Abstract：
Introduction. The identification of mitotic figures is essential for the diagnosis, grading, and classification of various different tumors. Despite its importance, there is a paucity of literature reporting the consistency in interpreting mitotic figures among pathologists. This study leverages publicly accessible datasets and social media to recruit an international group of pathologists to score an image database of more than 1000 mitotic figures collectively. Materials and Methods. Pathologists were instructed to randomly select a digital slide from The Cancer Genome Atlas (TCGA) datasets and annotate 10-20 mitotic figures within a 2 mm2 area. The first 1010 submitted mitotic figures were used to create an image dataset, with each figure transformed into an individual tile at 40x magnification. The dataset was redistributed to all pathologists to review and determine whether each tile constituted a mitotic figure. Results. Overall pathologists had a median agreement rate of 80.2% (range 42.0%-95.7%). Individual mitotic figure tiles had a median agreement rate of 87.1% and a fair inter-rater agreement across all tiles (kappa = 0.284). Mitotic figures in prometaphase had lower percentage agreement rates compared to other phases of mitosis. Conclusion. This dataset stands as the largest international consensus study for mitotic figures to date and can be utilized as a training set for future studies. The agreement range reflects a spectrum of criteria that pathologists use to decide what constitutes a mitotic figure, which may have potential implications in tumor diagnostics and clinical management.
摘要：
Introduction.有丝分裂图的鉴定对于诊断至关重要，分级,以及各种不同肿瘤的分类。尽管它很重要,很少有文献报道病理学家在解释有丝分裂图方面的一致性。这项研究利用可公开访问的数据集和社交媒体来招募国际病理学家小组，对超过1000个有丝分裂图的图像数据库进行评分。材料和方法。指示病理学家从癌症基因组图谱(TCGA)数据集中随机选择数字载玻片,并在2mm2面积内注释10-20个有丝分裂图。前1010个提交的有丝分裂图用于创建图像数据集，每个图转换为一个单独的瓷砖在40倍的放大率。将数据集重新分配给所有病理学家，以审查并确定每个图块是否构成有丝分裂图。结果。总体病理学家的中位一致率为80.2%(范围42.0%-95.7%)。单个有丝分裂图块的中位数一致率为87.1％，所有图块的评分者之间的一致一致（kappa=0.284）。与有丝分裂的其他阶段相比，前中期的有丝分裂数字的百分比一致率较低。结论。该数据集是迄今为止最大的有丝分裂图国际共识研究，可用作未来研究的训练集。协议范围反映了病理学家用来决定什么构成有丝分裂图的一系列标准，这可能对肿瘤诊断和临床管理有潜在的影响。
