Stromal Filters in Automated Immunostain Scoring

Kunal Patel, A. Bui, G. Riedlinger, Y. Yagi
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引用次数: 2

Abstract

KI-67 is a marker for cell proliferation which binds a nuclear antigen and is thus a prime antibody in immunohistochemistry. Scoring methodologies derived from KI-67 immunostaining have shown promise as predictors of lethality in a range of cancers [1]. The most common scoring method is the labelling index, a ratio of KI-67 positive cells to the entire population [1]. Manual calculation of a labelling index can be a time-consuming process for pathologists, who count cells in a bright field. Automated scoring algorithms and programs have been written to generate labelling indices, but they are nonspecific with respect to cell type and most often skew results by including stromal cells in the index. This problem is particularly relevant in breast cancer, where tumors are often located in a field of fibroadipose tissue. We have adapted algorithms for immunostain scoring and tested 2 stromal cell filtration algorithms which remove these cells based on their elongated nuclear morphology.
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间质过滤器在自动免疫染色评分中的应用
KI-67是细胞增殖的标志物,它与核抗原结合,因此是免疫组织化学中的主要抗体。KI-67免疫染色的评分方法已显示出作为一系列癌症致死率预测指标的前景[1]。最常见的评分方法是标记指数,即KI-67阳性细胞与总体的比例[1]。对于病理学家来说,手动计算标记指数可能是一个耗时的过程,他们在明亮的视野中计数细胞。已经编写了自动评分算法和程序来生成标记索引,但它们对于细胞类型是非特异性的,并且通常通过在索引中包含基质细胞来扭曲结果。这个问题与乳腺癌尤其相关,因为乳腺癌的肿瘤通常位于纤维脂肪组织区。我们已经适应了免疫染色评分的算法,并测试了2种基质细胞过滤算法,这些算法根据细胞的细长核形态去除这些细胞。
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