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基于细胞核群体的肿瘤定量异质性评估。

Quantitative tumor heterogeneity assessment on a nuclear population basis.

作者信息

Wessel Lindberg Anne-Sofie, Conradsen Knut, Larsen Rasmus, Friis Lippert Michael, Røge Rasmus, Vyberg Mogens

机构信息

DTU Compute, Technical University of Denmark, DK-2800, Kongens Lyngby, Denmark.

Visiopharm, DK-2970, Hoersholm, Denmark.

出版信息

Cytometry A. 2017 Jun;91(6):574-584. doi: 10.1002/cyto.a.23047. Epub 2017 Jan 31.

Abstract

Immunohistochemistry Ki-67 stain is widely used for visualizing cell proliferation. The common method for scoring the proliferation is to manually select and score a hot spot. This method is time-consuming and will often not give reproducible results due to subjective selection of the hotspots and subjective scoring. An automatic hotspot detection and proliferative index scoring would be time-saving, make the determination of the Ki-67 score easier and minimize the uncertainty of the score by introducing a more objective and standardized score. Tissue Micro Array cores stained for Ki-67 and their neighbor slide stained for Pan Cytokeratin were aligned and Ki-67 positive and negative nuclei were identified inside tumor regions. A heatmap was calculated based on these and illustrates the distribution of the heterogenous response of Ki-67 positive nuclei in the tumor tissue. An automatic hot spot detection was developed and the Ki-67 score was calculated. All scores were compared with scores provided by a pathologist using linear regression models. No significant difference was found between the Ki-67 scores guided by the developed heatmap and the scores provided by a pathologist. For comparison, scores were also calculated at a random place outside the hot spot and these scores were found to be significantly different from the pathologist scores. A heatmap visualizing the heterogeneity in tumor tissue expressed by Ki-67 was developed and used for an automatic identification of hot spots in which a Ki-67 score was calculated. The Ki-67 scores did not differ significantly from scores provided by a pathologist. © 2017 International Society for Advancement of Cytometry.

摘要

免疫组织化学Ki-67染色广泛用于可视化细胞增殖。对增殖进行评分的常用方法是手动选择并对一个热点进行评分。这种方法耗时且由于热点的主观选择和主观评分,往往无法给出可重复的结果。自动热点检测和增殖指数评分将节省时间,使Ki-67评分的确定更容易,并通过引入更客观和标准化的评分来最小化评分的不确定性。对Ki-67染色的组织微阵列核心及其对全细胞角蛋白染色的相邻玻片进行对齐,并在肿瘤区域内识别Ki-67阳性和阴性细胞核。基于这些数据计算出一个热图,该热图说明了肿瘤组织中Ki-67阳性细胞核的异质性反应分布。开发了一种自动热点检测方法并计算了Ki-67评分。使用线性回归模型将所有评分与病理学家提供的评分进行比较。在由开发的热图指导的Ki-67评分与病理学家提供的评分之间未发现显著差异。为了进行比较,还在热点之外的随机位置计算了评分,发现这些评分与病理学家的评分有显著差异。开发了一种可视化由Ki-67表达的肿瘤组织异质性的热图,并用于自动识别计算Ki-67评分的热点。Ki-67评分与病理学家提供的评分没有显著差异。© 2017国际细胞计量学促进协会。

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