Label-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM).

Label-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM).
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DOI:
10.1038/s41598-018-25261-7
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发表时间:
2018-05-02
期刊:
影响因子:
4.6
通讯作者:
Popescu G
Popescu G
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Majeed H;Nguyen TH;Kandel ME;Kajdacsy-Balla A;Popescu G

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乳腺癌是全世界妇女中最常见的癌症类型。乳腺组织的标准组织病理学是疾病诊断的主要手段,包括病理学家对染色组织进行手动显微镜检查。由于该方法依赖于定性信息,因此可能导致观察者之间的差异。此外,对于困难的病例,病理学家通常需要额外的恶性肿瘤标志物来帮助诊断,这一需求可以通过新的显微镜方法来满足。我们提出了一种定量的方法,无标记乳腺组织的评价,使用空间光干涉显微镜(SLIM)。通过提取恶性肿瘤的组织标记物的基础上揭示的光路长度的纳米结构,我们的方法提供了一个客观的,无标记的和潜在的自动化的乳腺组织病理学的方法。我们通过对由68名不同受试者组成的组织微阵列进行成像来展示我们的方法,其中34名为恶性组织,34名为良性组织。三重交叉验证结果显示,检测癌症的灵敏度为94%,特异性为85%。我们的疾病特征代表了样本的内在物理属性,与染色质量无关,有助于通过机器学习包进行分类,因为我们的图像不会因扫描或仪器而异。
Breast cancer is the most common type of cancer among women worldwide. The standard histopathology of breast tissue, the primary means of disease diagnosis, involves manual microscopic examination of stained tissue by a pathologist. Because this method relies on qualitative information, it can result in inter-observer variation. Furthermore, for difficult cases the pathologist often needs additional markers of malignancy to help in making a diagnosis, a need that can potentially be met by novel microscopy methods. We present a quantitative method for label-free breast tissue evaluation using Spatial Light Interference Microscopy (SLIM). By extracting tissue markers of malignancy based on the nanostructure revealed by the optical path-length, our method provides an objective, label-free and potentially automatable method for breast histopathology. We demonstrated our method by imaging a tissue microarray consisting of 68 different subjects −34 with malignant and 34 with benign tissues. Three-fold cross validation results showed a sensitivity of 94% and specificity of 85% for detecting cancer. Our disease signatures represent intrinsic physical attributes of the sample, independent of staining quality, facilitating classification through machine learning packages since our images do not vary from scan to scan or instrument to instrument.
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