Cornelia Liedtke1, Dennis Görlich2, Ingo Bauerfeind3, Tanja Fehm4, Barbara Fleige5, Gisela Helms6, Annette Lebeau7, Annette Staebler8, Beyhan Ataseven9, Carsten Denkert10, Bernd Gerber11, Jörg Heil12, David Krug13,14, Sherko Kümmel15, Lukas Schwentner16, Gunter von Minckwitz17, Sibylle Loibl17, Michael Untch18, Thorsten Kühn19. 1. Department of Gynecology and Obstetrics, Charite - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany. 2. Institute of Biostatistics and Clinical Research, Westfälische Wilhelms-Universität Münster, Münster, Germany. 3. Department of Gynecology and Obstetrics, Klinikum Landshut, Landshut, Germany. 4. Department of Gynecology and Obstetrics, University Hospital Düsseldorf, Düsseldorf, Germany. 5. Department of Pathology, Multidisciplinary Breast Centre, Helios Klinikum Berlin-Buch, Berlin, Germany. 6. Department of Gynecology and Obstetrics, University Medical Centre Tübingen, Tübingen, Germany. 7. Department of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. 8. Department of Pathology, University Medical Centre Tübingen, Tübingen, Germany. 9. Department of Gynecology and Gynecologic Oncology, Kliniken Essen-Mitte, Essen, Germany. 10. Institute of Pathology, Charité University Hospital Berlin, Berlin, Germany. 11. Department of Gynecology and Obstetrics, University Hospital Rostock, Rostock, Germany. 12. Department of Obstetrics and Gynecology, University Hospital Heidelberg, Heidelberg, Germany. 13. Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany. 14. National Centre for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology, Heidelberg, Germany. 15. Breast Centre, Kliniken Essen-Mitte, Essen, Germany. 16. Department of Gynecology and Obstetrics, University of Ulm, Ulm, Germany. 17. German Breast Group, Neu-Isenburg, Germany. 18. Department of Gynecology and Obstetrics, Multidisciplinary Breast Centre, Helios Klinikum Berlin-Buch, Berlin, Germany. 19. Interdisciplinary Breast Centre, Department of Gynecology and Obstetrics, Klinikum Esslingen, Esslingen, Germany.
Abstract
BACKGROUND: Prediction of non-sentinel lymph node (SLN) status after primary systemic therapy (PST) may allow tailored axillary staging. The aim of this analysis was to compare established nomograms from i) the primary operative (n = 6) and ii) the neoadjuvant (n = 1) setting with an optimized nomogram to predict non-SLN status in patients after PST. METHODS: 181 patients converting from cN1 prior to PST to ycN0 but found to have a histologically positive SLN in the SENTINA trial were analyzed. Established models were applied. An optimized model was compiled using univariate and subsequent multivariable logistic regression (backward selection, likelihood ratio test). RESULTS: Area-under-the-curve (AUC) values from the primary operative models showed sufficient performance (0.82-0.71). For the neoadjuvant model, the AUC was found to be inferior to prior analyses (0.66) but within published confidence intervals. The SENTINA nomogram comprised the diameter of the largest lymph node (p = 0.006, odds ratio (OR) = 1.19), tumor size prior to PST (p = 0.085, OR = 1.31), and number of all positive SLN (p = 0.083, OR = 2.04). This model was validated using a separate cohort of arm C (n = 168, AUC 0.79, 95% confidence interval 0.74-0.85). CONCLUSION: We validated 7 models of prediction of non-SLN among patients showing axillary conversion through PST. Our own 'SENTINA nomogram' yielded AUC values comparable to previous nomograms.
BACKGROUND: Prediction of non-sentinel lymph node (SLN) status after primary systemic therapy (PST) may allow tailored axillary staging. The aim of this analysis was to compare established nomograms from i) the primary operative (n = 6) and ii) the neoadjuvant (n = 1) setting with an optimized nomogram to predict non-SLN status in patients after PST. METHODS: 181 patients converting from cN1 prior to PST to ycN0 but found to have a histologically positive SLN in the SENTINA trial were analyzed. Established models were applied. An optimized model was compiled using univariate and subsequent multivariable logistic regression (backward selection, likelihood ratio test). RESULTS: Area-under-the-curve (AUC) values from the primary operative models showed sufficient performance (0.82-0.71). For the neoadjuvant model, the AUC was found to be inferior to prior analyses (0.66) but within published confidence intervals. The SENTINA nomogram comprised the diameter of the largest lymph node (p = 0.006, odds ratio (OR) = 1.19), tumor size prior to PST (p = 0.085, OR = 1.31), and number of all positive SLN (p = 0.083, OR = 2.04). This model was validated using a separate cohort of arm C (n = 168, AUC 0.79, 95% confidence interval 0.74-0.85). CONCLUSION: We validated 7 models of prediction of non-SLN among patients showing axillary conversion through PST. Our own 'SENTINA nomogram' yielded AUC values comparable to previous nomograms.
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