Factors predicting non-sentinel lymph node metastasis in T1-2 invasive breast cancer with 1-2 axillary sentinel lymph node metastases: Presentation of Ondokuz Mayis scoring system.
| Autor: | Kuru B; Department of General Surgery, Ondokuz Mayis School of Medicine, Samsun, Turkey., Sullu Y, Yuruker S, Koray Bayrak I, Ozen N |
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| Jazyk: | angličtina |
| Zdroj: | Journal of B.U.ON. : official journal of the Balkan Union of Oncology [J BUON] 2016 Sept-Oct; Vol. 21 (5), pp. 1129-1136. |
| Abstrakt: | Purpose: To evaluate the predicting factors for non-sentinel lymph node (SLN) metastases in T1-2 invasive breast cancer with 1-2 metastatic SLN that fully matched the ACOSOG Z0011 criteria. Also, to develop a scoring system to predict the risk of non-SLN metastasis and to discriminate the low-risk patients for omission of the axillary lymph node dissection (ALND) in this population. Methods: Two hundred and seven T1-2 invasive breast cancer patients with 1-2 metastatic SLN who underwent ALND at our Institution were included in the study. Independent factors predicting the non-SLN metastasis were found using logistic regression analysis, and a scoring system to predict the non-SLN metastasis was created. Results: Seventy (34%) out of 207 patients had non- SLN metastasis. Multivariate logistic regression analysis demonstrated that tumor size, presence of lymphovascular invasion (LVI), number of negative SLNs, and size of SLN metastasis were independent factors predicting non-SLN metastasis. There were 68 (33%) and 108 (52%) patients with a the score of ? 4 (predicted probability of ?10%) with a false negative rate (FNR) of 4.4%, and ?5 (predicted probability of ?15%) with a FNR of 7.4%, respectively. The area under the curve (AUC) value for the Ondokuz Mayis scoring system was 0.88 (95% CI 0.83-0.93). Conclusions: The present Ondokuz Mayis model with an AUC of 0.88 showed excellent discrimination capacity to distinguish patients at low risk for positive non-SLN from high risk patients and could help spare ALND in an important portion of patients. |
| Databáze: | MEDLINE |
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