A Computational Model-Enhanced Approach for Tumor Localization During Lumpectomy

 描述（由申请人提供）：在过去的几十年中，已经表明，当获得清晰的边缘时，乳房肿块切除术与乳房切除术（全部或根治性）一样有效，即，当切除包裹的癌性乳腺组织时，边缘中没有明显的癌症。具体来说，切缘清晰的5年复发率约为2-7%（与更激进的手术相同），而如果存在阳性切除边缘，则风险增加至22%。术中确定手术切缘（即肿瘤定位）的困难在于，几何和空间线索在手术室展示中很快丢失 这与大多数诊断成像研究的悬垂呈现有很大不同。产生可以提高切除保真度的手术技术将对这一可观的患者群体产生巨大影响（仅在美国每年就有近23万名女性，其中80%在0、1或2期被检测到），特别是在考虑复发率时。软组织变形的生物力学计算模型与术中引导和成像技术相结合，以创建交互式显示，以帮助导航和定位肿瘤，这将是改善肿块切除术结局的重要一步。我们在本申请中的假设是，肿瘤定位是改善MR驱动切除术以及其他互补疾病检测方法（例如，放射导向探针、光学成像技术等）的主要混杂因素。该应用的具体目的是：（1）生成一种平台技术，伴随计算模型增强方法，以将术前MR成像数据与手术野对齐，用于进行BCS，以及（2）使用两个旁观者采集可行性数据 患者研究（每个n=6）测量乳房变形的程度和性质，并评估初始模型增强配准框架。

 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.