International multicenter tool to predict the risk of nonsentinel node metastases in breast cancer

Tuomo J. Meretoja, Marjut H K Leidenius, Päivi S. Heikkilä, Gabor Boross, István Sejben, Peter Regitnig, Gero Luschin-Ebengreuth, Janez Žgajnar, Andraz Perhavec, Barbara Gazic, G. Lazar, Tibor Takács, Andras Vörös, Zuhair A. Saidan, Rana M. Nadeem, Isabella Castellano, Anna Sapino, Simonetta Bianchi, Vania Vezzosi, Emmanuel BarrangerRuben Lousquy, Riccardo Arisio, Maria Pia Foschini, Shigeru Imoto, Hiroshi Kamma, Tove F. Tvedskov, Niels Kroman, Maj Brit Jensen, Riccardo A. Audisio, G. Cserni

Research output: Contribution to journalArticle

49 Citations (Scopus)

Abstract

Background Axillary treatment of breast cancer patients is undergoing a paradigm shift, as completion axillary lymph node dissections (ALNDs) are being questioned in the treatment of patients with tumor-positive sentinel nodes. This study aims to develop a novel multi-institutional predictive tool to calculate patient-specific risk of residual axillary disease after tumor-positive sentinel node biopsy. Methods Breast cancer patients with a tumor-positive sentinel node and a completion ALND from five European centers formed the original patient series (N = 1000). Statistically significant variables predicting nonsentinel node involvement were identified in logistic regression analysis. A multivariable predictive model was developed and validated by area under the receiver operating characteristics curve (AUC), first internally in 500 additional patients and then externally in 1068 patients from other centers. All statistical tests were two-sided. Results Nine tumor-and sentinel node-specific variables were identified as statistically significant factors predicting nonsentinel node involvement in logistic regression analysis. A resulting predictive model applied to the internal validation series resulted in an AUC of 0.714 (95% confidence interval [CI] = 0.665 to 0.763). For the external validation series, the AUC was 0.719 (95% CI = 0.689 to 0.750). The model was well calibrated in the external validation series. Conclusions We present a novel, international, multicenter, predictive tool to assess the risk of additional axillary metastases after tumor-positive sentinel node biopsy in breast cancer. The predictive model performed well in internal and external validation but needs to be further studied in each center before application to clinical use.

Original languageEnglish
Pages (from-to)1888-1896
Number of pages9
JournalJournal of the National Cancer Institute
Volume104
Issue number24
DOIs
Publication statusPublished - Dec 19 2012

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Breast Neoplasms
Neoplasm Metastasis
Area Under Curve
Neoplasms
Lymph Node Excision
Logistic Models
Regression Analysis
Confidence Intervals
Biopsy
ROC Curve
cyhalothrin
Therapeutics

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

Cite this

International multicenter tool to predict the risk of nonsentinel node metastases in breast cancer. / Meretoja, Tuomo J.; Leidenius, Marjut H K; Heikkilä, Päivi S.; Boross, Gabor; Sejben, István; Regitnig, Peter; Luschin-Ebengreuth, Gero; Žgajnar, Janez; Perhavec, Andraz; Gazic, Barbara; Lazar, G.; Takács, Tibor; Vörös, Andras; Saidan, Zuhair A.; Nadeem, Rana M.; Castellano, Isabella; Sapino, Anna; Bianchi, Simonetta; Vezzosi, Vania; Barranger, Emmanuel; Lousquy, Ruben; Arisio, Riccardo; Foschini, Maria Pia; Imoto, Shigeru; Kamma, Hiroshi; Tvedskov, Tove F.; Kroman, Niels; Jensen, Maj Brit; Audisio, Riccardo A.; Cserni, G.

In: Journal of the National Cancer Institute, Vol. 104, No. 24, 19.12.2012, p. 1888-1896.

Research output: Contribution to journalArticle

Meretoja, TJ, Leidenius, MHK, Heikkilä, PS, Boross, G, Sejben, I, Regitnig, P, Luschin-Ebengreuth, G, Žgajnar, J, Perhavec, A, Gazic, B, Lazar, G, Takács, T, Vörös, A, Saidan, ZA, Nadeem, RM, Castellano, I, Sapino, A, Bianchi, S, Vezzosi, V, Barranger, E, Lousquy, R, Arisio, R, Foschini, MP, Imoto, S, Kamma, H, Tvedskov, TF, Kroman, N, Jensen, MB, Audisio, RA & Cserni, G 2012, 'International multicenter tool to predict the risk of nonsentinel node metastases in breast cancer', Journal of the National Cancer Institute, vol. 104, no. 24, pp. 1888-1896. https://doi.org/10.1093/jnci/djs455
Meretoja, Tuomo J. ; Leidenius, Marjut H K ; Heikkilä, Päivi S. ; Boross, Gabor ; Sejben, István ; Regitnig, Peter ; Luschin-Ebengreuth, Gero ; Žgajnar, Janez ; Perhavec, Andraz ; Gazic, Barbara ; Lazar, G. ; Takács, Tibor ; Vörös, Andras ; Saidan, Zuhair A. ; Nadeem, Rana M. ; Castellano, Isabella ; Sapino, Anna ; Bianchi, Simonetta ; Vezzosi, Vania ; Barranger, Emmanuel ; Lousquy, Ruben ; Arisio, Riccardo ; Foschini, Maria Pia ; Imoto, Shigeru ; Kamma, Hiroshi ; Tvedskov, Tove F. ; Kroman, Niels ; Jensen, Maj Brit ; Audisio, Riccardo A. ; Cserni, G. / International multicenter tool to predict the risk of nonsentinel node metastases in breast cancer. In: Journal of the National Cancer Institute. 2012 ; Vol. 104, No. 24. pp. 1888-1896.
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abstract = "Background Axillary treatment of breast cancer patients is undergoing a paradigm shift, as completion axillary lymph node dissections (ALNDs) are being questioned in the treatment of patients with tumor-positive sentinel nodes. This study aims to develop a novel multi-institutional predictive tool to calculate patient-specific risk of residual axillary disease after tumor-positive sentinel node biopsy. Methods Breast cancer patients with a tumor-positive sentinel node and a completion ALND from five European centers formed the original patient series (N = 1000). Statistically significant variables predicting nonsentinel node involvement were identified in logistic regression analysis. A multivariable predictive model was developed and validated by area under the receiver operating characteristics curve (AUC), first internally in 500 additional patients and then externally in 1068 patients from other centers. All statistical tests were two-sided. Results Nine tumor-and sentinel node-specific variables were identified as statistically significant factors predicting nonsentinel node involvement in logistic regression analysis. A resulting predictive model applied to the internal validation series resulted in an AUC of 0.714 (95{\%} confidence interval [CI] = 0.665 to 0.763). For the external validation series, the AUC was 0.719 (95{\%} CI = 0.689 to 0.750). The model was well calibrated in the external validation series. Conclusions We present a novel, international, multicenter, predictive tool to assess the risk of additional axillary metastases after tumor-positive sentinel node biopsy in breast cancer. The predictive model performed well in internal and external validation but needs to be further studied in each center before application to clinical use.",
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T1 - International multicenter tool to predict the risk of nonsentinel node metastases in breast cancer

