ORIGINAL ARTICLE

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

M Scarpelli¹, R Montironi¹, L M Tarquini¹, P W Hamilton², A López Beltran³, J Ranger-Moore⁴, P H Bartels⁵

¹ Section of Pathological Anatomy and Histopathology, Polytechnic University of the Marche Region, I-60020 Ancona, Italy
² The Queen’s University, Belfast BT 12 6BL, Northern Ireland, UK
³ Unit of Anatomic Pathology, Cordoba University Medical School, Cordoba 14071, Spain
⁴ College of Public Health, Arizona Cancer Center, University of Arizona, Tucson, AZ85721, USA
⁵ Optical Sciences Center, University of Arizona

Correspondence to:
Correspondence to:
Professor R Montironi
Section of Pathological Anatomy and Histopathology, Polytechnic University of the Marche Region (Ancona), School of Medicine, Umberto I Hospital, Via Conca, 71, I-60020 Torrette, Ancona, Italy; r.montironi{at}univpm.it

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 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.

Abbreviations: CK, cytokeratin; CLASIF, Cooley-Lohnes classifier; H&E, haematoxylin and eosin; ISUP, International Society of Urological Pathology; NR, non-recurrent; OD, optical density; PUNLMP, papillary urothelial neoplasms of low malignant potential; R, recurrent; WHO, World Health Organisation

Keywords: urothelium; papillary urothelial neoplasm of low malignant potential; recurrence

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