A Computational Framework for Breast Surgery: Application to Breast Conserving Therapy

  • David Thanoon
  • Marc Garbey
  • Nam-Ho Kim
  • Barbara Bass
Chapter

Abstract

If a surgical intervention is needed, early stage breast cancer may lead to three basic surgery choices: breast-sparing surgery followed by radiation therapy, mastectomy, mastectomy with breast reconstruction surgery. Breast-sparing surgery (breast conservation therapy (BCT)) removes the breast tumor and a margin of surrounding normal tissues. It is also known by other names: lumpectomy, partial mastectomy, segmental mastectomy, and quadrantectomy. Radiation therapy follows lumpectomy to eliminate any microscopic cancer cells in the remaining breast tissue. The purpose of BCT is to give women the same cure rate they would have if they were treated with a mastectomy but to leave the breast intact, with an appearance and texture as close as possible to what they had before treatment. Trials for breast conservative therapy with patients affected by breast cancer (I and II) have demonstrated conclusively that BCT produces disease control and survival rates at least equivalent to those of mastectomy, and possibly better in the long run for patients with stage II [1]. BCT is combined to radiation therapy. While BCT removes the tissue that contain the tumor with a negative margin, radiotherapy insure that residual microscopic disease are controlled. Contra-indications to BCT are generally for patients with high probability of recurrence, high probability of normal tissue damage from irradiation. While cosmesis after BCT might be generally satisfactory, the quality of the result is very sensitive to the location and extent of the tumor. Further, the breast is a very deformable structure with a complicated anatomy that is patient specific. The mechanical properties of glandular, fatty and cancerous tissue are quite different, and vary from one patient to another. The Cooper’s ligament also plays a key role in the outcome. Strong asymmetry in the location or large tumor size are prone to anesthetic BCT result. Surgical results are also depending on the time scale. Beside the short time scale modeling that might be caught by the mechanical model, one can expect that inflammation, postsurgical radiotherapy, and healing dynamic can change significantly in the long time scale of the cosmetic outcome. In other words biology plays a role as well.

Keywords

Compressibility Incompressibility 

References

  1. 1.
    Benda RK et al (2004) Breast-conserving therapy (BCT) for early stage breast cancer. J Surg Oncol 85:14–27CrossRefGoogle Scholar
  2. 2.
    Sivaramakrishna R (2005) 3D breast image registration – a review. Technol Cancer Res Treat 4:39–48Google Scholar
  3. 3.
    Azar FS, Metaxas DN, Schnall MD (1999) A finite element model of the breast for predicting mechanical deformations during interventional procedures. Proc Int Soc Magn Reson Med 7:1084Google Scholar
  4. 4.
    Sciaretta J, Bishop J, Samani A, Plewes DB (1999) MR validation of soft tissue deformation as modeled by non-linear finite element analysis. Proc Int Soc Magn Reson Med 7:246Google Scholar
  5. 5.
    Williams C, Clymer B, Schmalbrock P (1999) Biomechanics of breast tissue: preliminary study of force-deformation relationship. Proc Int Soc Magn Reson Med 7:524Google Scholar
  6. 6.
    Azar FS, Metaxas DN, Schnall MD (2002) Methods for modeling and predicting mechanical deformations of the breast under external perturbations. Med Image Anal 6:1–27CrossRefGoogle Scholar
  7. 7.
    Rajagopal V, Chung JH, Bullivant D, Nielsen PMF, Nash MP (2007) Determining the finite elasticity reference state from a loaded configuration. Int J Numer Methods Eng 72:1434–1451MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Krosukop TA et al (1998) Elastic moduli of breast and prostate tissues under compression. Ultrason Imaging 20:260–274Google Scholar
  9. 9.
    Manduca A et al (2001) Magnetic resonance elastography: non-invasive mapping of tissue elasticity. Med Image Anal 5:237–254CrossRefGoogle Scholar
  10. 10.
    Rajagopal V, Lee A et al (2008) Creating individual-specific biomechanical models of the breast for medical image analysis. Acad Radiol 15:1425–1436CrossRefGoogle Scholar
  11. 11.
    Wolfram S (1994) Cellular automata and complexity, collected papers. Westview Press, Boulder, COMATHGoogle Scholar
  12. 12.
    Holland JH (1995) Hidden order: how adaptation builds complexity. Helix Books, Reading, MAGoogle Scholar
  13. 13.
    Deutsch A, Dormann S (2005) Cellular automaton modeling of biological pattern formation. Birkhäuser, BostonMATHGoogle Scholar
  14. 14.
    Hwang M, Garbey M, Berceli SA, Tran Son tay R, Ruled-based simulation of multi-cellular biological systems - A review of modeling techniques. Cell Mol Bioeng, in pressGoogle Scholar
  15. 15.
    Bailey AM, Thorne BC, Peirce SM (2007) Multi-cell agent-based simulation of the microvasculature to study the dynamics of circulating inflammatory cell trafficking. Ann Biomed Eng 35:916–936CrossRefGoogle Scholar
  16. 16.
    Mallet DG, De Pillis LG (2006) A cellular automata model of tumor-immune system interactions. J Theor Biol 239:334–350CrossRefGoogle Scholar
  17. 17.
    Mi Q, Rivire B, Clermont G, Steed DL, Vodovotz Y (2007) Agent-based model of inflammation and wound healing: insights into diabetic foot ulcer pathology and the role of transforming growth factor-β1, Wound Repair Regen 15:671–682CrossRefGoogle Scholar
  18. 18.
    Galle J, Hoffmann M, Aust G (2009) From single cells to tissue architecture-a bottom-up approach to modelling the spatio-temporal organisation of complex multi-cellular systems. J Math Biol 58:261–283MATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Thorne BC, Bailey AM, Peirce SM (2007) Combining experiments with multi-cell agent-based modeling to study biological tissue patterning. Brief Bioinform 8:245–257CrossRefGoogle Scholar
  20. 20.
    Berceli SA, Tran-Son-Tay R, Garbey M, Jiang Z (2009) Hemo-dynamically driven vein graft remodeling: a systems biology approach. Vascular 17:S2–S9Google Scholar
  21. 21.
    COMSOL 3.4, COMSOL Multiphysics. COMSOL Reaction Engineering LabGoogle Scholar
  22. 22.
    Schwartz J-M (2003) Calcul rapide de forces et de deformations mecaniques non-lineaires et visco-elastiques pour la simulation de chirurgie. PhD, University LavalGoogle Scholar
  23. 23.
    Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7:308–313MATHGoogle Scholar
  24. 24.
    Tanner C, Schnabel J, Hill D et al (2006) Factors influencing the accuracy of biomechanical models. Med Phys 33:1758–1769CrossRefGoogle Scholar
  25. 25.
    Ribba B, Colin T, Schnell S (2006) A multiscale mathematical model of cancer, and its use in analyzing irradiation therapies. Theor Biol Med Model 3:7CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • David Thanoon
    • 1
  • Marc Garbey
    • 1
  • Nam-Ho Kim
    • 2
  • Barbara Bass
    • 3
    • 4
  1. 1.Department of Computer ScienceUniversity of HoustonHoustonUSA
  2. 2.Department of Mechanic and Aerospace EngineeringUniversity of FloridaGainesvilleUSA
  3. 3.Department of Surgery at The Methodist HospitalHoustonUSA
  4. 4.Weill Medical College of Cornell UniversityNew YorkUSA

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