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

  • Houvenaeghel, Gilles1, 2
  • Lambaudie, Eric1, 2
  • Classe, Jean-Marc3
  • Mazouni, Chafika4
  • Giard, Sylvia5
  • Cohen, Monique1
  • Faure, Christelle6
  • Charitansky, Hélène7
  • Rouzier, Roman8
  • Daraï, Emile9
  • Hudry, Delphine10
  • Azuar, Pierre11
  • Villet, Richard12
  • Gimbergues, Pierre13
  • Tunon de Lara, Christine14
  • Martino, Marc1
  • Fraisse, Jean10
  • Dravet, François3
  • Chauvet, Marie Pierre5
  • Boher, Jean Marie1
  • 1 Institut Paoli Calmettes et CRCM, 232 boulevard de Sainte Marguerite, Marseille, 13009, France , Marseille (France)
  • 2 Institut de Recherche pour le Développement, Aix-Marseille University, Unité Mixte de Recherche S912, Marseille, 13385, France , Marseille (France)
  • 3 Institut René Gauducheau, Site Hospitalier Nord, St Herblain, France , St Herblain (France)
  • 4 Institut Gustave Roussy, 114 rue Edouard Vaillant, Villejuif, France , Villejuif (France)
  • 5 Centre Oscar Lambret, 3 rue Frédéric Combenal, Lille, France , Lille (France)
  • 6 Centre Léon Bérard, 28 rue Laennec, Lyon, France , Lyon (France)
  • 7 Centre Claudius Regaud, 20-24 rue du Pont St Pierre, Toulouse, France , Toulouse (France)
  • 8 Centre René Huguenin, 35 rue Dailly, Saint Cloud, France , Saint Cloud (France)
  • 9 Hôpital Tenon, 4 rue de la Chine, Paris, France , Paris (France)
  • 10 Centre Georges François Leclerc, 1 rue du Professeur Marion, Dijon, France , Dijon (France)
  • 11 Hôpital de Grasse, Chemin de Clavary, Grasse, France , Grasse (France)
  • 12 Hôpital des Diaconnesses, 18 rue du Sergent Bauchat, Paris, France , Paris (France)
  • 13 Centre Jean Perrin, 58 rue Montalembert, Clermont Ferrand, France , Clermont Ferrand (France)
  • 14 Institut Bergonié, 229 Cours de l’Argonne, Bordeaux, France , Bordeaux (France)
Published Article
BMC Cancer
Springer (Biomed Central Ltd.)
Publication Date
Jan 10, 2019
DOI: 10.1186/s12885-018-5227-3
Springer Nature


BackgroundA 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.MethodsA 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.ResultsConsecutive 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.ConclusionsSeveral 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.

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