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Pathol Oncol Res ; 15(4): 733-40, 2009 Dec.
Artículo en Inglés | MEDLINE | ID: mdl-19440855

RESUMEN

Several models have previously been proposed to predict the probability of non-sentinel lymph node (NSLN) metastases after a positive sentinel lymph node (SLN) biopsy in breast cancer. The aim of this study was to assess the accuracy of two previously published nomograms (MSKCC, Stanford) and to develop an alternative model with the best predictive accuracy in a Czech population. In the basic population of 330 SLN-positive patients from the Czech Republic, the accuracy of the MSKCC and the Stanford nomograms was tested by the area under the receiver operating characteristics curve (AUC). A new model (MOU nomogram) was proposed according to the results of multivariate analysis of relevant clinicopathologic variables. The new model was validated in an independent test population from Hungary (383 patients). In the basic population, six of 27 patients with isolated tumor cells (ITC) in the SLN harbored additional NSLN metastases. The AUCs of the MSKCC and Stanford nomograms were 0.68 and 0.66, respectively; for the MOU nomogram it reached 0.76. In the test population, the AUC of the MOU nomogram was similar to that of the basic population (0.74). The presence of only ITC in SLN does not preclude further nodal involvement. Additional variables are beneficial when considering the probability of NSLN metastases. In the basic population, the previously published nomograms (MSKCC and Stanford) showed only limited accuracy. The developed MOU nomogram proved more suitable for the basic population, such as for another independent population from a mid-European country.


Asunto(s)
Neoplasias de la Mama/patología , Metástasis Linfática/diagnóstico , Nomogramas , Biopsia del Ganglio Linfático Centinela , Adulto , Anciano , Anciano de 80 o más Años , Algoritmos , Neoplasias de la Mama/etnología , República Checa , Femenino , Humanos , Metástasis Linfática/patología , Persona de Mediana Edad , Valor Predictivo de las Pruebas , Curva ROC
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