Renewal: Terahertz Polarization Imaging for Detecting Breast Tumor Margins

项目总结/摘要 保乳治疗（乳房肿瘤切除术）是最常见的乳腺癌手术之一 在美国乳房肿瘤切除术的最佳结果是在手术切缘 没有癌症当首次手术后在手术边缘检测到癌症残留时， 需要进行手术切除癌细胞。不幸的是，相当多的患者接受乳房 在当地医院进行的保留性手术（BCS），无法获得即时的现场病理结果， 再次切除或再次手术率高（大于30%）。因此，对新的 可以为当地医院和门诊诊所提供的术中技术。我们以前的 研究得出的结论是，虽然太赫兹在临床前模型中的研究表明， 癌组织和脂肪组织之间的临床相关性越高， 组织仍然具有挑战性。为了进一步建立在我们以前的奖项的成功，并提高敏感性， 太赫兹成像在手术边缘的癌症检测，我们已经确定了我们可以 显著改善仪器、动物模型和图像分析算法。 作为更新申请的一部分，我们将重新设计我们的仪器，以开发太赫兹偏振- 灵敏的成像方法。在这种新方法中，波的所有四种偏振都将被合并， 增加关于不同类型肿瘤组织的空间和光谱信息（目标1）。我们将测试 系统在致癌物诱导大鼠乳腺癌模型中体内并使用生物组织模拟 幻影，以确定信号产生的太赫兹图像（目标2）的来源。最后，我们将改善 检测算法的准确性，通过利用空间信息嵌入在太赫兹图像与空间 统计（目标3）。目标是更好地检测健康纤维组织的存在，因为它们具有潜在的生长潜力 癌组织附近的健康胶原蛋白。我们预计，新的拟议办法将 增加癌组织和健康邻近组织之间的图像对比度，从而更好地区分 和肿瘤边缘的癌症分类。这一更新应用程序将使我们能够开发一个优化的 利用多种偏振的方法，太赫兹图像中编码的空间信息用于 分析，并验证我们的方法对大鼠乳腺肿瘤。建议的成功 研究将使我们能够将我们的工作扩展到临床试验，使用临床平移和兼容的太赫兹 技术.

Project Summary/Abstract Breast-conserving therapy (lumpectomy) is one of the most commonly performed breast cancer surgeries in the United States. The best outcome of the lumpectomy surgery is achieved when the surgical margins are free of cancer. When remnants of cancer are detected at the surgical margins after the initial operation, a second operation will be required to remove the cancer. Unfortunately, a significant number of patients undergo breast conserving surgery (BCS) at local hospitals that do not have access to immediate on-site pathology leading to high rates of re-excision or reoperation (greater than 30%). Therefore, there is a significant need for new intraoperative technology that can be made available for local hospitals and outpatient clinics. Our previous research concludes that while terahertz studies in pre-clinical models have shown strong differentiation between cancerous and fatty tissues, the more clinically relevant differentiation between cancerous and healthy non-fatty tissue remains challenging. To further build upon the successes of our previous award and improve the sensitivity of terahertz imaging cancer detection on the surgical margins, we have identified areas where we can significantly improve the instrumentation, the animal model, and the image analysis algorithms. As part of this renewal application, we will re-design our instrumentation to develop terahertz polarization- sensitive imaging methodology. In this new approach, all four polarizations of the waves will be incorporated to increase the spatial and spectral information about different types of tumor tissues (Aim 1). We will test the system in vivo in a carcinogen-induced model of breast cancer in rats and use biological tissue simulating phantoms to determine the sources of signal generation in the THz images (Aim 2). Finally, we will improve the detection algorithm accuracy by exploiting the spatial information embedded in the terahertz images with spatial statistics (Aim 3). The goal is to better detect the presence of healthy fibrous tissue due to their potential growth of healthy collagen adjacent to cancerous tissues in tumors. We anticipate that the new proposed approach will increase the image contrast between cancerous and healthy adjacent tissues, leading to better differentiation and classification of cancer on the tumor margins. This renewal application will allow us to develop an optimized approach that leverages multiple polarizations, the spatial information encoded in the terahertz images for analysis, and the validation of our approach on mammary tumors from rats. The success of the proposed research will allow us to expand our work to clinical trials using clinically translational and compatible terahertz technology.