Scarpelli M, Montironi R, Tarquini L M, Hamilton P W, López Beltran A, Ranger-Moore J, Bartels P H
Section of Pathological Anatomy and Histopathology, Polytechnic University of the Marche Region, I-60020 Ancona, Italy.
J Clin Pathol. 2004 Nov;57(11):1201-7. doi: 10.1136/jcp.2004.017608.
To analyse nuclear chromatin texture in non-recurrent and recurrent papillary urothelial neoplasms of low malignant potential (PUNLMPs).
Ninety three karyometric features were analysed on haematoxylin and eosin stained sections from 20 PUNLMP cases: 10 from patients with a solitary PUNLMP lesion, who were disease free during at least eight years' follow up, and 10 from patients with unifocal PUNLMP, one or more recurrences being seen during follow up.
Kruskal-Wallis analysis was used to search for features showing significant differences between recurrent and non-recurrent cases. Significance was better than p<0.005 for more than 20 features. Based on significance, six texture features were selected for discriminant analysis. Stepwise linear discriminant analysis reduced Wilk's lambda to 0.87, indicating a highly significant difference between the two multivariate data sets, but only modest ability to discriminate (70% correct case classification). A box sequential classifier was used based on data derived from discriminant analysis. The classifier took three classification steps and classified 19 of the 20 cases correctly (95% correct case classification). To determine whether significant case grouping could also be obtained based on an objective criterion, the merged data sets of non-recurrent and recurrent cases were submitted to the unsupervised learning algorithm P-index. Two clusters were formed with significant differences. The subsequent application of a Cooley/Lohnes classifier resulted in an overall correct case classification rate of 85%.
Karyometry and multivariate analyses detect subvisual differences in chromatin organisation state between non-recurrent and recurrent PUNLMPs, thus allowing identification of lesions that do or do not recur.
分析低恶性潜能乳头状尿路上皮肿瘤(PUNLMP)的非复发性和复发性病例的核染色质纹理。
对20例PUNLMP病例的苏木精和伊红染色切片分析了93个核测量特征:10例来自孤立性PUNLMP病变患者,在至少8年的随访期间无疾病复发;10例来自单灶性PUNLMP患者,随访期间出现1次或多次复发。
采用Kruskal-Wallis分析寻找复发性和非复发性病例之间存在显著差异的特征。20多个特征的显著性优于p<0.005。基于显著性,选择6个纹理特征进行判别分析。逐步线性判别分析将威尔克斯λ值降至0.87,表明两个多变量数据集之间存在高度显著差异,但判别能力一般(病例分类正确率为70%)。基于判别分析得出的数据,使用了盒式序列分类器。该分类器分三个分类步骤,20例病例中有19例分类正确(病例分类正确率为95%)。为了确定是否也能基于客观标准获得显著的病例分组,将非复发性和复发性病例的合并数据集提交给无监督学习算法P指数。形成了两个有显著差异的聚类。随后应用Cooley/Lohnes分类器,病例总体分类正确率为85%。
核测量和多变量分析检测到非复发性和复发性PUNLMP之间染色质组织状态的亚视觉差异,从而能够识别复发或不复发的病变。