Skip to main content
SpringerLink
Log in

Quantitative assessment of the efficacy of two different treatment plan optimization algorithms in treating tumors in locations of high heterogeneity

  • Original Research
  • Published:
Journal of Radiation Oncology

Abstract

Purpose

The purpose of this study is to evaluate the differences in treatment optimization algorithms in two leading treatment planning systems, Pinnacle v9.8 (Philips Healthcare, Amsterdam, Netherlands) and Raystation 8A (Raysearch Americas Inc., Garden City, USA). The aim is to compare and contrast between planning systems in terms of sparing of vital organs (SVO) using several gradient and conformity indices, as well as vital organ dose limits.

Methods

The study includes patients (N = 18) presenting with lung (10), liver (4), and head and neck (4) tumors and treated with intensity modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT) planned using the same objectives and weights, number of iterations, beam angles, and planning target volume (PTV) coverage. Both plans were analyzed for Radiation Therapy Oncology Group (RTOG) conformity index, Paddick conformity index, gradient index (GI), dose gradient index, and sparing of vital organs. This study utilized both segmented and dynamic approaches to multileaf collimation (SMLC and DMLC, respectively) in the Raystation planning system in lung IMRT plans, as some plans could not be optimized in Raystation with SMLC (7).

Results

It was determined that in lung plans, Pinnacle demonstrated better sparing of the right lung and the spinal cord, but Raystation more effectively spared the heart and esophagus. In liver plans, Raystation demonstrated a superior GI, indicating faster dose falloff; this correlated with lower volumes of the liver receiving 24 Gy. In head and neck (H&N) plans, Pinnacle demonstrated superior sparing of the parotid but inferior GI, indicating more rapid dose falloff in Raystation beyond the PTV.

Conclusions

The analysis for the lung, liver, and H&N cases indicated that both planning systems are equivocal for the majority of parameters measured, with a few differentiating trends. In H&N plans, Pinnacle showed improved parotid sparing but inferior performance in calculated GI values. Liver plans showed superiority of Raystation in GI computations, but the most notable differences were in the lung plans where Pinnacle spared the spinal cord significantly more in contrast to Raystation’s performance but also delivered significantly more dose to the esophagus.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Abbreviations

PCI:

Paddick conformity index

RTOGCI:

RTOG conformity index

CN:

conformation number

CNS:

central nervous system

CT:

computed tomography

CTV:

clinical target volume

IDL:

isodose line

DGI:

dose gradient index

DVH:

dose-volume histogram

DMLC:

dynamic multileaf collimator

GI:

gradient index

GTV:

gross tumor volume

IMRT:

intensity modulated radiation therapy

LL:

left lung

LP:

left parotid

MLC:

multileaf collimator

OAR:

organ at risk

PI:

prescription isodose

PIV:

prescription isodose volume

PIV50% :

volume of the 50% prescription isodose

PTV:

planning target volume

Reff,50%Rx :

effective radius of the 50% prescription isodose

Reff,Rx :

effective radius of the prescription isodose

RL:

right lung

RP:

right parotid

ROI:

region of interest

RTOG:

Radiation Therapy Oncology Group

SMLC:

segmented multileaf collimator

SRS:

stereotactic radiosurgery

SVO:

sparing of vital organs

TPS:

treatment planning system

TV:

target volume, defined as the PTV for this study

TVPIV :

volume of prescription isodose located within the target volume

VOAR:

volume of organ at risk

References

  1. Siegel RL, Miller KD, Jemal A (2017) Cancer statistics, 2017. CA Cancer J Clin 67:7–30. https://doi.org/10.3322/caac.21387

    Article  PubMed  Google Scholar 

  2. Barton MB, Jacob S, Shafiq J, Wong K, Thompson SR, Hanna TP, Delaney GP (2014) Estimating the demand for radiotherapy from the evidence: a review of changes from 2003 to 2012. Radiother Oncol 112:140–144. https://doi.org/10.1016/j.radonc.2014.03.024

    Article  PubMed  Google Scholar 

  3. Jaffray DA, Gospodarowicz MK. (2015) Radiation Therapy for Cancer DCP3. doi: https://doi.org/10.1596/978-1-4648-0349-9_ch14

  4. Sterzing F, Stoiber EM, Nill S, Bauer H, Huber P, Debus J, Münter MW (2009) Intensity modulated radiotherapy (IMRT) in the treatment of children and adolescents - a single institution’s experience and a review of the literature. Radiat Oncol 4:37. https://doi.org/10.1186/1748-717x-4-37

