Valor pronóstico de la ploidía del adn y la morfometría nuclear en el cáncer de próstata metastásico

Jabaloyas, J.M. Martínez; Sánchez, A Jiménez; Cerdá, J.L. Ruíz; Chinesta, S. Sanz; Sempere, A.; Cruz, J.F. Jiménez

doi:10.1016/s0210-4806(04)73078-8

Actas Urológicas Españolas

2004

DOI: 10.1016/s0210-4806(04)73078-8

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Valor pronóstico de la ploidía del adn y la morfometría nuclear en el cáncer de próstata metastásico

J.M. Martínez Jabaloyas¹,

A Jiménez Sánchez²,

J.L. Ruíz Cerdá³

et al.

Abstract: The study of DNA content and nuclear morphometry in the primitive tumor provides independent prognostic information in survival analysis for patients with metastatic prostate cancer. However, there is limited improvement with respect to the classical factors in predicting survival. This questions its utility in the daily clinical usage.

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Cited by 6 publications

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“…The results of the study by Martinez- Jabaloyas et al (77) revealed that mean nuclear area and other factors proved to have a prognostic value in the univariate analysis and concluded (78) that nuclear morphometry in the primitive tumor provides independent prognostic information in survival analysis for patients with metastatic prostate cancer. The combined evaluation of high nuclear morphology, ploidy, and cell survival parameters such as Bcl- 2 expression might better identify patients with poor prognosis among early stage prostate carcinomas diagnosed by FNA biopsies (79).…”

mentioning

confidence: 99%

Quantitative Pathology: Historical Background, Clinical Research and Application of Nuclear Morphometry and DNA Image Cytometry

Buhmeida

2006

Libyan J Med

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mentioning

confidence: 99%

Quantitative Pathology: Historical Background, Clinical Research and Application of Nuclear Morphometry and DNA Image Cytometry

Buhmeida

2006

Libyan J Med

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Identification of patients with low‐risk for aneuploidy: Comparative discriminatory models using linear and machine‐learning classifiers in prostate cancer

et al. 2007

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Our study demonstrates that models using GS and PCARBX are able to predict PNB ploidy status with acceptable accuracy. While machine learning classifier-derived models yield similar accuracy as LR-derived models, the latter methodology has the distinct advantage of being applicable in future datasets to estimate case-specific predictions. This information may be useful in identifying potentially aneuploid patients, who can then be targeted for more aggressive therapy.

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Morphometric sum optical density as a surrogate marker for ploidy status in prostate cancer: an analysis in 180 biopsies using logistic regression and binary recursive partitioning

et al. 2006

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We sought to identify morphometric descriptors predictive of nondiploidy in prostatic adenocarcinoma on prostate needle biopsies using logistic regression (LR) and binary recursive partitioning (BRP) and compare the equivalence of both methods. A total of 180 prostate needle biopsies diagnosed as prostatic adenocarcinoma were selected. Deoxyribonucleic acid ploidy and morphometry were performed separately on Feulgen-stained sections from these biopsies using the CAS-200 system and Nuclear Morphometry Suite, respectively. Seven morphometric predictors were tested as predictor variables, including nuclear area, circularity, elongation, sum optical density (OD), configuration run length, coefficient of variation (CV), and angularity. Logistic regression (LR) identified a two-parameter model including sum OD and circularity that had a 93.9% overall correct prediction rate (area under curve=0.950; 95% CI: 0.913, 0.987). A reduced model including only sum OD was equally good without any significant loss of predictive accuracy (93.3% correct overall classification rate). BRP also selected sum OD as the most predictive parameter; a sum OD cut-off of 7.73 in this model identified 93.3% of the nondiploid cases correctly. Morphometric OD can be used as a surrogate marker of nondiploidy. LR and BRP models are both equivalent in identifying and correctly classifying nondiploid cases of prostate cancer using sum OD as the predictor variable.

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Valor pronóstico de la ploidía del adn y la morfometría nuclear en el cáncer de próstata metastásico

Cited by 6 publications

References 31 publications

Quantitative Pathology: Historical Background, Clinical Research and Application of Nuclear Morphometry and DNA Image Cytometry

Quantitative Pathology: Historical Background, Clinical Research and Application of Nuclear Morphometry and DNA Image Cytometry

Identification of patients with low‐risk for aneuploidy: Comparative discriminatory models using linear and machine‐learning classifiers in prostate cancer

Morphometric sum optical density as a surrogate marker for ploidy status in prostate cancer: an analysis in 180 biopsies using logistic regression and binary recursive partitioning

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