Improving the prognostic value of histopathological grading and clinical staging in renal cell carcinomas by means of computer‐assisted microscopy

The present work aims to refine prognosis in cases of renal cell carcinoma (RCC) by integrating a variety of parameters with the prognostic information provided by histopathological grading and clinical staging, carried out on a series of 97 RCCs. To this end, Feulgen‐stained RCC cell nuclei were characterized by means of 38 variables describing nuclear DNA ploidy levels and morphology. All of these data were subjected to a principal components analysis. On the basis of this multivariate analysis, Fuhrman grade II was subdivided into grades II− and II+, and Fuhrman grade III into grade III− and III+. The same kind of subcategorization was performed in the case of the T2 and T3 clinical stages. The results show that the classification into grade II− and III− RCCs correspond to a more favourable prognosis than grade II+ and III+, to which shorter survival periods were attributable. Similar results were obtained for the subcategorization of the T2 and T3 clinical stages. Very simple biological characterizations of these grade‐ or stage‐related RCC groups were obtained by means of a decision tree approach applied to the cytometry‐generated variables. The resulting classification rules were validated on a new series of 18 patients and enabled very accurate predictions of survival. Copyright © 1999 John Wiley & Sons, Ltd.

[1]  C Decaestecker,et al.  Classification strategies for the grading of renal cell carcinomas, based on nuclear morphometry and densitometry , 1997, The Journal of pathology.

[2]  H. Sugao,et al.  Flow cytometric analysis of nuclear DNA content of renal cell carcinoma correlated with histologic and clinical features , 1993, Cancer.

[3]  C Decaestecker,et al.  Decision tree induction: a useful tool for assisted diagnosis and prognosis in tumor pathology. , 1997, Laboratory investigation; a journal of technical methods and pathology.

[4]  T. Strohmeyer,et al.  Classic and modern prognostic indicators in renal cell carcinoma. Review of the literature. , 1991, Urologia internationalis.

[5]  The contribution of image cytometry and artificial intelligence-related methods of numerical data analysis for adipose tumor histopathologic classification. , 1996, Laboratory investigation; a journal of technical methods and pathology.

[6]  C Decaestecker,et al.  Methodological aspects of using decision trees to characterise leiomyomatous tumors. , 1996, Cytometry.

[7]  D. Grignon,et al.  DNA flow cytometry as a predictor of outcome of stage I renal cell carcinoma , 1989, Cancer.

[8]  J. Schalken,et al.  Prognostic value of karyometric and clinical characteristics in renal cell carcinoma quantitative assessment of tumor heterogeneity , 1993, Cancer.

[9]  C Decaestecker,et al.  THE USE OF THE DECISION TREE TECHNIQUE AND IMAGE CYTOMETRY TO CHARACTERIZE AGGRESSIVENESS IN WORLD HEALTH ORGANIZATION (WHO) GRADE II SUPERFICIAL TRANSITIONAL CELL CARCINOMAS OF THE BLADDER , 1996, The Journal of pathology.

[10]  B. Loftus,et al.  A comparative analysis of grading systems in renal adenocarcinoma , 1994, Histopathology.

[11]  R. Kiss,et al.  Characterization of factors in routine laboratory protocols that significantly influence the Feulgen reaction. , 1993, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[12]  M Petein,et al.  Computer‐assisted chromatin texture characterization of Feulgen‐stained nuclei in a series of 331 transitional bladder cell carcinomas , 1994, The Journal of pathology.

[13]  J. Baak,et al.  Nuclear morphometry as an important prognostic factor in stage I renal cell carcinoma , 1986, Cancer.

[14]  P. Bringuier,et al.  DNA ploidy status and DNA content instability within single tumors in renal cell carcinoma. , 1993, Cytometry.

[15]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[16]  S. Fuhrman,et al.  Prognostic significance of morphologic parameters in renal cell carcinoma , 1982, The American journal of surgical pathology.

[17]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[18]  J. O'connor,et al.  Value of deoxyribonucleic acid ploidy and nuclear morphometry for prediction of disease progression in renal cell carcinoma. , 1996, The Journal of urology.