Discrete Frequency Infrared Spectroscopic Imaging for Breast Histopathology

项目摘要 红外(IR)光谱成像直接测量每个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，而不需要 染料或染料的使用-为临床诊断和研究提供了一条潜在的新途径，以广泛帮助 公共卫生。由于机器学习是该方法的组成部分，对疾病特征的认知可以使 诊断更快、更便宜、更准确。有趣的是，这种方法可以测量肿瘤的分子 特征和微环境在一次拍摄中结合起来。这些功能可以扩展最先进的技术 通过提供多路无斑点分子数据和预测模型的病理学实践 来自多种细胞类型的化学信息。然而，存在着重大的挑战和工程 在实现这一愿景之前需要开发，包括：(A)在以下方面具有竞争力的成像系统 符合当前临床实践的测量时间，(B)准确和可靠的结果，使我们的能力超越 常规病理学，以及(C)病理学家和非技术专家的有力使用。在最后一次 在项目期间(在25个同行评审的出版物中报道，2个被授予专利)，我们开发了高清(HD) 红外成像，这是现在红外成像制造商的标准商业配置。我们也 提出了“不锈钢染色”的概念，即“低清晰度”红外图像看起来像低分辨率。 被污染的图像。我们还演示了对少数乳房组织进行高度准确的分类 病理学。在这个项目期间，我们提出了一种先进的红外成像系统(新设计的光学系统， 扫描)，以使该技术足够强大，以提供约10分钟的样本到图像的时间 外科手术切除。这允许实时高清成像，并将允许图像，例如来自不锈钢污渍的图像 接近临床医生和研究人员使用的那些产品的质量。技术创新在于设计是 40多年来首次创新的红外成像重新设计，在速度、精度和图像上都有更高的性能 质量比以往任何时候都要高。我们方法的另一个关键部分是开发适当的计算管道 乳房病理学中存在的问题。除了传统的模型外，我们还将验证DEEP的新兴工具 在适当的时候学习。最后，在这些技术实现之后，对一组 乳腺癌护理和研究中的重要问题。这些解决方案将经过严格的评估 病理学家诊断，使用高质量的注释数据，来自400名患者的手术切除和 组织微阵列。因此，针对乳房病理学中许多已识别的痛点的方案将会产生 除了技术进步，使该方法准备就绪可供使用。

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.