In this paper, we have proposed a method for renal cell carcinoma (RCC) grading, using a three-dimensional (3D) quantitative analysis of cell nuclei based on digital image cytometry. We acquired volumetric RCC data for each grade using confocal laser scanning microscopy (CLSM) and developed a method for grading RCC using 3D visualization and quantitative analysis of cell nuclei. First, we used a method of segmenting cell nuclei based on Pun's method. Second, to determine quantitative features, we used a 3D labeling method based on slice information. After applying the labeling algorithm, we determined the measurements of cell nuclei using 3D quantitative analysis. To evaluate which of the quantitative features provided by 3D analysis could contribute to diagnostic information and could increase accuracy in nuclear grading, we analyzed statistical differences in 3D features among the grades. We compared features measured in two dimensions (diameter, area, perimeter, and circularity) with features measured in three dimensions (volume, surface area, and spherical shape factor) between identical cell nuclei by using regression analysis. For 3D visualization, we used a contour-based method for surface rendering. We found a statistically significant correlation between the nuclear grade and the 3D morphological features. Comparing our results to an ideal RCC grading system, we found that our nuclear grading system based on the 3D features of a cell nucleus provides distinct dividing points between grades and also provides data that can be easily interpreted for diagnoses. 3D visualization of cell nuclei offers a realistic display and additional valuable medical information that can lead to an objective diagnosis. This method could overcome the limitations inherent in 2D analysis and could improve the accuracy and reproducibility of quantification of cell nuclei. Our study showed that a nuclear grading system based on the 3D features of a cell nucleus might be an ideal grading system.