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Predicting axillary sentinel node status in patients with primary breast cancer

Publikace na Přírodovědecká fakulta, Fakulta tělesné výchovy a sportu, 1. lékařská fakulta, 3. lékařská fakulta |
2013

Tento text není v aktuálním jazyce dostupný. Zobrazuje se verze "en".Abstrakt

The aim of this study is to determine characteristics in early breast cancer that could estimate the risk of presence of metastatic cells in sentinel lymph node(s). The prediction of presence or absence of axillary sentinel involvement, could spare a considerable proportion of patients from axillary surgery with the similar outcome of their disease.

The study is based on retrospective analysis of medical records of 170 patients diagnosed with primary breast cancer. These women underwent primary surgery of the breast and axilla in which at least one sentinel lymph node was obtained.

Logistic regression has been employed to construct a model predicting axillary sentinel lymph node involvement using preoperative and postoperative tumor characteristics. Postoperative model uses tumor features obtained from definitive histology.

Its predictive capability is good, area under curve (AUC) equals to 0.78. The comparison between preoperative and postoperative results showed only significant differences in values of histopathological grading; we have considered grading was not reliably stated before surgery.

In preoperative model the characteristics available and reliable at the time of diagnoses were used. The predictive capability of this model is only fair when using the data available at the time of diagnosis (AUC = 0.66).

We conclude, that models based on postoperative values enable to estimate the likelihood of occurrence of axillary sentinel node(s) metastases and can be used in clinical practice in case surgical procedure is divided into two steps, breast surgery first and axillary surgery thereafter. Even if preoperative values were not different from postoperative ones, the preoperative predictive capability is lower compared to postoperative values.

The reason for this worse prediction was identified in failed preoperative diagnostics.