    Article  PubMed  PubMed Central  Google Scholar 

  5. Teh BS, Woo SY, Butler EB (1999) Intensity modulated radiation therapy (IMRT): a new promising technology in radiation oncology. Oncologist 4:433–442

    CAS  PubMed  Google Scholar 

  6. Chen W-Z (2014) Impact of dose calculation algorithm on radiation therapy. World J Radiol 6:874–880. https://doi.org/10.4329/wjr.v6.i11.874

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Jothybasu K, Bahl A, Subramani V, Rath G, Sharma D, Julka P (2009) Static versus dynamic intensity-modulated radiotherapy: profile of integral dose in carcinoma of the nasopharynx. J Med Phys 34:66–72. https://doi.org/10.4103/0971-6203.51932

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Alaei P, Higgins PD, Weaver R, Nguyen N (2004) Comparison of dynamic and step-and-shoot intensity-modulated radiation therapy planning and delivery. Med Dosim 29:1–6. https://doi.org/10.1016/j.meddos.2003.10.002

    Article  PubMed  Google Scholar 

  9. Hall EJ (2006) Intensity-modulated radiation therapy, protons, and the risk of second cancers. Int J Radiat Oncol Biol Phys 65:1–7. https://doi.org/10.1016/j.ijrobp.2006.01.027

    Article  PubMed  Google Scholar 

  10. Shaw E, Kline R, Gillin M, Souhami L, Hirschfeld A, Dinapoli R, Martin L (1993) Radiation Therapy Oncology Group: radiosurgery quality assurance guidelines. Int J Radiat Oncol Biol Phys 27:1231–1239. https://doi.org/10.1016/0360-3016(93)90548-A

    Article  CAS  PubMed  Google Scholar 

  11. Feuvret L, Noël G, Mazeron JJ, Bey P (2006) Conformity index: a review. Int J Radiat Oncol Biol Phys 64:333–342. https://doi.org/10.1016/j.ijrobp.2005.09.028

    Article  PubMed  Google Scholar 

  12. Paddick I (2000) A simple scoring ratio to index the conformity of radiosurgical treatment plans: technical note. J Neurosurg 93:219–222. https://doi.org/10.3171/jns.2000.93.supplement

    Article  PubMed  Google Scholar 

  13. Paddick I, Lippitz B (2006) A simple dose gradient measurement tool to complement the conformity index. J Neurosurg 105(Suppl):194–201. https://doi.org/10.3171/sup.2006.105.7.194

    Article  PubMed  Google Scholar 

  14. Torrens M, Chung C, Chung HT, Hanssens P, Jaffray D, Kemeny A, Larson D, Levivier M, Lindquist C, Lippitz B, Novotny J, Paddick I, Prasad D, Yu CP (2014) Standardization of terminology in stereotactic radiosurgery: report from the Standardization Committee of the International Leksell Gamma Knife Society: special topic. J Neurosurg 121:2–15. https://doi.org/10.3171/2014.7.GKS141199

    Article  PubMed  Google Scholar 

  15. Sheth NS, Sim S, Cheng J, Lustgarten J, Estin D, Olson T, Weiss M, Murphy S, Chen Y, Yang J (2011) A dose gradient index for stereotactic radiosurgery/radiotherapy: evaluated with helical tomotherapy. Int J Radiat Oncol Biol Phys 81(2):S867–S868

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiation Oncology, University of Toledo Medical Center, 3000 Arlington Avenue, Toledo, OH, 43614, USA

    E. Ishmael Parsai, Vincent Ulizio, Jacob M. Eckstein & Krishna Reddy

Authors
  1. E. Ishmael Parsai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vincent Ulizio
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jacob M. Eckstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Krishna Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. Ishmael Parsai.

Ethics declarations

Funding

No funding was received for this study.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Statement of informed consent was not applicable since the manuscript does not contain any patient data.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Parsai, E.I., Ulizio, V., Eckstein, J.M. et al. Quantitative assessment of the efficacy of two different treatment plan optimization algorithms in treating tumors in locations of high heterogeneity. J Radiat Oncol 8, 233–238 (2019). https://doi.org/10.1007/s13566-019-00392-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13566-019-00392-0

Keywords

Search

Navigation