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

Abstract

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.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-5227-3) contains supplementary material, which is available to authorized users.

Development and validation of a nomogram for prediction of lymph node metastasis in early-stage breast cancer

“…Lu et al confirmed that the lowest probability of node metastasis was in estrogen receptor (ER) − /HER2 − cancers (31). Similarly, in our study, triple negative tumors had the lowest probability of LNM, while HR − /HER2 + tumors had the highest probability, which was in agreement with the findings of previous study (26). Reyal et al hypothesized that the process of axillary LNM was mainly related to intrinsic biological characteristics in the ER − /HER2 − breast cancer subgroup, while tumor size, growth rate and lympho-vascular invasion were the main determinants in both the ER positive or the HER2 positive breast cancer subgroups (32).…”

“…We also found that the risk of LN involvement decreases with women's age from youngest to oldest. Younger patients (age <40 years old) were more likely to have LNM than other age groups, and same results were shown in other researches (14,26). Moreover, increasing tumor histologic grade was significantly associated with an increased risk of LNM.…”

Diagnostic Accuracy of Pre-Operative Breast Magnetic Resonance Imaging (MRI) In Predicting Axillary Lymph Node Metastasis: Variations In Intrinsic Subtypes, And Potential to Omit Sentinel Lymph Node Biopsy In Some Low Risk Groups of Patients With Invasive Breast Cancer.&nbsp;

“…For patients in the TNBC subgroup having small tumor burdens, the average number of FN ALNs was more then 2, which might be due to only 1 (5.9%) patient developing ALN metastasis. Similar to our study, Lu et al published that the lowest probability of node metastasis was for ER-/ HER2-tumors [39], and Houvenaeghel et al also reported TNBC had the lowest probability of node metastasis [40]. Owing to a high NPV and small possibility of ALN metastasis, ALN staging by MRI is still promising in the TNBC group.…”

Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study

“…On the basis of the state-of-art, most works propose non-sentinel lymph node status predictive models by using features of a different nature, including nomograms of clinical and pathologic variables [ 33 , 34 , 35 , 36 , 37 ]. On the other hand, the literature is poor in studies aimed at the development of sentinel lymph nodes predictive models [ 38 , 39 , 40 ].…”