Advertisement
Research Article| Volume 45, ISSUE 4, P346-351, December 2020

Dosimetric and planning efficiency comparison for lung SBRT: CyberKnife vs VMAT vs knowledge-based VMAT

      Abstract

      This is the first study that compared treatment plan quality and planning efficiency for lung stereotactic body radiation therapy (SBRT) using CyberKnife (CK) Multiplan vs Varian Eclipse treatment planning systems, including volumetric modulated arc therapy (VMAT) and knowledge-based VMAT (KBP-VMAT). Thirteen lung SBRT patients treated with 50 to 55 Gy in 3 or 5 fractions were retrospectively included in this study. CK plans created with Multiplan V. 4.6.1 using 2 fixed circular cones were previously approved used for treatment. For the comparison, the computed tomography (CT) data sets and contours from the CK plans were used to generate VMAT and KBP-VMAT plans (University of California San Diego publicly-shared RapidPlan model) using Eclipse V. 13.7. Metrics used for the comparison of CK, VMAT, and KBP-VMAT plans included monitor units (MUs), conformity indices, dose heterogeneity, high-dose spillage, low-dose spillage, adjacent organs at risk (OAR) doses, and treatment planning time. One-way analysis of variance with post-hoc Tukey tests and paired t-tests were used to analyze the difference of these metrics corresponding to the different planning techniques. All of the 3 planning techniques achieved our clinical goals. With similar planning target volume (PTV) coverage, CK plans yielded the most MU (p< 0.001), the least dose homogeneity (p < 0.002), and the least D2cm dose (p < 0.001), while KBP-VMAT plans resulted in the most OAR sparing. No significant difference was found among other dosimetric metrics such as high-dose spillage, lung V20 and volume receiving 50% of the prescription dose. Compared to VMAT, KBP-VMAT improved OAR sparing (p < 0.05), but required significantly more MU (p < 0.001). KBP-VMAT was associated with the shortest planning time. Eclipse-based VMAT can achieve comparable plan quality for lung SBRT as CK, in a more efficient manner. RapidPlan can facilitate the planning process of KBP-VMAT, with potentially better OAR sparing but higher MU requirements. Further improvement for KBP-VMAT is likely achievable by developing site-specific patient models.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Medical Dosimetry
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      Reference

      1. Key Statistics for Lung Cancer. https://www.cancer.org/cancer/non-small-cell-lung-cancer/about/key-statistics.html. Accessed September 16, 2019.

        • Chen VW
        • Ruiz BA
        • Hsieh M-C
        • Wu X-C
        • Ries LAG
        • Lewis DR
        Analysis of stage and clinical/prognostic factors for lung cancer from SEER registries: AJCC staging and collaborative stage data collection system.
        Cancer. 2014; 120: 3781-3792https://doi.org/10.1002/cncr.29045
        • Ettinger DS
        • Wood DE
        • Aisner DL
        • et al.
        Non–small cell lung cancer, Version 5.2017, NCCN clinical practice guidelines in oncology.
        J Natl Comprehens Cancer Netw. 2017; 15: 504-535https://doi.org/10.6004/jnccn.2017.0050
        • Kang KH
        • Okoye CC
        • Patel RB
        • et al.
        Complications from stereotactic body radiotherapy for lung cancer.
        Cancers (Basel). 2015; 7: 981-1004https://doi.org/10.3390/cancers7020820
      2. Stereotactic body radiation therapy: The report of AAPM Task Group 101. - PubMed - NCBI. https://www.ncbi.nlm.nih.gov/pubmed/20879569. Accessed September 20, 2019.

