Karyometry detects subvisual differences in chromatin organisation state between non-recurrent and recurrent papillary urothelial neoplasms of low malignant potential

• Published in: Journal of clinical pathology Vol. 57; no. 11; pp. 1201 - 1207
• Main Authors: Scarpelli, M, Montironi, R, Tarquini, L M, Hamilton, P W, López Beltran, A, Ranger-Moore, J, Bartels, P H
• Format: Journal Article
• Language: English
• Published: London BMJ Publishing Group Ltd and Association of Clinical Pathologists 01-11-2004; BMJ; BMJ Publishing Group LTD; Copyright 2004 Journal of Clinical Pathology
• Subjects: Algorithms; Biological and medical sciences; Cancer; Cell Nucleus - genetics; Chromatin - genetics; CLASIF; Classification; Cooley-Lohnes classifier; cytokeratin; Discriminant Analysis; Female; Gene expression; H&E; haematoxylin and eosin; Humans; International Society of Urological Pathology; Investigative techniques, diagnostic techniques (general aspects); ISUP; Karyometry - methods; Male; Medical sciences; Middle Aged; Neoplasm Recurrence, Local - genetics; Neoplasm Recurrence, Local - pathology; non-recurrent; optical density; Original; papillary urothelial neoplasm of low malignant potential; papillary urothelial neoplasms of low malignant potential; Pathology; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques; Phenotype; Prognosis; PUNLMP; recurrence; recurrent; Statistical analysis; Studies; Tumors; Urologic Neoplasms - genetics; Urologic Neoplasms - pathology; urothelium; Urothelium - pathology; WHO; World Health Organisation; Anatomic pathology; Karyometry; Chromatin; Relapse; Recurrent; Malignant tumor; Comparative study
• ISSN: 0021-9746; 1472-4146
• DOI: 10.1136/jcp.2004.017608

Aim: To analyse nuclear chromatin texture in non-recurrent and recurrent papillary urothelial neoplasms of low malignant potential (PUNLMPs). Materials: 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. Results: 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 λ 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%. Conclusions: 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.