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

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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.

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Correspondence to David Thanoon .

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Thanoon, D., Garbey, M., Kim, NH., Bass, B. (2010). A Computational Framework for Breast Surgery: Application to Breast Conserving Therapy. In: Garbey, M., Bass, B., Collet, C., Mathelin, M., Tran-Son-Tay, R. (eds) Computational Surgery and Dual Training. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1123-0_15

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  • DOI: https://doi.org/10.1007/978-1-4419-1123-0_15

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  • Online ISBN: 978-1-4419-1123-0

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