Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
Analyst. 2019 Apr 8;144(8):2635-2642. doi: 10.1039/c8an01782d.
Infrared (IR) spectroscopic imaging, utilizing both the molecular and structural disease signatures, enables extensive profiling of tumors and their microenvironments. Here, we examine the relative merits of using either the fingerprint or the high frequency regions of the IR spectrum for tissue histopathology. We selected a complex model as a test case, evaluating both stromal and epithelial segmentation for various breast pathologies. IR spectral classification in each of these spectral windows is quantitatively assessed by estimating area under the curve (AUC) of the receiver operating characteristic curve (ROC) for pixel level accuracy and images for diagnostic ability. We found only small differences, though some that may be sufficiently important in diagnostic tasks to be clinically significant, between the two regions with the fingerprint region-based classifiers consistently emerging as more accurate. The work provides added evidence and comparison with fingerprint region, complex models, and previously untested tissue type (breast) - that the use of restricted spectral regions can provide high accuracy. Our study indicates that the fingerprint region is ideal for epithelial and stromal models to obtain high pixel level accuracies. Glass slides provide a limited spectral feature set but provides accurate information at the patient level.
红外（IR）光谱成像利用分子和结构疾病特征，能够广泛分析肿瘤及其微环境。在这里，我们研究了使用 IR 光谱的指纹区或高频区进行组织组织病理学的相对优点。我们选择了一个复杂的模型作为测试案例，评估了各种乳腺病变的基质和上皮分割。通过估计接收者操作特征曲线（ROC）的曲线下面积（AUC）来对每个光谱窗口中的 IR 光谱分类进行定量评估，以评估像素级精度和图像的诊断能力。我们发现这两个区域之间只有很小的差异，尽管在某些诊断任务中，差异可能足够重要而具有临床意义，但基于指纹区域的分类器的差异始终更为准确。这项工作提供了额外的证据和比较，表明使用受限的光谱区域可以提供高精度。我们的研究表明，对于获得高像素级精度的上皮和基质模型，指纹区域是理想的选择。载玻片提供了有限的光谱特征集，但在患者水平提供了准确的信息。
