Skip to main content
SpringerLink
Log in

Computer-Assisted Target Volume Determination

  • Chapter
  • First Online:
Image-Based Computer-Assisted Radiation Therapy

  • 1367 Accesses

Abstract

The gross tumor volume (GTV) regions are the fundamental regions used to determine the clinical target volumes (CTVs) and planning target volume (PTV). The accuracy of the GTVs may affect tumor control and adverse events related to organs at risk or normal tissue. The PTV is the volume that includes the CTV plus CTV-to-PTV margin including the internal margin (IM) and the setup margin (SM). This chapter introduces the computational segmentation approaches for GTV and computational determination of the CTV-to-PTV margin.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Aristophanous M, Penney BC, Martel MK, Pelizzari CA (2007) A Gaussian mixture model for definition of lung tumor volumes in positron emission tomography. Med Phys 34:4223–4235

    Article  PubMed  Google Scholar 

  • Belhassen S, Zaidi H (2010) A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET. Med Phys 37:1309–1324

    Article  PubMed  Google Scholar 

  • Biehl KJ, Kong FM, Dehdashti F et al (2006) 18F-FDG PET definition of gross tumor volume for radiotherapy of non-small cell lung cancer: is a single standardized uptake value threshold approach appropriate? J Nucl Med 47:1808–1812

    PubMed  Google Scholar 

  • Boellaard R (2009) Standards for PET image acquisition and quantitative data analysis. J Nucl Med 50(Suppl 1):11S–20S

    Article  CAS  PubMed  Google Scholar 

  • Bradley JD, Perez CA, Dehdashti F, Siegel BA (2004) Implementing biologic target volumes in radiation treatment planning for non-small cell lung cancer. J Nucl Med 45:96S–101S

    PubMed  Google Scholar 

  • Chan TF, Sandberg BY, Vese LA (2000) Active contours without edges for vector-valued images. J Vis Commun Image Represent 11:130–141

    Article  Google Scholar 

  • Chao KS, Bhide S, Chen H et al (2007) Reduce in variation and improve efficiency of target volume delineation by a computer-assisted system using a deformable image registration approach. Int J Radiat Oncol Biol Phys 68:1512–1521

    Article  PubMed  Google Scholar 

  • Chui H, Rangarajan A (2003) A new point matching algorithm for non-rigid registration. Comput Vis Image Underst 89:114–141

    Article  Google Scholar 

  • Crum WR, Camara O, Hill DLG (2006) Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans Med Imaging 25:1451–1461

    Article  PubMed  Google Scholar 

  • Day E, Betler J, Parda D, Reitz B, Kirichenko A, Mohammadi S, Miften M (2009) A region growing method for tumor volume segmentation on PET images for rectal and anal cancer patients. Med Phys 36:4349–4358

    Article  PubMed  Google Scholar 

  • El Naqa I, Yang D, Apte A et al (2007) Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning. Med Phys 34:4738–4749

    Article  PubMed  Google Scholar 

  • Gardner SJ, Wen N, Kim J, Liu C, Pradhan D, Aref I, Elshaikh MA (2015) Contouring variability of human-and deformable-generated contours in radiotherapy for prostate cancer. Phys Med Biol 60:4429–4447

    Article  PubMed  Google Scholar 

  • Geets X, Lee JA, Bol A, Lonneux M, Gregoire V (2007) A gradient-based method for segmenting FDG-PET images: methodology and validation. Eur J Nucl Med Mol Imaging 34:1427–1438

    Article  PubMed  Google Scholar 

  • Hatt M, Cheze Le Rest C, Albarghach N et al (2011) PET functional volume delineation: a robustness and repeatability study. Eur J Nucl Med Mol Imaging 38:3663–3672

    Google Scholar 

  • Ikushima K, Arimura H, Jin Z, Yabu-uchi H, Kuwazuru J, Shioyama Y, Sasaki T, Honda H, Sasaki M (2016) Computer-assisted framework for machine-learning-based delineation of GTV regions on datasets of planning CT and PET/CT images. J Radiat Res:57. doi:10.1093/jrr/rrw082

  • International Commission on Radiation Units and Measurements, (ICRU) (1993) Prescribing, recording, and reporting photon beam therapy, ICRU Report 50. ICRU, Bethesda

    Google Scholar 

  • International Commission on Radiation Units and Measurements, (ICRU) (1999) Prescribing, Recording and Reporting Photon Beam Therapy. (Supplement to ICRU Report 50), ICRU Report 62. ICRU, Bethesda

    Google Scholar 

  • Jin Z, Arimura H, Shioyama Y et al (2014) Computer-assisted delineation of lung tumor regions in treatment planning CT images with PET/CT image sets based on an optimum contour selection method. J Radiat Res 55(6):1153–1162

    Article  PubMed  PubMed Central  Google Scholar 

  • Kerhet A, Small C, Quon H et al (2010) Application of machine learning methodology for PET-based definition of lung cancer. Curr Oncol 17(1):41–47

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Leunens G, Menten J, Weltens C et al (1993) Quality assessment of medical decision making in radiation oncology: variability in target volume delineation for brain tumours. Radiother Oncol 29(2):169–175

