免疫组化染色结直肠癌中肿瘤芽生的自动评估及其与临床结局的相关性

Automatic evaluation of tumor budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome.

作者信息

Weis Cleo-Aron, Kather Jakob Nikolas, Melchers Susanne, Al-Ahmdi Hanaa, Pollheimer Marion J, Langner Cord, Gaiser Timo

机构信息

Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany.

Department of Medical Oncology and Internal Medicine VI, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg University, Heidelberg, Germany.

出版信息

Diagn Pathol. 2018 Aug 28;13(1):64. doi: 10.1186/s13000-018-0739-3.

DOI:10.1186/s13000-018-0739-3

PMID:30153844

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6114534/

Abstract

BACKGROUND

Tumor budding, meaning a detachment of tumor cells at the invasion front of colorectal carcinoma (CRC) into single cells or clusters (<=5 tumor cells), has been shown to correlate to an inferior clinical outcome by several independent studies. Therefore, it has been discussed as a complementary prognostic factor to the TNM staging system, and it is already included in national guidelines as an additional prognostic parameter. However, its application by manual evaluation in routine pathology is hampered due to the use of several slightly different assessment systems, a time-consuming manual counting process and a high inter-observer variability. Hence, we established and validated an automatic image processing approach to reliably quantify tumor budding in immunohistochemically (IHC) stained sections of CRC samples.

METHODS

This approach combines classical segmentation methods (like morphological operations) and machine learning techniques (k-means and hierarchical clustering, convolutional neural networks) to reliably detect tumor buds in colorectal carcinoma samples immunohistochemically stained for pan-cytokeratin. As a possible application, we tested it on whole-slide images as well as on tissue microarrays (TMA) from a clinically well-annotated CRC cohort.

RESULTS

Our automatic tumor budding evaluation tool detected the absolute number of tumor buds per image with a very good correlation to the manually segmented ground truth (R2 value of 0.86). Furthermore the automatic evaluation of whole-slide images from 20 CRC-patients, we found that neither the detected number of tumor buds at the invasion front nor the number in hotspots was associated with the nodal status. However, the number of spatial clusters of tumor buds (budding hotspots) significantly correlated to the nodal status (p-value = 0.003 for N0 vs. N1/N2). TMAs were not feasible for tumor budding evaluation, as the spatial relationship of tumor buds (especially hotspots) was not preserved.

CONCLUSIONS

Automatic image processing is a feasible and valid assessment tool for tumor budding in CRC on whole-slide images. Interestingly, only the spatial clustering of the tumor buds in hotspots (and especially the number of hotspots) and not the absolute number of tumor buds showed a clinically relevant correlation with patient outcome in our data.

摘要

背景

肿瘤芽生是指结直肠癌（CRC）侵袭前沿的肿瘤细胞脱离形成单个细胞或细胞簇（≤5个肿瘤细胞），多项独立研究表明其与较差的临床结局相关。因此，它被讨论作为TNM分期系统的补充预后因素，并且已被纳入国家指南作为额外的预后参数。然而，由于使用了几种略有不同的评估系统、耗时的手动计数过程以及观察者间的高变异性，其在常规病理学中的手动评估应用受到阻碍。因此，我们建立并验证了一种自动图像处理方法，以可靠地量化CRC样本免疫组织化学（IHC）染色切片中的肿瘤芽生。

方法

该方法结合了经典分割方法（如实形态学操作）和机器学习技术（k均值和层次聚类、卷积神经网络），以可靠地检测全细胞角蛋白免疫组织化学染色的结直肠癌样本中的肿瘤芽。作为一种可能的应用，我们在来自临床注释良好的CRC队列的全切片图像以及组织微阵列（TMA）上对其进行了测试。

结果

我们的自动肿瘤芽生评估工具检测到每张图像中肿瘤芽的绝对数量，与手动分割的真实情况具有非常好的相关性（R2值为0.86）。此外，通过对20例CRC患者的全切片图像进行自动评估，我们发现侵袭前沿检测到的肿瘤芽数量和热点区域的数量均与淋巴结状态无关。然而，肿瘤芽的空间簇数量（芽生热点）与淋巴结状态显著相关（N0与N1/N2相比，p值 = 0.003）。TMA对于肿瘤芽生评估不可行，因为肿瘤芽的空间关系（尤其是热点）未得到保留。