Discrete Frequency Infrared Spectroscopic Imaging for Breast Histopathology

项目摘要 红外（IR）光谱成像直接测量细胞和组织的化学成分， 图像中的像素。使用机器学习，这些化学数据可以转换为病理学知识，而无需 染料或染色剂的使用-为临床诊断和研究提供了一种潜在的新途径， 公共卫生由于机器学习是该方法的组成部分，因此对疾病特征的认知可以使 诊断更快、更便宜、更精确。有趣的是，这种方法可以测量肿瘤的分子 特征和微环境在一起。这些功能可以扩展现有技术 通过提供多路复用的无染色分子数据和预测模型， 多种细胞类型的化学信息。然而，存在重大挑战和工程 在实现这一愿景之前，需要进行以下开发：（a）在以下方面具有竞争力的成像系统 （B）准确和有保证的结果，扩展了我们的能力， 常规病理学，以及（c）病理学家和非技术专家的稳健使用证明。在过去 项目期间（报告在25同行评议的出版物，2授予专利），我们开发了“高清”（HD） 红外成像，现在是红外成像制造商的标准商业配置。我们也 开发了“不锈钢染色”的概念，其中“低清晰度”红外图像看起来像低分辨率 染色图像。我们还证明了对少数乳腺癌患者的高度准确的乳腺组织分类。 病理学在这个项目期间，我们提出了一个先进的红外成像系统（新设计的光学， 扫描），使该技术足够强大，可以为大型设备提供约10分钟的采样到成像时间 手术切除这允许在真实的时间中进行HD成像，并且将允许诸如来自不锈钢污渍的图像被 接近临床医生和研究人员使用的质量。技术创新在于设计， 40多年来首次对红外成像进行全新设计，在速度、精度和成像性能方面都更高 质量比以往任何时候。我们的方法的另一个关键部分是开发适当的计算流水线， 乳腺病理学中现存的问题。除了传统的模型，我们将验证新兴的工具， 适当的时候学习。最后，这些技术实现之后是对一组 乳腺癌治疗和研究中的重要问题。解决方案将根据 病理学家诊断，使用高质量，注释数据，从400名患者的手术切除和多个 组织微阵列因此，针对乳腺病理学中一些已确定的痛点的方案将产生 除了技术进步，使该方法随时可用。

PROJECT ABSTRACT Infrared (IR) spectroscopic imaging directly measures the chemical composition of cells and tissues for each pixel in the image. Using machine learning, this chemical data can be converted to pathology knowledge, without the use of dyes or stains – providing a potentially new avenue for clinical diagnoses and research to broadly aid public health. Since machine learning is integral to the approach, cognition of disease features can make diagnoses faster, cheaper and more precise. Interestingly, the approach can measure the tumor’s molecular characteristics and the microenvironment together in one shot. These capabilities can extend state of the art pathology practice by providing multiplexed stain-free molecular data and predictive models involving spatial and chemical information from multiple cell types. However, there are significant challenges and engineering development needed before this vision can be realized, including: (a) an imaging system that is competitive in measurement time with current clinical practice, (b) accurate and assured results that extend our ability beyond routine pathology, and (c) demonstration of robust use by pathologists and non-experts in technology. In the last project period(reported in 25 peer-reviewed publications, 2 granted patents), we developed “high-definition” (HD) IR imaging, which is now the standard commercial configuration for IR imaging manufacturers. We also developed the concept of “stainless staining” in which “low-definition” IR images appear to look like low-resolution stained images. We also demonstrated highly accurate breast tissue classification for a small number of pathologies. In this project period, we propose an advanced IR imaging system (newly designed optics, scanning) to make the technology powerful enough to provide a sample-to-image time of ~10 min for large surgical resections. This allows HD imaging in real time and will allow images, such as from stainless stains, be near the quality of those used by clinicians and researchers. Technological innovations lie in a design that is the first novel re-design of IR imaging in over 40 years and performance that is higher in speed, accuracy and image quality than ever before. Another critical part of our approach is to develop appropriate computational pipelinesfor extant problems in breast pathology. In addition to traditional models, we will validate the emerging tools of deep learning when appropriate. Finally, these technological realizations are followed by validation for a set of important problems in breast cancer care and research. The solutions will be rigorously evaluated against pathologist diagnoses, using high-quality, annotated data from 400 patients’ surgical resections and multiple tissue microarrays. Consequently, protocols for a number of identified pain points in breast pathology will result in addition to the technological progress, making the approach ready for use.