A Computational Model-Enhanced Approach for Tumor Localization During Lumpectomy

 描述(申请人提供)：在过去的几十年里，已经证明，当获得清晰的切缘时，乳房肿块切除术与乳房切除术(全切缘或根治术)一样有效，也就是说，切缘内没有明显的癌症，因为切除了被包裹的癌组织。具体地说，清晰切缘的5年复发率约为2-7%(与更激进的手术相同)，而如果切缘为正，风险将增加到22%。术中确定手术切缘的困难，即肿瘤的定位，是几何和空间线索在手术中很快就消失了 这与大多数诊断性影像研究的悬挂式演示有很大不同。开发可以提高切除保真度的外科技术将对这一相当大的患者群体产生巨大影响(仅在美国每年就有近23万名女性被发现，80%的患者是在0、1或2期被发现的)，特别是在考虑复发率的情况下。将软组织变形的生物力学计算模型与术中指导和成像技术相结合，创建交互式显示，帮助导航和定位肿瘤，将是改善肿块切除术结果的重要一步。我们在这一应用中的假设是，肿瘤定位是改进MR驱动的切除以及其他补充疾病检测方法(例如，放射导向探头、光学成像技术等)的主要混杂因素。该应用程序的具体目标是：(1)生成具有伴随的计算模型增强方法的平台技术，以将术前MR成像数据对准外科领域进行BCS，以及(2)使用两个旁观者获取可行性数据 患者研究(n=6)测量乳房畸形的程度和性质，并评估最初的模型增强的登记框架。

 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.