    Article  CAS  PubMed  Google Scholar 

  • Niyazi M, Landrock S, Elsner A et al (2013) Automated biological target volume delineation for radiotherapy treatment planning using FDG-PET/CT. Radiat Oncol 8:180. doi:10.1186/1748-717X-8-180

    Article  PubMed  PubMed Central  Google Scholar 

  • Okada T, Shimada R, Hori M et al (2008) Automated segmentation of the liver from 3D CT images using probabilistic atlas and multilevel statistical shape model. Acad Radiol 15:1390–1403

    Article  PubMed  Google Scholar 

  • Rousson M, Khamene A, Diallo M, Carlos Celi J, Sauer F (2005) Constrained surface evolutions for prostate and bladder segmentation in CT images. Lect Notes Comput Sci (LNCS) 3765:251–260

    Article  Google Scholar 

  • Sethian JA (1999) Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science. Cambridge University Press, Cambridge, UK

    Google Scholar 

  • Söhn M, Birkner M, Yan D et al (2005) Modelling individual geometric variation based on dominant eigenmodes of organ deformation: implementation and evaluation. Phys Med Biol 50:5893–5908

    Article  PubMed  Google Scholar 

  • Söhn M, Alber M, Yan D (2007) Principal component analysis-based pattern analysis of dose-volume histograms and influence on rectal toxicity. Int J Radiat Oncol Biol Phys 69(1):230–239

    Article  PubMed  Google Scholar 

  • Söhn M, Sobotta B, Alber M (2012) Dosimetric treatment course simulation based on a statistical model of deformable organ motion. Phys Med Biol 57:3693–3709

    Article  PubMed  Google Scholar 

  • Stroom JC, Heijmen BJ (2002) Geometrical uncertainties, radiotherapy planning margins, and the ICRU-62 report. Radiother Oncol 64:75–83

    Article  PubMed  Google Scholar 

  • Thörnqvist S, Hysing LB, Zolnay AG et al (2013) Treatment simulations with a statistical deformable motion model to evaluate margins for multiple targets in radiotherapy for high-risk prostate cancer. Radiother Oncol 109(3):344–349

    Article  PubMed  Google Scholar 

  • Trefethen LN, Bau D III (1997) Numerical linear algebra. Society for Industrial and Applied Mathematics, Philadelphia

    Book  Google Scholar 

  • van de Steene J, Linthout N, de Mey J, Vinh-Hung V, Claassens C, Noppen M, Bel A, Storme G (2002) Definition of gross tumor volume in lung cancer: inter-observer variability. Radiother Oncol 62:37–49

    Article  PubMed  Google Scholar 

  • van der Wielen GJ, Mutanga TF, Incrocci L et al (2008) Deformation of prostate and seminal vesicles relative to intraprostatic fiducial markers. Int J Radiat Oncol Biol Phys 72:1604–1611

    Article  PubMed  Google Scholar 

  • van Herk M (2004) Errors and margins in radiotherapy. Semin Radiat Oncol 14(1):52–64

    Article  PubMed  Google Scholar 

  • van Herk M, Remeijer P, Rasch C et al (2000) The probability of correct target dosage: dose-population histograms for deriving treatment margins in radiotherapy. Int J Radiat Oncol Biol Phys 47:1121–1135

    Article  PubMed  Google Scholar 

  • Vapnik VN (1999) The nature of statistical learning theory, 2nd edn. Springer, New York

    Google Scholar 

  • Weiss E, Hess CF (2003) The impact of gross tumor volume (GTV) and clinical target volume (CTV) definition on the total accuracy in radiotherapy theoretical aspects and practical experiences. Strahlenther Onkol 179:21–30

    Article  PubMed  Google Scholar 

  • Xu H, Vile DJ, Sharma M et al (2014) Coverage-based treatment planning to accommodate deformable organ variations in prostate cancer treatment. Med Phys 41(10):101705. doi:10.1118/1.4894701

    Article  PubMed  PubMed Central  Google Scholar 

  • Xu H, Gordon JJ, Siebers JV (2015) Coverage-based treatment planning to accommodate delineation uncertainties in prostate cancer treatment. Med Phys 42(9):5435–5443

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhang T, Tachiya Y, Sakaguchi Y et al (2010) Phantom study on three-dimensional target volume delineation by PET/CT-based auto-contouring. Fukuoka Acta Med 101:238–246

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan

    Hidetaka Arimura

  2. Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan

    Yusuke Shibayama, Mohammad Haekal, Ze Jin & Koujiro Ikushima

Authors
  1. Hidetaka Arimura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yusuke Shibayama
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Haekal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ze Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Koujiro Ikushima
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hidetaka Arimura .

Editor information

Editors and Affiliations

  1. Department of Health Sciences, Kyushu University, Fukuoka, Japan

    Hidetaka Arimura

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this chapter

Cite this chapter

Arimura, H., Shibayama, Y., Haekal, M., Jin, Z., Ikushima, K. (2017). Computer-Assisted Target Volume Determination. In: Arimura, H. (eds) Image-Based Computer-Assisted Radiation Therapy. Springer, Singapore. https://doi.org/10.1007/978-981-10-2945-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-2945-5_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2943-1

  • Online ISBN: 978-981-10-2945-5

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation