rss
J Clin Pathol 2004;57:1201-1207 doi:10.1136/jcp.2004.017608
  • Original article

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

  1. M Scarpelli1,
  2. R Montironi1,
  3. L M Tarquini1,
  4. P W Hamilton2,
  5. A López Beltran3,
  6. J Ranger-Moore4,
  7. P H Bartels5
  1. 1Section of Pathological Anatomy and Histopathology, Polytechnic University of the Marche Region, I-60020 Ancona, Italy
  2. 2The Queen’s University, Belfast BT 12 6BL, Northern Ireland, UK
  3. 3Unit of Anatomic Pathology, Cordoba University Medical School, Cordoba 14071, Spain
  4. 4College of Public Health, Arizona Cancer Center, University of Arizona, Tucson, AZ85721, USA
  5. 5Optical Sciences Center, University of Arizona
  1. 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.montironiunivpm.it
  • Accepted 1 June 2004

Abstract

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.

Footnotes

    Latest from JCP Education

    Latest from JCP Education

    Register for free content


    Free sample
    This recent issue is free to all users to allow everyone the opportunity to see the full scope and typical content of JCP.
    View free sample issue >>

    Free archive
    The full back archive is now available for JCP. Institutional subscribers may access the entire archive as part of their subscription. Personal subscribers will also have access to all content when logged in. Non-subscribers who register have free access to all articles published before 2006, back to volume 1 issue 1.
    Register to access the free archive >>

    Don't forget to sign up for content alerts so you keep up to date with all the articles as they are published.

  • Latest Pathology jobs

    Latest Pathology jobs