Nuclear quantitative grading by discriminant analysis of renal cell carcinoma samples. A patient survival evaluation

Specimens from 60 cases of renal cell carcinoma (RCC) were graded employing quantitative nuclear data combined with multivariate discriminant analysis. Evaluation of patient survival was analysed with respect to quantitative microscopic and qualitative features. Both morphometric and stereological e...

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Veröffentlicht in:The Journal of pathology 1994-06, Vol.173 (2), p.105-114
Hauptverfasser: Artacho-Pérula, Emilio, Roldán-Villalobos, Rafael, Martínez-Cuevas, Juan F., López-Rubio, Fernando
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Sprache:eng
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Zusammenfassung:Specimens from 60 cases of renal cell carcinoma (RCC) were graded employing quantitative nuclear data combined with multivariate discriminant analysis. Evaluation of patient survival was analysed with respect to quantitative microscopic and qualitative features. Both morphometric and stereological estimators were used to establish the nuclear size and form pattern of the RCC specimens. Tumoural dedifferentiation paralleled progressive increases in nuclear elongation and in two‐ and, especially, three‐dimensional—mean nuclear volume (MNV)—size parameters. Using stepwise discriminant analysis, 85‐0 per cent of the specimens were correctly classified when differentiating grade 2 and 3 tumours. It is concluded that simple and realistic estimates of MNV are the best discriminator for objective grading in patients with RCC. Univariate survival analysis demonstrated the important significance of several features such as MNV, clinical stage, and nuclear discriminant and histopathological tumour grades. Nuclear form factor PE, area, and perimeter were also significant. A prognosis study based on the Cox model using a stepwise selection of parameters showed that only MNV has an independent prognostic role when examining all investigated quantitative parameters. The clinical stage was the best prognostic feature when all quantitative and qualitative characteristics were included in the analysis.
ISSN:0022-3417
1096-9896
DOI:10.1002/path.1711730206