Over the past several decades, it has been shown that breast lumpectomy is as effective as mastectomy (total or radical) when clear margins are achieved, i.e. no cancer is evident in the margins as encapsulated cancerous breast tissue is resected. Specifically, clear margins have a 5-year recurrence rate of approximately 2-7% (the same as the more radical procedures) while that risk increases to as much as 22% if positive resection margins are present. The difficulty with determining surgical margins intraoperatively, i.e. tumor localization, is that geometric and spatial cues are quickly lost in the OR presentation which differs considerably from the pendant presentation for most diagnostic imaging studies. Generating surgical technologies that could improve the fidelity of resection would have dramatic impact to this considerable population of patients (nearly 230,000 women per year in the United States alone with 80% being detected at stages 0, 1, or 2) especially when considering recurrence rates. The combination of biomechanical computational models of soft tissue deformation with intraoperative guidance and imaging technologies to create interactive displays to help navigate and localize the tumor would be an important step forward in improving the outcomes of lumpectomy. Our hypothesis in this application is that tumor localization is a major confounding factor for improving MR-driven resections as well other complementary disease detection methods (e.g. radioguided probes, optical imaging technologies, etc.).
The specific aims of the application are: (1) generate a platform technology with accompanying computational model-enhanced approach to align preoperative MR imaging data to the surgical field for conducting BCS, and (2) acquire feasibility data using two bystander patient studies (n=6 each) measuring the extent and nature of breast deformations and assess initial model-enhanced registration framework.

Public Health Relevance

Over the past several decades, it has been shown that breast conservation therapy (usually consists of lumpectomy and radiation) is as effective as mastectomy (total or radical) when clear margins are achieved, i.e. no cancer is evident in the margins as encapsulated cancerous breast tissue is resected. Unfortunately, the re-operation rates reported in the literature to obtain a negative margin range from 17-59% and as a result can compromise this procedure. Generating surgical technologies that could improve the fidelity of resection would have dramatic impact to a considerable population of patients (nearly 230,000 women per year in the United States alone with 80% being detected at stages 0, 1, or 2) especially when considering average recurrence rates of 22% when positive margins are found. In this exploratory work, we will investigate the combination of computational modeling techniques with surgical navigation and interventional imaging technologies to improve localization of tumors intraoperatively for the purpose of improving the fidelity and reliability o single-pass negative-margin resection for breast conserving surgery (BCS).

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21EB022380-02
Application #
9306843
Study Section
Special Emphasis Panel (ZRG1-SBIB-Q (80)S)
Program Officer
Pai, Vinay Manjunath
Project Start
2016-07-01
Project End
2018-04-30
Budget Start
2017-05-01
Budget End
2018-04-30
Support Year
2
Fiscal Year
2017
Total Cost
$189,325
Indirect Cost
$64,325
Name
Vanderbilt University Medical Center
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
965717143
City
Nashville
State
TN
Country
United States
Zip Code
37240
Weis, Jared A; Miga, Michael I; Yankeelov, Thomas E (2017) Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy. Comput Methods Appl Mech Eng 314:494-512
Griesenauer, Rebekah H; Weis, Jared A; Arlinghaus, Lori R et al. (2017) Breast tissue stiffness estimation for surgical guidance using gravity-induced excitation. Phys Med Biol 62:4756-4776