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利用图像细胞术对胆管癌和壶腹腺癌进行分类。

Use of image cytometry to classify biliary and ampullary adenocarcinomas.

作者信息

Yeaton P, Frierson H F, Hittelet A, Duckworth C, DePrez C, Bourgeois N, Salmon I, Jones R S, Kiss R, Decaestecker C

机构信息

Department of Internal Medicine, University of Virginia Health Sciences Center, Charlottesville, USA.

出版信息

Anal Quant Cytol Histol. 1998 Dec;20(6):509-16.

PMID:9870103
Abstract

OBJECTIVE

To create an objective classification system to perform TNM classification of ampullary adenocarcinoma and cholangiocarcinoma using image cytometric data derived from Feulgen-stained tumor nuclei.

STUDY DESIGN

Surgically resected cases of ampullary adenocarcinoma and cholangiocarcinoma with established TNM classifications were selected on the basis of available formalin-fixed, paraffin-embedded tissue. Fifteen numerical variables related to morphometric, densitometric and textural features of each tumor nucleus were recorded. We employed a methodology based on multivariate statistical tools to characterize the association of morphonuclear variables with TNM classification. The first step consisted of identifying and selecting representative nuclei of each T class. From this "purified" data set an objective classification system was created. The classification system was assessed using internal and external validation.

RESULTS

Employing ANOVA, all 15 variables were significantly associated with T classification, 11 of 15 with N and 4 with M. Multivariate analysis was employed to distinguish between T1, T2 and T3 lesions. Our methodology correctly classified 76% of T1 nuclei, 47% of T2 nuclei and 84% of T3 nuclei. Heterogeneity within an individual tumor was defined in 61% of cases included in the training set. Complete concordance between pathologic classification and the classification system was observed in 71% of an independent validation.

摘要

目的

利用福尔根染色肿瘤细胞核的图像细胞计量数据,创建一个用于壶腹腺癌和胆管癌TNM分类的客观分类系统。

研究设计

基于现有的福尔马林固定、石蜡包埋组织,选取已确定TNM分类的壶腹腺癌和胆管癌手术切除病例。记录与每个肿瘤细胞核的形态测量、密度测量和纹理特征相关的15个数值变量。我们采用基于多变量统计工具的方法来描述形态核变量与TNM分类之间的关联。第一步包括识别和选择每个T类的代表性细胞核。从这个“纯化”数据集中创建了一个客观分类系统。使用内部和外部验证对分类系统进行评估。

结果

采用方差分析,所有15个变量均与T分类显著相关,15个变量中的11个与N相关,4个与M相关。采用多变量分析区分T1、T2和T3病变。我们的方法正确分类了76%的T1细胞核、47%的T2细胞核和84%的T3细胞核。在训练集中纳入的61%的病例中定义了单个肿瘤内的异质性。在71%的独立验证中观察到病理分类与分类系统之间完全一致。

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