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1.
Int. braz. j. urol ; 46(5): 691-704, Sept.-Oct. 2020. tab, graf
Artículo en Inglés | LILACS | ID: biblio-1134242

RESUMEN

ABSTRACT Background: The diagnostic value and suitability of prostate cancer antigen 3 (PCA3) for the detection of prostate cancer (PCa) have been inconsistent in previous studies. Thus, the aim of the present meta-analysis was performed to systematically evaluate the diagnostic value of PCA3 for PCa. Materials and Methods: A meta-analysis was performed to search relevant studies using online databases EMBASE, PubMed and Web of Science published until February 1st, 2019. Ultimately, 65 studies met the inclusion criteria for this meta-analysis with 8.139 cases and 14.116 controls. The sensitivity, specificity, positive likelihood ratios (LR+), negative likelihood ratios (LR−), and other measures of PCA3 were pooled and determined to evaluate the diagnostic rate of PCa by the random-effect model. Results: With PCA3, the pooled overall diagnostic sensitivity, specificity, LR+, LR−, and 95% confidence intervals (CIs) for predicting significant PCa were 0.68 (0.64-0.72), 0.72 (0.68-0.75), 2.41 (2.16-2.69), 0.44 (0.40-0.49), respectively. Besides, the summary diagnostic odds ratio (DOR) and 95% CIs for PCA3 was 5.44 (4.53-6.53). In addition, the area under summary receiver operating characteristic (sROC) curves and 95% CIs was 0.76 (0.72-0.79). The major design deficiencies of included studies were differential verification bias, and a lack of clear inclusion and exclusion criteria. Conclusions: The results of this meta-analysis suggested that PCA3 was a non-invasive method with the acceptable sensitivity and specificity in the diagnosis of PCa, to distinguish between patients and healthy individuals. To validate the potential applicability of PCA3 in the diagnosis of PCa, more rigorous studies were needed to confirm these conclusions.


Asunto(s)
Humanos , Masculino , Neoplasias de la Próstata/diagnóstico , Biomarcadores de Tumor , Oportunidad Relativa , Curva ROC , Sensibilidad y Especificidad , Antígenos de Neoplasias
2.
Int Braz J Urol ; 46(5): 691-704, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-31961625

RESUMEN

BACKGROUND: The diagnostic value and suitability of prostate cancer antigen 3 (PCA3) for the detection of prostate cancer (PCa) have been inconsistent in previous studies. Thus, the aim of the present meta-analysis was performed to systematically evaluate the diagnostic value of PCA3 for PCa. MATERIALS AND METHODS: A meta-analysis was performed to search relevant studies using online databases EMBASE, PubMed and Web of Science published until February 1st, 2019. Ultimately, 65 studies met the inclusion criteria for this meta-analysis with 8.139 cases and 14.116 controls. The sensitivity, specificity, positive likelihood ratios (LR+), negative likelihood ratios (LR-), and other measures of PCA3 were pooled and determined to evaluate the diagnostic rate of PCa by the random-effect model. RESULTS: With PCA3, the pooled overall diagnostic sensitivity, specificity, LR+, LR-, and 95% confidence intervals (CIs) for predicting significant PCa were 0.68 (0.64-0.72), 0.72 (0.68-0.75), 2.41 (2.16-2.69), 0.44 (0.40-0.49), respectively. Besides, the summary diagnostic odds ratio (DOR) and 95% CIs for PCA3 was 5.44 (4.53-6.53). In addition, the area under summary receiver operating characteristic (sROC) curves and 95% CIs was 0.76 (0.72-0.79). The major design deficiencies of included studies were differential verification bias, and a lack of clear inclusion and exclusion criteria. CONCLUSIONS: The results of this meta-analysis suggested that PCA3 was a non-invasive method with the acceptable sensitivity and specificity in the diagnosis of PCa, to distinguish between patients and healthy individuals. To validate the potential applicability of PCA3 in the diagnosis of PCa, more rigorous studies were needed to confirm these conclusions.


Asunto(s)
Neoplasias de la Próstata , Antígenos de Neoplasias , Biomarcadores de Tumor , Humanos , Masculino , Oportunidad Relativa , Neoplasias de la Próstata/diagnóstico , Curva ROC , Sensibilidad y Especificidad
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