        • Wang Z
        • Kong Q-T
        • Li J
        • et al.
        Clinical outcomes of cyberknife stereotactic radiosurgery for lung metastases.
        J Thorac Dis. 2015; 7: 407-412https://doi.org/10.3978/j.issn.2072-1439.2015.01.09
        • Khadige M
        • Salleron J
        • Marchesi V
        • Oldrini G
        • Peiffert D
        • Beckendorf V
        Cyberknife ® stereotactic radiation therapy for stage I lung cancer and pulmonary metastases: evaluation of local control at 24 months.
        J Thorac Disease. 2018; 10 (4976-4984-4984)
        • Teoh M
        • Clark CH
        • Wood K
        • Whitaker S
        • Nisbet A
        Volumetric modulated arc therapy: A review of current literature and clinical use in practice.
        Br J Radiol. 2011; 84: 967-996https://doi.org/10.1259/bjr/22373346
        • Verbakel WFAR
        • Senan S
        • Cuijpers JP
        • Slotman BJ
        • Lagerwaard FJ
        Rapid delivery of stereotactic radiotherapy for peripheral lung tumors using volumetric intensity-modulated arcs.
        Radiother Oncol. 2009; 93: 122-124https://doi.org/10.1016/j.radonc.2009.05.020
        • Merrow CE
        • Wang IZ
        • Podgorsak MB
        A dosimetric evaluation of VMAT for the treatment of non‐small cell lung cancer.
        J Appl Clin Med Phys. 2012; 14: 228-238https://doi.org/10.1120/jacmp.v14i1.4110
        • Aoki S
        • Yamashita H
        • Haga A
        • et al.
        Flattening filter-free technique in volumetric modulated arc therapy for lung stereotactic body radiotherapy: A clinical comparison with the flattening filter technique.
        Oncol Lett. 2018; 15: 3928-3936https://doi.org/10.3892/ol.2018.7809
        • Hussein M
        • Heijmen BJM
        • Verellen D
        • Nisbet A
        Automation in intensity modulated radiotherapy treatment planning—A review of recent innovations.
        Br J Radiol. 2018; 9120180270https://doi.org/10.1259/bjr.20180270
        • Petrovic S
        • Khussainova G
        • Jagannathan R
        Knowledge-light adaptation approaches in case-based reasoning for radiotherapy treatment planning.
        Artif Intell Med. 2016; 68: 17-28https://doi.org/10.1016/j.artmed.2016.01.006
        • McIntosh C
        • Purdie TG
        Contextual Atlas Regression Forests: multiple-atlas-based automated dose prediction in radiation therapy.
        IEEE Trans Med Imaging. 2016; 35: 1000-1012https://doi.org/10.1109/TMI.2015.2505188
        • Sheng Y
        • Li T
        • Zhang Y
        • et al.
        Atlas-guided prostate intensity modulated radiation therapy (IMRT) planning.
        Phys Med Biol. 2015; 60: 7277-7291https://doi.org/10.1088/0031-9155/60/18/7277
        • Lin K-M
        • Simpson J
        • Sasso G
        • Raith A
        • Ehrgott M
        Quality assessment for VMAT prostate radiotherapy planning based on data envelopment analysis.
        Phys Med Biol. 2013; 58: 5753-5769https://doi.org/10.1088/0031-9155/58/16/5753
        • Liu ESF
        • Wu VWC
        • Harris B
        • Lehman M
        • Pryor D
        • Chan LWC
        Vector-model-supported approach in prostate plan optimization.
        Med Dosim. 2017; 42: 79-84https://doi.org/10.1016/j.meddos.2017.01.001
        • Chanyavanich V
        • Das SK
        • Lee WR
        • Lo JY
        Knowledge-based IMRT treatment planning for prostate cancer.
        Med Phys. 2011; 38: 2515-2522https://doi.org/10.1118/1.3574874
        • Kuo L
        • Yorke ED
        • Dumane VA
        • et al.
        Geometric dose prediction model for hemithoracic intensity-modulated radiation therapy in mesothelioma patients with two intact lungs.
