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Dumane et al. J Cancer Metastasis Treat 2019;5:42                   Journal of Cancer
               DOI: 10.20517/2394-4722.2019.08                           Metastasis and Treatment




               Original Article                                                              Open Access


               Training and evaluation of a knowledge-based
               model for automated treatment planning of multiple

               brain metastases


               Vishruta A. Dumane, Tsu-Chi Tseng, Ren-Dih Sheu, Yeh-Chi Lo, Vishal Gupta, Audrey Saitta, Kenneth E.
               Rosenzweig, Sheryl Green

               Departmentof Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

               Correspondence to: Dr. Vishruta A. Dumane, Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, 1184
               5th Avenue, Box 1236, New York, NY 10029, USA. E-mail: vishruta.dumane@mountsinai.org

               How to cite this article: Dumane VA, Tseng TC, Sheu RD, Lo YC, Gupta V, Saitta A, Rosenzweig KE, Green S. Training and
               evaluation of a knowledge-based model for automated treatment planning of multiple brain metastases. J Cancer Metastasis
               Treat 2019;5:42. http://dx.doi.org/10.20517/2394-4722.2019.08

               Received: 14 Jan 2019    First Decision: 12 Feb 2019    Revised: 25 Mar 2019    Accepted: 15 Apr 2019    Published: 14 May 2019

               Science Editor: Lucyna Kepka    Copy Editor: Cai-Hong Wang    Production Editor:  Huan-liang Wu



               Abstract
               Aim: Volumetric modulated arc therapy (VMAT) has been utilized to plan and treat multiple cranial metastases
               using a single isocenter due to its ability to provide steep dose gradients around targets as well as low doses to
               critical structures. VMAT treatment is delivered in a much shorter time compared to using a single isocenter for the
               treatment of each lesion. However, there is a need to develop methods to reduce the treatment planning time for
               these cases while also standardizing the plan quality. In this work we demonstrate the use of RapidPlan, which is
               knowledge-based treatment (KBP) planning software to plan multiple cranial SRS cases.

               Methods: The 66 patient plans with 125 lesions (range 1-4, median 1) were used to train a model. In addition, the
               model was validated using 10 cases that were previously treated and chosen randomly. The clinical plans were
               compared to plans generated by RapidPlan for target coverage and critical organ dose.

               Results: Coverage to the target volume, gradient index, conformity index and minimum dose to the target showed no
               significant difference between the original clinical plan vs. the plan generated by KBP. A comparison of doses to the
               critical organs namely the brainstem, brain, chiasm, eyes, optic nerves and lenses showed no significant difference.
               Target dose homogeneity was slightly better with the clinical plan, however this difference was also statistically
               insignificant.



                           © The Author(s) 2019. Open Access This article is licensed under a Creative Commons Attribution 4.0
                           International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
                sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long
                as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license,
                and indicate if changes were made.


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