Department of Diagnostics and Public Health, Section of Pathology, University of Verona, Verona, Italy.
ARC-Net Research Center, University and Hospital Trust of Verona, Verona, Italy.
Mod Pathol. 2022 Jun;35(6):712-720. doi: 10.1038/s41379-022-01055-1. Epub 2022 Mar 5.
Ki-67 assessment is a key step in the diagnosis of neuroendocrine neoplasms (NENs) from all anatomic locations. Several challenges exist related to quantifying the Ki-67 proliferation index due to lack of method standardization and inter-reader variability. The application of digital pathology coupled with machine learning has been shown to be highly accurate and reproducible for the evaluation of Ki-67 in NENs. We systematically reviewed all published studies on the subject of Ki-67 assessment in pancreatic NENs (PanNENs) employing digital image analysis (DIA). The most common advantages of DIA were improvement in the standardization and reliability of Ki-67 evaluation, as well as its speed and practicality, compared to the current gold standard approach of manual counts from captured images, which is cumbersome and time consuming. The main limitations were attributed to higher costs, lack of widespread availability (as of yet), operator qualification and training issues (if it is not done by pathologists), and most importantly, the drawback of image algorithms counting contaminating non-neoplastic cells and other signals like hemosiderin. However, solutions are rapidly developing for all of these challenging issues. A comparative meta-analysis for DIA versus manual counting shows very high concordance (global coefficient of concordance: 0.94, 95% CI: 0.83-0.98) between these two modalities. These findings support the widespread adoption of validated DIA methods for Ki-67 assessment in PanNENs, provided that measures are in place to ensure counting of only tumor cells either by software modifications or education of non-pathologist operators, as well as selection of standard regions of interest for analysis. NENs, being cellular and monotonous neoplasms, are naturally more amenable to Ki-67 assessment. However, lessons of this review may be applicable to other neoplasms where proliferation activity has become an integral part of theranostic evaluation including breast, brain, and hematolymphoid neoplasms.
Ki-67 评估是诊断所有解剖部位神经内分泌肿瘤（NEN）的关键步骤。由于缺乏方法标准化和读者间可变性，因此在量化 Ki-67 增殖指数方面存在一些挑战。结合机器学习的数字病理学应用已被证明对评估 NEN 中的 Ki-67 非常准确且可重复。我们系统地回顾了所有关于胰腺神经内分泌肿瘤（PanNEN）中 Ki-67 评估的数字图像分析（DIA）发表研究。与当前手动计数捕获图像的金标准方法相比，DIA 的最常见优势在于提高了 Ki-67 评估的标准化和可靠性，以及其速度和实用性。DIA 的主要局限性归因于更高的成本、缺乏广泛的可用性（到目前为止）、操作人员的资格和培训问题（如果不是病理学家进行的），以及最重要的是，图像算法计数非肿瘤细胞和其他信号（如含铁血黄素）的污染问题。然而，针对所有这些具有挑战性的问题，解决方案正在迅速发展。DIA 与手动计数的比较荟萃分析显示这两种方法之间非常高的一致性（全球一致性系数：0.94，95%CI：0.83-0.98）。这些发现支持广泛采用经过验证的 DIA 方法来评估 PanNEN 中的 Ki-67，前提是采取措施确保仅通过软件修改或对非病理学家操作人员进行教育来计数肿瘤细胞，以及选择标准的感兴趣区域进行分析。由于 NEN 是细胞性和单调的肿瘤，因此其自然更适合 Ki-67 评估。然而，本综述的经验教训可能适用于其他增殖活性已成为治疗评估不可或缺部分的肿瘤，包括乳腺、脑和血液淋巴肿瘤。