AU - Meretoja, Tuomo J.

AU - Leidenius, Marjut H K

AU - Heikkilä, Päivi S.

AU - Boross, Gabor

AU - Sejben, István

AU - Regitnig, Peter

AU - Luschin-Ebengreuth, Gero

AU - Žgajnar, Janez

AU - Perhavec, Andraz

AU - Gazic, Barbara

AU - Lazar, G.

AU - Takács, Tibor

AU - Vörös, Andras

AU - Saidan, Zuhair A.

AU - Nadeem, Rana M.

AU - Castellano, Isabella

AU - Sapino, Anna

AU - Bianchi, Simonetta

AU - Vezzosi, Vania

AU - Barranger, Emmanuel

AU - Lousquy, Ruben

AU - Arisio, Riccardo

AU - Foschini, Maria Pia

AU - Imoto, Shigeru

AU - Kamma, Hiroshi

AU - Tvedskov, Tove F.

AU - Kroman, Niels

AU - Jensen, Maj Brit

AU - Audisio, Riccardo A.

AU - Cserni, G.

PY - 2012/12/19

Y1 - 2012/12/19

N2 - Background Axillary treatment of breast cancer patients is undergoing a paradigm shift, as completion axillary lymph node dissections (ALNDs) are being questioned in the treatment of patients with tumor-positive sentinel nodes. This study aims to develop a novel multi-institutional predictive tool to calculate patient-specific risk of residual axillary disease after tumor-positive sentinel node biopsy. Methods Breast cancer patients with a tumor-positive sentinel node and a completion ALND from five European centers formed the original patient series (N = 1000). Statistically significant variables predicting nonsentinel node involvement were identified in logistic regression analysis. A multivariable predictive model was developed and validated by area under the receiver operating characteristics curve (AUC), first internally in 500 additional patients and then externally in 1068 patients from other centers. All statistical tests were two-sided. Results Nine tumor-and sentinel node-specific variables were identified as statistically significant factors predicting nonsentinel node involvement in logistic regression analysis. A resulting predictive model applied to the internal validation series resulted in an AUC of 0.714 (95% confidence interval [CI] = 0.665 to 0.763). For the external validation series, the AUC was 0.719 (95% CI = 0.689 to 0.750). The model was well calibrated in the external validation series. Conclusions We present a novel, international, multicenter, predictive tool to assess the risk of additional axillary metastases after tumor-positive sentinel node biopsy in breast cancer. The predictive model performed well in internal and external validation but needs to be further studied in each center before application to clinical use.

AB - Background Axillary treatment of breast cancer patients is undergoing a paradigm shift, as completion axillary lymph node dissections (ALNDs) are being questioned in the treatment of patients with tumor-positive sentinel nodes. This study aims to develop a novel multi-institutional predictive tool to calculate patient-specific risk of residual axillary disease after tumor-positive sentinel node biopsy. Methods Breast cancer patients with a tumor-positive sentinel node and a completion ALND from five European centers formed the original patient series (N = 1000). Statistically significant variables predicting nonsentinel node involvement were identified in logistic regression analysis. A multivariable predictive model was developed and validated by area under the receiver operating characteristics curve (AUC), first internally in 500 additional patients and then externally in 1068 patients from other centers. All statistical tests were two-sided. Results Nine tumor-and sentinel node-specific variables were identified as statistically significant factors predicting nonsentinel node involvement in logistic regression analysis. A resulting predictive model applied to the internal validation series resulted in an AUC of 0.714 (95% confidence interval [CI] = 0.665 to 0.763). For the external validation series, the AUC was 0.719 (95% CI = 0.689 to 0.750). The model was well calibrated in the external validation series. Conclusions We present a novel, international, multicenter, predictive tool to assess the risk of additional axillary metastases after tumor-positive sentinel node biopsy in breast cancer. The predictive model performed well in internal and external validation but needs to be further studied in each center before application to clinical use.

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