Automated VMAT treatment planning using sequential convex programming: Algorithm development and clinical implementation Journal Article

  


Authors: Dursun, P.; Hong, L.; Jhanwar, G.; Huang, Q.; Zhou, Y.; Yang, J.; Pham, H.; Cervino, L.; Moran, J. M.; Deasy, J. O.; Zarepisheh, M.
Article Title: Automated VMAT treatment planning using sequential convex programming: Algorithm development and clinical implementation
Abstract: Objective.To develop and clinically implement a fully automated treatment planning system (TPS) for volumetric modulated arc therapy (VMAT).Approach.We solve two constrained optimization problems sequentially. The tumor coverage is maximized at the first step while respecting all maximum/mean dose clinical criteria. The second step further reduces the dose at the surrounding organs-at-risk as much as possible. Our algorithm optimizes the machine parameters (leaf positions and monitor units) directly and the resulting mathematical non-convexity is handled using thesequential convex programmingby solving a series of convex approximation problems. We directly integrate two novel convex surrogate metrics to improve plan delivery efficiency and reduce plan complexity by promoting aperture shape regularity and neighboring aperture similarity. The entire workflow is automated using the Eclipse TPS application program interface scripting and provided to users as a plug-in, requiring the users to solely provide the contours and their preferred arcs. Our program provides the optimal machine parameters and does not utilize the Eclipse optimization engine, however, it utilizes the Eclipse final dose calculation engine. We have tested our program on 60 patients of different disease sites and prescriptions for stereotactic body radiotherapy (paraspinal (24 Gy × 1, 9 Gy × 3), oligometastis (9 Gy × 3), lung (18 Gy × 3, 12 Gy × 4)) and retrospectively compared the automated plans with the manual plans used for treatment. The program is currently deployed in our clinic and being used in our daily clinical routine to treat patients.Main results.The automated plans found dosimetrically comparable or superior to the manual plans. For paraspinal (24 Gy × 1), the automated plans especially improved tumor coverage (the average PTV (Planning Target Volume) 95% from 96% to 98% and CTV100% from 95% to 97%) and homogeneity (the average PTV maximum dose from 120% to 116%). For other sites/prescriptions, the automated plans especially improved the duty cycle (23%-39.4%).Significance.This work proposes a fully automated approach to the mathematically challenging VMAT problem. It also shows how the capabilities of the existing (Food and Drug Administration)FDA-approved commercial TPS can be enhanced using an in-house developed optimization algorithm that completely replaces the TPS optimization engine. The code and pertained models along with a sample dataset will be released on our ECHO-VMAT GitHub (https://github.com/PortPy-Project/ECHO-VMAT). © 2023 Institute of Physics and Engineering in Medicine.
Keywords: retrospective studies; intensity modulated radiation therapy; neoplasm; neoplasms; radiotherapy dosage; retrospective study; algorithms; algorithm; radiotherapy, intensity-modulated; radiotherapy planning, computer-assisted; procedures; organs at risk; humans; human; automated planning; direct machine parameter optimization; sequential convex programming; vmat optimization
Journal Title: Physics in Medicine and Biology
Volume: 68
Issue: 15
ISSN: 0031-9155
Publisher: IOP Publishing Ltd  
Date Published: 2023-08-07
Start Page: 155006
Language: English
DOI: 10.1088/1361-6560/ace09e
PUBMED: 37343584
PROVIDER: scopus
DOI/URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165520426&doi=10.1088%2f1361-6560%2face09e&partnerID=40&md5=c670ebe2a7bb0fbd213570b46e53c917
Notes: Article -- MSK Cancer Center Support Grant (P30 CA008748) acknowledged in PubMed and PDF -- MSK corresponding author is Pinar Dursun -- Source: Scopus
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MSK Authors
  1. Linda Xueqi Hong
    88 Hong
  2. Ying Zhou
    35 Zhou
  3. Joseph Owen Deasy
    523 Deasy
  4. Jie Yang
    50 Yang
  5. Hai Pham
    56 Pham
  6. Masoud Zarepisheh
    39 Zarepisheh
  7. Qijie Huang
    15 Huang
  8. Laura Isabel Cervino Arriba
    135 Cervino Arriba
  9. Pinar Dursun
    5 Dursun Gunduz
  10. Gourav Lalitkumar Jhanwar
    14 Jhanwar
  11. Jean Marie Moran
    48 Moran
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