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免疫组织化学评估肿瘤芽分级 T1 结直肠癌:最佳临界值和一种新的计算机辅助半自动方法。

Immunohistochemical evaluation of tumor budding for stratifying T1 colorectal cancer: optimal cut-off value and a novel computer-assisted semiautomatic method.

机构信息

Division of Pathology, The Cancer Institute; Department of Pathology, The Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, 135-8550, Japan.

Department of Endoscopy, The Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, 135-8550, Japan.

出版信息

Mod Pathol. 2019 May;32(5):675-683. doi: 10.1038/s41379-018-0189-1. Epub 2018 Dec 14.

Abstract

High-grade tumor budding is an adverse prognostic factor for submucosal invasive (T1) colorectal cancer used to predict the risk for lymph node metastasis in endoscopically resected specimens. Cytokeratin immunohistochemistry is a potential option for evaluating tumor budding. The optimal cut-off value between low- and high-grade budding has not yet been determined, however, and the high inter-observer variability in selecting budding foci remains problematic. We explored the optimal cut-off value for predicting lymph node metastasis using cytokeratin immunohistochemistry, and developed a novel computer-assisted semiautomatic quantification method to reduce inter-observer variability. A retrospective single-institution study of 463 T1 colorectal cancer cases was conducted. Cases were split into derivation and validation datasets. Tumor budding foci were counted manually and semiautomatically using Image J software on cytokeratin immunohistochemistry-stained specimens. We determined the cut-off values and compared inter-observer variability among pathologists between the two methods. Univariate and multivariate analyses of the derivation dataset were performed to select the risk factors for lymph node metastasis. Predictive simulation for the validation dataset was conducted. The optimal cut-off values for the manual and semiautomatic methods were ≥10 and ≥12, respectively. For both methods, multivariate analyses revealed that venous invasion, lymphatic invasion, and high-grade tumor budding were independent risk factors for lymph node metastasis. The semiautomatic method provided significantly better inter-observer agreement. The predictive and observed lymph node metastasis frequencies were highly correlated in the validation dataset.

摘要

高级肿瘤芽殖是黏膜下浸润(T1)结直肠癌的不良预后因素,用于预测内镜切除标本中淋巴结转移的风险。角蛋白免疫组化是评估肿瘤芽殖的潜在选择。然而,低级别和高级别芽殖之间的最佳临界值尚未确定,并且在选择芽殖焦点时观察者间的变异性仍然存在问题。我们探讨了使用角蛋白免疫组化预测淋巴结转移的最佳临界值,并开发了一种新的计算机辅助半自动定量方法来减少观察者间的变异性。对 463 例 T1 结直肠癌病例进行了回顾性单中心研究。病例分为推导数据集和验证数据集。在角蛋白免疫组化染色标本上手动和半自动使用 Image J 软件计数肿瘤芽殖焦点。我们确定了临界值,并比较了两种方法中病理学家之间的观察者间变异性。对推导数据集进行单因素和多因素分析,以选择淋巴结转移的危险因素。对验证数据集进行预测模拟。手动和半自动方法的最佳临界值分别为≥10 和≥12。对于两种方法,多因素分析均表明静脉侵犯、淋巴血管侵犯和高级别肿瘤芽殖是淋巴结转移的独立危险因素。半自动方法提供了更好的观察者间一致性。在验证数据集中,预测和观察到的淋巴结转移频率高度相关。

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