Yeaton P, Frierson H F, Hittelet A, Duckworth C, DePrez C, Bourgeois N, Salmon I, Jones R S, Kiss R, Decaestecker C
Department of Internal Medicine, University of Virginia Health Sciences Center, Charlottesville, USA.
Anal Quant Cytol Histol. 1998 Dec;20(6):509-16.
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.
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.
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.
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。
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。
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。