Brey Eric M, Lalani Zahid, Johnston Carol, Wong Mark, McIntire Larry V, Duke Pauline J, Patrick Charles W
Laboratory of Reparative Biology and Bioengineering, Department of Plastic Surgery, University of Texas M. D. Anderson Cancer Center and University of Texas Center for Biomedical Engineering, Houston 77030, USA.
J Histochem Cytochem. 2003 May;51(5):575-84. doi: 10.1177/002215540305100503.
The increased use of immunohistochemistry (IHC) in both clinical and basic research settings has led to the development of techniques for acquiring quantitative information from immunostains. Staining correlates with absolute protein levels and has been investigated as a clinical tool for patient diagnosis and prognosis. For these reasons, automated imaging methods have been developed in an attempt to standardize IHC analysis. We propose a novel imaging technique in which brightfield images of diaminobenzidene (DAB)-labeled antigens are converted to normalized blue images, allowing automated identification of positively stained tissue. A statistical analysis compared our method with seven previously published imaging techniques by measuring each one's agreement with manual analysis by two observers. Eighteen DAB-stained images showing a range of protein levels were used. Accuracy was assessed by calculating the percentage of pixels misclassified using each technique compared with a manual standard. Bland-Altman analysis was then used to show the extent to which misclassification affected staining quantification. Many of the techniques were inconsistent in classifying DAB staining due to background interference, but our method was statistically the most accurate and consistent across all staining levels.
免疫组织化学（IHC）在临床和基础研究中的使用日益增加，这促使了从免疫染色中获取定量信息的技术的发展。染色与绝对蛋白质水平相关，并已作为患者诊断和预后的临床工具进行研究。出于这些原因，人们开发了自动成像方法，试图使免疫组织化学分析标准化。我们提出了一种新颖的成像技术，可以将二氨基联苯胺（DAB）标记抗原的明场图像转换为标准化的蓝色图像，从而能够自动识别阳性染色组织。一项统计分析通过测量我们的方法与之前发表的七种成像技术与两名观察者的手动分析的一致性，来对它们进行比较。使用了18张显示不同蛋白质水平的DAB染色图像。通过计算与手动标准相比，每种技术误分类像素的百分比来评估准确性。然后使用布兰德-奥特曼分析来显示误分类对染色定量的影响程度。由于背景干扰，许多技术在对DAB染色进行分类时并不一致，但我们的方法在统计学上是最准确的，并且在所有染色水平上都是一致的。
