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Analytical Cellular Pathology
Volume 16, Issue 3, Pages 161-175

The Chromatin Pattern of Cell Nuclei Is of Prognostic Value for Renal Cell Carcinomas

Christine François,1 Myriam Remmelink,2 Michel Petein,2 Roland van Velthoven,3 André Danguy,1 Eric Wespes,4 Isabelle Salmon,2 Robert Kiss,1 and Christine Decaestecker1

1Laboratory of Histology, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium
2Department of Pathology, Erasmus University Hospital, Brussels, Belgium
3Division of Urology, Department of Surgery, J. Bordet Institute, Brussels, Belgium
4Department of Urology, Erasmus University Hospital, Brussels, Belgium

Received 8 October 1997; Accepted 26 February 1998

Copyright © 1998 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Using a series of 105 renal cell carcinomas (RCCs) we investigated whether features quantitatively describing the appearance of Feulgen‐stained nuclei and, more particularly, of their chromatin (on the basis of computer‐assisted microscopy) can contribute any significant prognostic information. Thirty morphonuclear and 8 nuclear DNA content‐related variables were thus generated. The actual prognostic values of this set of cytometric variables was compared (by means of discriminant statistical analysis) to conventional diagnostic and/or prognostic markers including histopathological grades, tumour invasion levels and the presence or absence of metastases. We obtained complete clinical follow‐ups for 49 of the 105 RCC patients under study, making it possible to define a subset of patients with a bad prognosis (i.e., who died in the 12 months following nephrectomy) and a subset of patients with a good prognosis (i.e., who survived at least 24 months following nephrectomy). An original method of data analysis related to artificial intelligence (decision tree induction) enabled a strong prognostic model to be set up. In the case of 10 new patients, this model identified all the dead patients as having a bad survival status, with a total of 8 correct predictions. Another prognostic model similarly generated enabled the correct predictions to be confirmed.