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. 2019 Jan 10;19(1):45.
doi: 10.1186/s12885-018-5227-3.

Lymph node positivity in different early breast carcinoma phenotypes: a predictive model

Gilles Houvenaeghel  1   2 , Eric Lambaudie  3   4 , Jean-Marc Classe  5 , Chafika Mazouni  6 , Sylvia Giard  7 , Monique Cohen  3 , Christelle Faure  8 , Hélène Charitansky  9 , Roman Rouzier  10 , Emile Daraï  11 , Delphine Hudry  12 , Pierre Azuar  13 , Richard Villet  14 , Pierre Gimbergues  15 , Christine Tunon de Lara  16 , Marc Martino  3 , Jean Fraisse  12 , François Dravet  5 , Marie Pierre Chauvet  7 , Jean Marie Boher  3
Affiliations

Lymph node positivity in different early breast carcinoma phenotypes: a predictive model

Gilles Houvenaeghel et al. BMC Cancer. .

Abstract

Background: A strong correlation between breast cancer (BC) molecular subtypes and axillary status has been shown. It would be useful to predict the probability of lymph node (LN) positivity.

Objective: To develop the performance of multivariable models to predict LN metastases, including nomograms derived from logistic regression with clinical, pathologic variables provided by tumor surgical results or only by biopsy.

Methods: A retrospective cohort was randomly divided into two separate patient sets: a training set and a validation set. In the training set, we used multivariable logistic regression techniques to build different predictive nomograms for the risk of developing LN metastases. The discrimination ability and calibration accuracy of the resulting nomograms were evaluated on the training and validation set.

Results: Consecutive sample of 12,572 early BC patients with sentinel node biopsies and no neoadjuvant therapy. In our predictive macro metastases LN model, the areas under curve (AUC) values were 0.780 and 0.717 respectively for pathologic and pre-operative model, with a good calibration, and results with validation data set were similar: AUC respectively of 0.796 and 0.725. Among the list of candidate's regression variables, on the training set we identified age, tumor size, LVI, and molecular subtype as statistically significant factors for predicting the risk of LN metastases.

Conclusions: Several nomograms were reported to predict risk of SLN involvement and NSN involvement. We propose a new calculation model to assess this risk of positive LN with similar performance which could be useful to choose management strategies, to avoid axillary LN staging or to propose ALND for patients with high level probability of major axillary LN involvement but also to propose immediate breast reconstruction when post mastectomy radiotherapy is not required for patients without LN macro metastasis.

Keywords: Breast cancer; Molecular subtype; Nomogram; Risk prediction; Sentinel node.

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Conflict of interest statement

Ethics approval and consent to participate

This work was approved by our institutional review board (IPC - Comité d’Orientation Stratégique). All procedures performed in this study involving human participants were done in accordance with the French ethical standards and with the 2008 Helsinki declaration. All included patients provided written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Nomograms. 1a: Nomogram predictive of LN Involvement– Pathologic model. 1b: Nomogram predictive of LN macro metastases – Pathologic model. 1c: Nomogram predictive of LN Involvement– Clinical model. 1d: Nomogram predictive of LN macro metastases – Clinical model.
See this image and copyright information in PMC

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