A Computational Model-Enhanced Approach for Tumor Localization During Lumpectomy

Vanderbilt University, Nashville TN

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Abstract

? DESCRIPTION (provided by applicant): 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.