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Clinical & Experimental Metastasis

, Volume 35, Issue 8, pp 777–783 | Cite as

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)

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

Abstract

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.

Keywords

Prediction of lymph node status Primary systemic therapy SENTINA trial Lymph node conversion 

Notes

Acknowledgements

The SENTINA-study received financial and logistic support from AGO-B, Brustkrebs Deutschland and the German Breast Group (GBG).

Compliance with ethical standards

Conflict of interest

The authors stated no potential conflicts of interest as to the content of the manuscript.

Ethical approval

The SENTINA trial was approved by the ethics committee of the University of Frankfurt.

Informed consent

Consent for publication has been obtained from all co-authors.

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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • C. Liedtke
    • 1
  • Hans-Christian Kolberg
    • 2
  • L. Kerschke
    • 3
  • D. Görlich
    • 3
  • I. Bauerfeind
    • 4
  • T. Fehm
    • 5
  • B. Fleige
    • 6
  • G. Helms
    • 7
  • A. Lebeau
    • 8
  • A. Stäbler
    • 9
  • S. Schmatloch
    • 10
  • M. Hausschild
    • 11
  • L. Schwentner
    • 12
  • Gunter von Minckwitz
    • 13
  • S. Loibl
    • 13
  • M. Untch
    • 14
  • T. Kühn
    • 15
  1. 1.Department of GynecologyCharité University Hospital BerlinBerlinGermany
  2. 2.Department of Gynecology and ObstetricsMarienhospital BottropBottropGermany
  3. 3.Institute of Biostatistics and Clinical ResearchWestfälische Wilhelms-UniversitätMünsterGermany
  4. 4.Department of Gynecology and ObstetricsKlinikum LandshutLandshutGermany
  5. 5.Department of Gynecology and ObstetricsUniversity Hospital DüsseldorfDusseldorfGermany
  6. 6.Department of Pathology, Multidisciplinary Breast CentreHelios Klinikum Berlin-BuchBerlinGermany
  7. 7.Department of Gynecology and ObstetricsUniversity Medical Centre TübingenTübingenGermany
  8. 8.Department of PathologyUniversity Medical Center Hamburg-EppendorfHamburgGermany
  9. 9.Department of PathologyUniversity of TübingenTübingenGermany
  10. 10.Klinikum KasselKasselGermany
  11. 11.Klinikum Rheinfelden, SchweizRheinfeldenSwitzerland
  12. 12.Department of Gynecology and ObstetricsUniversity of UlmUlmGermany
  13. 13.German Breast GroupNeu IsenburgGermany
  14. 14.Department of Gynecology and Obstetrics, Multidisciplinary Breast CentreHelios Klinikum Berlin-BuchBerlinGermany
  15. 15.Department of Gynecology and Obstetrics, Interdisciplinary Breast CentreKlinikum EsslingenEsslingenGermany

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