组织脱钙对通过数字图像分析定量乳腺癌生物标志物的影响。
Effects of tissue decalcification on the quantification of breast cancer biomarkers by digital image analysis.
作者信息
Gertych Arkadiusz, Mohan Sonia, Maclary Shawn, Mohanty Sambit, Wawrowsky Kolja, Mirocha James, Balzer Bonnie, Knudsen Beatrice S
机构信息
Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Departments of Surgery, Cedars Sinai Medical Center, 116N Robertson Blvd. Suite 903, Los Angeles, CA, 90048, USA.
出版信息
Diagn Pathol. 2014 Nov 25;9:213. doi: 10.1186/s13000-014-0213-9.
BACKGROUND
Recent technical advances in digital image capture and analysis greatly improve the measurement of protein expression in tissues. Breast cancer biomarkers provide a unique opportunity to utilize digital image analysis to evaluate sources of variability that are caused by the tissue preparation, in particular the decalcification treatment associated with the analysis of bone metastatic breast cancer, and to develop methods for comparison of digital data and categorical scores rendered by pathologists.
METHODS
Tissues were prospectively decalcified for up to 24 hours and stained by immunohistochemistry (IHC) for ER, PR, Ki-67 and p53. HER2 positive breast cancer sections were retrieved from the pathology archives, and annotated with the categorical HER2 expression scores from the pathology reports. Digital images were captured with Leica and Aperio slide scanners. The conversion of the digital to categorical scores was accomplished with a Gaussian mixture model and tested for accuracy by comparison to clinical scores.
RESULTS
We observe significant effects of the decalcification treatment on common breast cancer biomarkers that are used in the clinic. ER, PR and p53 staining intensities decreased 15 - 20%, whereas Ki-67 decreased > 90% during the first 6 hrs of treatment and stabilized thereafter. In comparison with the Aperio images, pixel intensities generated by the Leica system are lower. A novel statistical model for conversion of digital to categorical scores provides a systematic approach for conversion of nuclear and membrane stains and demonstrated a high concordance with clinical scores.
CONCLUSION
Digital image analysis greatly improves the quantification of protein expression in human tissues. Decalcification affects the accuracy of immunohistochemical staining results and cannot be reversed by image analysis. Measurement data obtained on a continuous scoring scale can be converted to categorical scores for comparison with categorical dataset that are generated by pathologists.
VIRTUAL SLIDES
The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_213.
背景
数字图像采集与分析方面的最新技术进展极大地改进了组织中蛋白质表达的测量。乳腺癌生物标志物为利用数字图像分析来评估由组织制备导致的变异性来源提供了独特机会，尤其是与骨转移性乳腺癌分析相关的脱钙处理，并开发用于比较数字数据和病理学家给出的分类评分的方法。
方法
组织前瞻性脱钙长达24小时，并通过免疫组织化学（IHC）检测雌激素受体（ER）、孕激素受体（PR）、Ki-67和p53。从病理档案中检索HER2阳性乳腺癌切片，并用病理报告中的分类HER2表达评分进行注释。用徕卡和Aperio玻片扫描仪采集数字图像。通过高斯混合模型将数字评分转换为分类评分，并与临床评分比较以测试准确性。
结果
我们观察到脱钙处理对临床中使用 的常见乳腺癌生物标志物有显著影响。在处理的前6小时内，ER、PR和p53染色强度下降了15 - 20%，而Ki-67下降超过90%，此后趋于稳定。与Aperio图像相比，徕卡系统生成的像素强度较低。一种将数字评分转换为分类评分的新型统计模型为核染色和膜染色的转换提供了系统方法，并与临床评分显示出高度一致性。
结论
数字图像分析极大地改进了人体组织中蛋白质表达的定量分析。脱钙会影响免疫组织化学染色结果的准确性，且无法通过图像分析逆转。以连续评分量表获得的测量数据可转换为分类评分，以便与病理学家生成的分类数据集进行比较。
虚拟切片
本文的虚拟切片可在此处找到：http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_213 。
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