trained to address these questions.

          Methods

          The six models differed in training size and plan composition: The KBP Full ( n = 203 plans), KBP 101 ( n = 101), KBP 50 ( n = 50), and KBP 25 ( n = 25) were trained with plans from two head‐and‐neck physicians. KBP A and KBP B each contained n = 101 plans from only one physician, respectively. An independent set of 39 patients treated to 6000–7000 cGy by a third physician was re‐planned with all KBP models for validation. Standard head‐and‐neck dosimetric parameters were used to compare resulting plans. KBP Full plans were compared to the clinical plans to evaluate overall model quality. Additionally, clinical and KBP Full plans were presented to another physician for blind review. Dosimetric comparison of KBP Full against KBP 101 , KBP 50 , and KBP 25 investigated the effect of model size. Finally, KBP A versus KBP B tested whether training KBP models on plans from one physician only influences the resulting output. Dosimetric differences were tested for significance using a paired t‐test ( p < 0.05).

          Results

          Compared to manual plans, KBP Full significantly increased PTV Low D95% and left parotid mean dose but decreased dose cochlea, constrictors, and larynx. The physician preferred the KBP Full plan over the manual plan in 20/39 cases. Dosimetric differences between KBP Full , KBP 101 , KBP 50 , and KBP 25 plans did not exceed 187 cGy on aggregate, except for the cochlea. Further, average differences between KBP A and KBP B were below 110 cGy.

          Conclusions

          Overall, all models were shown to produce high‐quality plans. Differences between model outputs were small compared to the prescription. This indicates only small improvements when increasing model size and minimal influence of the physician when choosing treatment plans for training head‐and‐neck KBP models.

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          Most cited references29

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          Variation in external beam treatment plan quality: An inter-institutional study of planners and planning systems.

          This study quantifies variation in radiation treatment plan quality for plans generated by a population of treatment planners given very specific plan objectives. A "Plan Quality Metric" (PQM) with 14 submetrics, each with a unique value function, was defined for a prostate treatment plan, serving as specific goals of a hypothetical "virtual physician." The exact PQM logic was distributed to a population of treatment planners (to remove ambiguity of plan goals or plan assessment methodology) as was a predefined computed tomographic image set and anatomic structure set (to remove anatomy delineation as a variable). Treatment planners used their clinical treatment planning system (TPS) to generate their best plan based on the specified goals and submitted their results for analysis. One hundred forty datasets were received and 125 plans accepted and analyzed. There was wide variability in treatment plan quality (defined as the ability of the planners and plans to meet the specified goals) quantified by the PQM. Despite the variability, the resulting PQM distributions showed no statistically significant difference between TPS employed, modality (intensity modulated radiation therapy versus arc), or education and certification status of the planner. The PQM results showed negligible correlation to number of beam angles, total monitor units, years of experience of the planner, or planner confidence. The ability of the treatment planners to meet the specified plan objectives (as quantified by the PQM) exhibited no statistical dependence on technologic parameters (TPS, modality, plan complexity), nor was the plan quality statistically different based on planner demographics (years of experience, confidence, certification, and education). Therefore, the wide variation in plan quality could be attributed to a general "planner skill" category that would lend itself to processes of continual improvement where best practices could be derived and disseminated to improve the mean quality and minimize the variation in any population of treatment planners. Copyright © 2012 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.
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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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              Clinical validation and benchmarking of knowledge-based IMRT and VMAT treatment planning in pelvic anatomy.

              The aim of this work was to determine whether a commercial knowledge-based treatment planning (KBP) module can efficiently produce IMRT and VMAT plans in the pelvic region (prostate & cervical cancer), and to assess sensitivity of plan quality to training data and model parameters.
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                Author and article information

                Contributors
                rkaderka@med.miami.edu
                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
                05 October 2023
                February 2024
                : 25
                : 2 ( doiID: 10.1002/acm2.v25.2 )
                : e14168
                Affiliations
                [ 1 ] Department of Radiation Oncology University of Miami Miller School of Medicine Miami Florida USA
                Author notes
                [*] [* ] Correspondence

                Robert Kaderka, Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12th Ave, Miami, FL 33136, USA.

                Email: rkaderka@ 123456med.miami.edu

                Article
                ACM214168
                10.1002/acm2.14168
                10860434
                37798910
                e5d4cc4f-cdf5-4ac4-bb3d-fd89714decb8
                © 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 August 2023
                : 22 February 2023
                : 15 September 2023
                Page count
                Figures: 3, Tables: 4, Pages: 12, Words: 5941
                Categories
                Radiation Oncology Physics
                Radiation Oncology Physics
                Custom metadata
                2.0
                February 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.6 mode:remove_FC converted:12.02.2024

                automated planning,head‐and‐neck,knowledge‐based planning

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