Improving accuracy in the grading of renal cell carcinoma by combining the quantitative description of chromatin pattern with the quantitative determination of cell kinetic parameters.

The determination of grade and stage in renal cell carcinomas (RCCs) often fails to predict the actual clinical outcome for individual patients. The aim of the present work was to investigate whether it is possible to significantly improve the prognostic accuracy of the grading system by using the combination of two independent computer-assisted microscopy techniques. The first technique relates to the quantitative description of morphonuclear and nuclear DNA content features by means of the image analysis of Feulgen-stained cell nuclei, and the second quantitatively characterizes tumor growth by means of different cell kinetic parameters. These parameters consist of a duplication of a time-related parameter determined by means of the technique of using silver-stained proteins in interphase nucleolar organizer regions (AgNOR), a proliferation index determined by means of MIB-1 immunohistochemistry, and an apoptotic index determined by means of the terminal dUTP nick end labeling technique. The prognostic value of these quantitative features was investigated in a series of 60 RCCs. The quantitative analysis of Feulgen-stained nuclei made it possible to identify subgroups of patients with significantly different prognoses in both grade II and grade III RCCs. We labeled the RCCs associated with the most favorable prognoses as grade II- and III- and those with the least favorable ones as grade II+ and III+. The two most important kinetic variables to identify patients with different clinical outcomes were the MIB-1 index and the mean AgNOR area in the MIB-1-positive cells. Three significantly different survival curves were obtained for the 53 grade II and III RCC patients. Our results show that conventional RCC grading can be significantly improved by the quantitative analysis of Feulgen-stained nuclei, by cell kinetic parameter determination, and, more importantly, by combining the proliferation index with the mean AgNOR area parameter.

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