Details

Autor(en) / Beteiligte

• Liedtke, C.
• Kolberg, Hans-Christian
• Kerschke, L.
• Görlich, D.
• Bauerfeind, I.
• Fehm, T.
• Fleige, B.
• Helms, G.
• Lebeau, A.
• Stäbler, A.
• Schmatloch, S.
• Hausschild, M.
• Schwentner, L.
• von Minckwitz, Gunter
• Loibl, S.
• Untch, M.
• Kühn, T.

Titel

Systematic analysis of parameters predicting pathological axillary status (ypN0 vs. ypN+) in patients with breast cancer converting from cN+ to ycN0 through primary systemic therapy (PST)

Ist Teil von

• Clinical & experimental metastasis, 2018-12, Vol.35 (8), p.777-783

Ort / Verlag

Dordrecht: Springer Netherlands

Erscheinungsjahr

2018

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• Optimization of axillary staging among patients converting from clinically node-positive disease to clinically node-negative disease through primary systemic therapy is needed. We aimed at developing a nomogram predicting the probability of positive axillary status after chemotherapy based on clinical/pathological parameters. Patients from study arm C of the SENTINA trial were included. Univariable/multivariable analyses were performed for 13 clinical/pathological parameters to predict a positive pathological axillary status after chemotherapy using logistic regression models. Odds ratios and 95%-confidence-intervals were reported. Model performance was assessed by leave-one-out cross-validation. Calculations were performed using the SAS Software (Version 9.4, SAS Institute Inc., Cary, NC, USA). 369 of 553 patients in Arm C were included in multivariable analysis. Stepwise backward variable selection based on a multivariable analysis resulted in a model including estrogen receptor (ER) status (odds ratio (OR) 3.916, 95% confidence interval (CI) 2.318–6.615, p < 0.001), multifocality (OR 2.106, 95% CI 1.203–3.689, p = 0.0092), lymphovascular invasion (OR 9.196, 95% CI 4.734–17.864, p < 0.001), and sonographic tumor diameter after PST (OR 1.034, 95% CI 1.010–1.059, p = 0.0051). When validated, our model demonstrated an accuracy of 70.2% using 0.5 as cut-point. An area under the curve of 0.81 was calculated. The use of individual parameters as predictors of lymph node status after chemotherapy resulted in an inferior accuracy. Our model was able to predict the probability of a positive axillary nodal status with a high accuracy. The use of individual parameters showed reduced predictive performance. Overall, tumor biology was the strongest parameter in our models.