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The role of nuclear morphometry for predicting disease outcome in patients with localized renal cell carcinoma
Author(s) -
Nativ Ofer,
Sabo Edmond,
Raviv Gil,
Medalia Ora,
Moskovitz Boaz,
Goldwasser Benad
Publication year - 1995
Publication title -
cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.052
H-Index - 304
eISSN - 1097-0142
pISSN - 0008-543X
DOI - 10.1002/1097-0142(19951015)76:8<1440::aid-cncr2820760822>3.0.co;2-8
Subject(s) - medicine , renal cell carcinoma , nephrectomy , univariate analysis , multivariate analysis , univariate , kidney disease , disease , multivariate statistics , nuclear medicine , pathology , kidney , statistics , mathematics
Background . More than one‐third of patients with localized renal cell carcinoma (RCC) will have disease progression after nephrectomy. Present histopathologic variables cannot accurately predict the outcome of individual patients. Methods . Nuclear morphometry was performed by an image analyzer on histologic sections from 39 specimens of pathologic T1 and T2 classification RCC. All patients underwent radical nephrectomy and were followed for a mean of 7.6 years. A univariate analysis and then a multivariate stepwise regression method were used to correlate results with patients' outcome. Results . The best predictors of disease free interval were mean nuclear elongation factor (MNEF) ( P = 0.023), mean nuclear regularity factor (MNRF) ( P = 0.034), and mean nuclear area (MNA) (N = 0.038). Univariate analysis identified a significant correlation between patient survival and MNEF ( P = 0.009), MNRF ( P = 0.020) and MNA (P = 0.023). Combination of MNEF and MNA was even more strongly associated with survival ( P = 0.0013). Multivariate analysis revealed that MNA ( P = 0.044) and MNEF ( P = 0.045) correlated independently with survival. Conclusion . These results suggest that nuclear morphometry provides objective independent prognostic information for patients with localized RCC.