Medical Oncology, National Center for Tumor Diseases, University of Heidelberg, Heidelberg, Germany.
PLoS One. 2009 Nov 16;4(11):e7847. doi: 10.1371/journal.pone.0007847.
Determining the correct number of positive immune cells in immunohistological sections of colorectal cancer and other tumor entities is emerging as an important clinical predictor and therapy selector for an individual patient. This task is usually obstructed by cell conglomerates of various sizes. We here show that at least in colorectal cancer the inclusion of immune cell conglomerates is indispensable for estimating reliable patient cell counts. Integrating virtual microscopy and image processing principally allows the high-throughput evaluation of complete tissue slides.
METHODOLOGY/PRINCIPAL FINDINGS: For such large-scale systems we demonstrate a robust quantitative image processing algorithm for the reproducible quantification of cell conglomerates on CD3 positive T cells in colorectal cancer. While isolated cells (28 to 80 microm(2)) are counted directly, the number of cells contained in a conglomerate is estimated by dividing the area of the conglomerate in thin tissues sections (< or =6 microm) by the median area covered by an isolated T cell which we determined as 58 microm(2). We applied our algorithm to large numbers of CD3 positive T cell conglomerates and compared the results to cell counts obtained manually by two independent observers. While especially for high cell counts, the manual counting showed a deviation of up to 400 cells/mm(2) (41% variation), algorithm-determined T cell numbers generally lay in between the manually observed cell numbers but with perfect reproducibility.
In summary, we recommend our approach as an objective and robust strategy for quantifying immune cell densities in immunohistological sections which can be directly implemented into automated full slide image processing systems.
在结直肠癌和其他肿瘤实体的免疫组织化学切片中确定阳性免疫细胞的正确数量，正成为个体患者的重要临床预测因子和治疗选择。这一任务通常会受到各种大小的细胞团块的阻碍。我们在此表明，至少在结直肠癌中，包含免疫细胞团块对于估计可靠的患者细胞计数是必不可少的。整合虚拟显微镜和图像处理技术原则上允许对完整的组织切片进行高通量评估。
方法/主要发现：对于这种大规模系统，我们展示了一种稳健的定量图像处理算法，用于可重复地对结直肠癌中 CD3 阳性 T 细胞的细胞团块进行定量。虽然孤立细胞（28 至 80 微米）可以直接计数，但通过将团块的面积除以在薄组织切片（<或=6 微米）中单个 T 细胞的中位数面积来估计团块中包含的细胞数量。我们将我们的算法应用于大量的 CD3 阳性 T 细胞团块，并将结果与由两位独立观察者手动获得的细胞计数进行比较。虽然特别是对于高细胞计数，手动计数显示出高达 400 个细胞/mm2（41%的差异）的偏差，算法确定的 T 细胞数量通常介于手动观察到的细胞数量之间，但具有完美的可重复性。
总之，我们建议将我们的方法作为一种客观而稳健的策略，用于量化免疫组织化学切片中的免疫细胞密度，该策略可直接应用于自动化全切片图像处理系统。
