The chromatin pattern of cell nuclei is of prognostic value for renal cell carcinomas

Article type: Research Article

Authors: François, Christine | Remmelink, Myriam | Petein, Michel | van Velthoven, Roland | Danguy, André | Wespes, Eric | Salmon, Isabelle | Kiss, Robert^{; ;} | Decaestecker, Christine^;

Affiliations: Laboratory of Histology, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium | Department of Pathology, Erasmus University Hospital, Brussels, Belgium | Department of Urology, Erasmus University Hospital, Brussels, Belgium | Division of Urology, Department of Surgery, J. Bordet Institute, Brussels, Belgium

Note: [] Corresponding author: Dr.Robert Kiss, Laboratory of Histology, Faculty of Medicine, Université Libre de Bruxelles, 808 route de Lennik, 1070 Brussels, Belgium. Fax: +32 2 555 62 85.

Note: [] Robert Kiss is a Senior Research Associate with the Fonds National de la Recherche Scientifique (FNRS), Belgium.

Note: [] Christine Decaestecher is supported by grants of the “Yvonne Boël” Foundation and of “Les Amis de l’Institut Babet”, Brussels, Belgium.

Abstract: 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.

Keywords: Renal cell carcinoma, prognosis, Feulgen staining, image cytometry, chromatin pattern, DNA ploidy, artificial intelligence

Journal: Analytical Cellular Pathology, vol. 16, no. 3, pp. 161-175, 1998