        J Appl Clin Med Phys. 2016; 17: 371-379https://doi.org/10.1120/jacmp.v17i3.6199
        • Zawadzka A
        • Nesteruk M
        • Brzozowska B
        • Kukołowicz PF
        Method of predicting the mean lung dose based on a patient׳s anatomy and dose-volume histograms.
        Med Dosim. 2017; 42: 57-62https://doi.org/10.1016/j.meddos.2016.12.001
        • Petit SF
        • van Elmpt W
        Accurate prediction of target dose-escalation and organ-at-risk dose levels for non-small cell lung cancer patients.
        Radiother Oncol. 2015; 117: 453-458https://doi.org/10.1016/j.radonc.2015.07.040
        • Ma C
        • Huang F
        Assessment of a knowledge-based RapidPlan model for patients with postoperative cervical cancer.
        Precis Radiat Oncol. 2017; 1: 102-107https://doi.org/10.1002/pro6.23
        • Fogliata A
        • Reggiori G
        • Stravato A
        • et al.
        RapidPlan head and neck model: The objectives and possible clinical benefit.
        Radiat Oncol. 2017; 12: 73https://doi.org/10.1186/s13014-017-0808-x
        • Foy JJ
        • Marsh R
        • Ten Haken RK
        • et al.
        An analysis of knowledge-based planning for stereotactic body radiation therapy of the spine.
        Pract Radiat Oncol. 2017; 7: e355-e360https://doi.org/10.1016/j.prro.2017.02.007
        • Chin Snyder K
        • Kim J
        • Reding A
        • et al.
        Development and evaluation of a clinical model for lung cancer patients using stereotactic body radiotherapy (SBRT) within a knowledge‐based algorithm for treatment planning.
        J Appl Clin Med Phys. 2016; 17: 263-275https://doi.org/10.1120/jacmp.v17i6.6429
        • Ding C
        • Chang C-H
        • Haslam J
        • Timmerman R
        • Solberg T
        A dosimetric comparison of stereotactic body radiation therapy techniques for lung cancer: robotic versus conventional LINAC-based systems.
        J Appl Clin Med Phys. 2010; 11: 3223https://doi.org/10.1120/jacmp.v11i3.3223
        • Chan MKH
        • Kwong DLW
        • Law GML
        • et al.
        Dosimetric evaluation of four-dimensional dose distributions of CyberKnife and volumetric-modulated arc radiotherapy in stereotactic body lung radiotherapy.
        J Appl Clin Med Phys. 2013; 14: 4229https://doi.org/10.1120/jacmp.v14i4.4229
        • Wu H
        • Jiang F
        • Yue H
        • Zhang H
        • Wang K
        • Zhang Y
        Applying a RapidPlan model trained on a technique and orientation to another: A feasibility and dosimetric evaluation.
        Radiat Oncol. 2016; 11https://doi.org/10.1186/s13014-016-0684-9
        • Kavanaugh JA
        • Holler S
        • DeWees TA
        • et al.
        Multi-institutional validation of a knowledge-based planning model for patients enrolled in RTOG 0617: Implications for plan quality controls in Cooperative Group Trials.
        Pract Radiat Oncol. 2019; 9: e218-e227https://doi.org/10.1016/j.prro.2018.11.007
        • Ueda Y
        • Fukunaga J
        • Kamima T
        • Adachi Y
        • Nakamatsu K
        • Monzen H
        Evaluation of multiple institutions’ models for knowledge-based planning of volumetric modulated arc therapy (VMAT) for prostate cancer.
        Radiat Oncol. 2018; 13https://doi.org/10.1186/s13014-018-0994-1
      3. UCSD RapidPlan models—UCSD Knowledge-based radiotherapy initiative. http://ucsd.knowledgebasedrt.org/ucsdmodels. Accessed September 20, 2019.

        • Torrens M
        • Chung C
        • Chung H-T
        • et al.
        Standardization of terminology in stereotactic radiosurgery: Report from the Standardization Committee of the International Leksell Gamma Knife Society: special topic.
        J Neurosurg. 2014; 121https://doi.org/10.3171/2014.7.GKS141199