Hayward Mary-Kate, Louise Jones J, Hall Allison, King Lorraine, Ironside Alastair J, Nelson Andrew C, Shelley Hwang E, Weaver Valerie M
Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California San Francisco, San Francisco, CA, USA.
Center for Tumor Biology, Barts Cancer Institute, John Vane Science Building, Barts and the London School of Medicine and Dentistry, UK.
Comput Struct Biotechnol J. 2020 Dec 3;18:4063-4070. doi: 10.1016/j.csbj.2020.11.040. eCollection 2020.
Abnormalities in cell nuclear morphology are a hallmark of cancer. Histological assessment of cell nuclear morphology is frequently used by pathologists to grade ductal carcinoma in situ (DCIS). Objective methods that allow standardization and reproducibility of cell nuclear morphology assessment have potential to improve the criteria needed to predict DCIS progression and recurrence. Aggressive cancers are highly heterogeneous. We asked whether cell nuclear morphology heterogeneity could be incorporated into a metric to classify DCIS. We developed a nuclear heterogeneity image index to objectively, and quantitatively grade DCIS. A whole-tissue cell nuclear morphological analysis, that classified tumors by the worst ten percent in a duct-by-duct manner, identified nuclear size ranges associated with each DCIS grade. Digital image analysis further revealed increasing heterogeneity within ducts or between ducts in tissues of worsening DCIS grade. The findings illustrate how digital image analysis comprises a supplemental tool for pathologists to objectively classify DCIS and in the future, may provide a method to predict patient outcome through analysis of nuclear heterogeneity.
细胞核形态异常是癌症的一个标志。病理学家经常使用细胞核形态的组织学评估来对导管原位癌（DCIS）进行分级。能够实现细胞核形态评估标准化和可重复性的客观方法有潜力改进预测DCIS进展和复发所需的标准。侵袭性癌症具有高度异质性。我们询问细胞核形态异质性是否可以纳入一个指标来对DCIS进行分类。我们开发了一种核异质性图像指数来客观、定量地对DCIS进行分级。一种全组织细胞核形态分析，以逐导管方式根据最差的百分之十对肿瘤进行分类，确定了与每个DCIS分级相关的核大小范围。数字图像分析进一步揭示，在DCIS分级恶化的组织中，导管内或导管间的异质性在增加。这些发现说明了数字图像分析如何构成一种辅助工具，供病理学家客观地对DCIS进行分类，并且在未来，可能通过分析核异质性提供一种预测患者预后的方法。
