Our goal is to design a decision support system to help breast cancer survivors understand their likely appearance changes following breast reconstruction and, therefore, enable them to choose a reconstruction strategy that will lead to maximal psychosocial adjustment. Our decision support system will employ state of the art 3D modeling and visualization technologies and will be founded on a sophisticated understanding of the complex body image considerations faced by breast cancer survivors. To achieve our goal, we must make fundamental advances that cross the disciplines of biomechanics, image processing, and surgery. Thus, our specific aims are: (1) To design objective and quantitative measures of breast morphology and appearance change that can be automatically computed from 3D surface scans of the human torso. While numerous anthropometric measures (such as distances) have been proposed without revolutionizing surgical planning or assessment, we will create measures of properties such as curvature that have been largely ignored in prior work and we will develop algorithms that will enable fully automated analyses (i.e., through automatic localization of fiducial points, i.e., anatomical landmarks such as nipples, sternal notch, etc.). (2) To develop mechanistic models of the human breast which are grounded in physics and parameterized using experimental measurements of the material properties of human tissues and biocompatible materials used in some reconstructive surgeries. These models will produce realistic predictions of post-operative appearance, including variation with patient pose, which cannot be achieved by simplistic morphing strategies that fail to account for natural constraints on the human form. (3) To develop a prototype patient decision support system that will provide breast cancer survivors with personalized information regarding their probable appearance changes following breast reconstruction. A decision support system is a sophisticated tool that helps a person considers multiple criteria in order to make a choice from among alternatives. A decision support system based on personalized simulations and measurements will facilitate communication between the patient and her healthcare team, allowing the patient to calibrate her expectations and be more comfortable with her treatment choices. The system design will be based on statistical relationships between our measures of breast morphology and patient characteristics, surgical variables, and resulting body image dissatisfaction. In the proposed study, the prototype decision support system will be evaluated and refined based on testing by healthcare professionals. If this project is successful, future studies would be designed to assess the impact of the decision support system when used by breast cancer survivors.

Public Health Relevance

Our goal is to design a computer system that will assist breast cancer survivors in understanding their likely appearance changes following breast reconstruction and, therefore, enable them to choose a reconstruction strategy that will lead to maximal psychosocial adjustment.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Biomedical Computing and Health Informatics Study Section (BCHI)
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O'Mara, Ann M
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University of Texas Austin
Biomedical Engineering
Schools of Engineering
United States
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Teo, Irene; Reece, Gregory P; Huang, Sheng-Cheng et al. (2018) Body image dissatisfaction in patients undergoing breast reconstruction: Examining the roles of breast symmetry and appearance investment. Psychooncology 27:857-863
Reddy, Jay P; Lei, Xiudong; Huang, Sheng-Cheng et al. (2017) Quantitative Assessment of Breast Cosmetic Outcome After Whole-Breast Irradiation. Int J Radiat Oncol Biol Phys 97:894-902
Kumaraswamy, N; Khatam, Hamed; Reece, Gregory P et al. (2017) Mechanical response of human female breast skin under uniaxial stretching. J Mech Behav Biomed Mater 74:164-175
Huang, Sheng-Cheng; Lee, Sara; Wang, Allen et al. (2016) UT Biomedical Informatics Lab (BMIL) Probability Wheel. SoftwareX 5:211-215
Li, Danni; Cheong, Audrey; Reece, Gregory P et al. (2016) Computation of breast ptosis from 3D surface scans of the female torso. Comput Biol Med 78:18-28
Teo, Irene; Reece, Gregory P; Christie, Israel C et al. (2016) Body image and quality of life of breast cancer patients: influence of timing and stage of breast reconstruction. Psychooncology 25:1106-12
Lee, Juhun; Kim, Edward; Reece, Gregory P et al. (2015) Automated calculation of ptosis on lateral clinical photographs. J Eval Clin Pract 21:900-10
Chua, Alicia S; DeSantis, Stacia M; Teo, Irene et al. (2015) Body image investment in breast cancer patients undergoing reconstruction: taking a closer look at the Appearance Schemas Inventory-Revised. Body Image 13:33-7
Khatam, Hamed; Reece, Gregory P; Fingeret, Michelle C et al. (2015) In-vivo quantification of human breast deformation associated with the position change from supine to upright. Med Eng Phys 37:13-22
Reece, Gregory P; Merchant, Fatima; Andon, Johnny et al. (2015) 3D surface imaging of the human female torso in upright to supine positions. Med Eng Phys 37:375-83

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